31st annual symposium on space science and technology 2015.pdf

Abstracts

ISRO-IISc Space Technology CellIndian Institute Science, Bangalore

8-9 January 2015

31st Annual Symposium on Space Science and Technology

31st Annual Symposium on Space Science and Technology Abstracts

1. A setup for the study of thermal properties of nanoscale devices of ultrathin layered materials Suman Sarkar, Kazi Rafsanjani Amin and Aveek bid

2.Low temperature giant magnetocaloric effect in magnetic ferroelectric GdMnO3 single crystals Aditya A. Wagh, P. S. Anil Kumar and Suja Elizabeth

3. Anomalous piezoelectric response in BaTiO3 based lead-free piezoceramics Ajay Kumar Kalyani, Kumar Brajesh and Rajeev Ranjan

4. Geospatial scenario based modelling of urban revolution in five major cities in India T.V. Ramachandra, Bharath H. A, Vinay S., Venugopal Rao K and. N V Joshi

5. Quantum communication and security using entangled states Ankur Raina, Shayan G. Srinivasa, Chithrabhanu P., Aadhi A., Shashi P. and R. P. Singh

6. Investigation of vegetation changes in the arid Trans-Himalayan ecosystem of northern India Sumanta Bagchi, Ekta Gupta, and Karthik Murthy

7. Study on self-assembly of donor-acceptor-donor molecular materials and melamine Nileshi Saraf, Joydeep Dhar and Satish Patil

8. High-resolution image restoration and enhancement using GPU Nithish Divakar and R. Venkatesh Babu

9.Turbulence-transport-chemistry interaction in statistically planar premixed flames in near isotropic turbulence A. Harshavardhana U, Swetaprovo Chaudhuri and K. N. Lakshmisha

10.Thermally activated unimolecular decomposition mechanisms of dimethylnitramine-aluminum (DMNA-Al) and dimethylnitramine-Zinc (DMNA-Zn) complex Anupam Bera and Atanu Bhattacharya


11. Investigation of spatial and temporal dynamics of vegetation in semi-arid-ecosystems Sumithra Sankaran, Sabiha Sachdeva, Ashwin Viswanathan and Vishwesha Guttal

12. Development of semiconductor nanocrystals for photovoltaics Biswajit Bhattacharyya, Rekha Mahadevu and Anshu Pandey

13. Die level 3D packaging of hybrid systems N. P. Vamsi Krishna and Prosenjit Sen

14. A small volume droplet dispenser based on electrohydrodynamic pulling K. R. Sreejith, and G. R. Jayanth

15. Synthesis optical and electrical characterization of II-VI colloidal quantum dots for IR applications Atul Prakash Abhale , Abhijit Chatterjee, Naresh Babu, Arup Banerjee and K.S.R. Koteswara Rao

16.Movement strategies for commensalism: coexistence of meso-carnivores in human-dominated landscapes Maria Thaker and AbiTamimVanak

17. Development and performance evaluation of a PCM coupled heat pipe G. M. Karthik, Girish Kini, A. Ambirajan and P. Kumar

18.Theoretical and computational study of frictional effects in viscoelastic and generalized elastic contacts D. Satish Kumar and Narayan K. Sundaram

19. Combustion studies of composite propellants containing nano- burn rate catalysts Charlie Oommen and R Arun Chandru

20. Electromagnetic field enhancement in sub-nm gaps Debadrita Paria and Ambarish Ghosh

21. Modelling primary atomization of liquid jets Santosh Hemchandra


22.Development of an optimization based image processing software system for Indian forest resource assessment using Radar Imaging Satellite (RISAT-1) Images S N Omkar and G. Narayana Naik

23. Numerical simulations of Darcy-Brinkman-Forccheimer equations for flow in porous media Karthick M and Gaurav Tomar

24. Double diffusive convection in the earth’s core Venkatesh Gopinath and Binod Sreenivasan

25.Utilizing ionic liquid and mixed solvent electrolytes to synthesize polymer electrolytes for lithium-based batteries Sudeshna Sen, Sneha Malunavar, C. Gouri and Aninda J. Bhattacharyya

26.Sparsity-based cross-terms-suppressed time-frequency distribution of multi-component linear frequency modulated signals Shreyas Hampali, Subhankar Ghose, and Chandra Sekhar Seelamantula

27. Investigating the origin of the Indian ocean Geoid low Attreyee Ghosh

28.Evolution of mesoproterozoic suture zones western India: Implications on India-Madagascar corre-lations C. Ishwar-Kumar and K. Sajeev

29.Estimation of soil hydraulic properties in a catchment using agro-hydrological models and micro-wave remote sensing K. Sreelash, M. Sekhar, S. K. Tomer, S. Bandyopadhyay and M.S. Mohan Kumar

30. Tribological properties of polytetrafluorethylene at cryogenic temperatures D.S. Nadig, V.K. Pavan and Paul P. George


31. Portable imaging flow analyzer for biological research applications in space Veerendra Kalyan Jagannadh and Sai Siva Gorthi

32.Processing of metallic thermal interface materials using liquid phase sintering followed by accumulative roll-bonding Deepak Sharma, Rajesh Kumar Tiwari, P. Ramesh Narayan and Praveen Kumar

33. Development of nanoparticle based radiation detectors for space applications B.H.M. Darukesha, V. Radhakrishna, M. Ravindra and K. Rajanna

34. Spatial coherence of tropical rain R. Ratan, V. Venugopal, J. Sukhatme and R. Murtugudde

35.Amorphous silicon carbide thinfilms by pulsed DC magnetron sputtering for micro electro-mechanical systems (MEMS) applications Habibuddin Shaik, M.A. Sumesh and G. Mohan Rao

36.Low temperature electrical transport studies on carbon nitride films prepared by chemical vapour deposition K. Ramesh, M. Prashantha, R.Venkatesh, N. Naresh and M.V.N. Prasad

37. Generation of BPSK/QPSK modulated microwave signals using optical techniques K R Yogesh Prasad, T Srinivas, Gopal Hegde and Abdul Hameed

38. Development and characterisation of nano-porous aluminium alloy surfaces Arti Yadav, Subrata Chakarbarti, A. Manimaran, M.S Bobji

A setup for the study of thermal properties of nanoscale devices of

ultrathin layered materials

Suman Sarkar, Kazi Rafsanjani Amin and Aveek bid

Department of Physics

Indian Institute of Science

Abstract: The aim of the proposed project

is to develop a research facility for

exploring thermal properties (Seebeck

effect, thermal conductivity etc.) of

nanoscale devices based on different forms

of layered semiconductor materials like

graphene and Bi2Se3/Bi2Te3. This will be

integrated to an electrical transport

characterization system to allow a

comprehensive study of transport and

thermodynamic properties in

nanostructured materials. In addition we

would like to measure fluctuations in

thermal conductivity to probe the effect of

defects on thermal conductivity of

technologically important materials. The

set up developed by us over the last few

months is capable of measuring voltage

fluctuations as low as 10-20 V2/Hz over a

wide band-width. We are currently in the

process of integrating the system with a low

temperature cryostat that will enable us to

carry out the measurements of thermal

conductivity over a temperature range 1.5-

300K. We have also done preliminary

measurements of single layer graphene

devices using this set-up.

I. INTRODUCTION In recent years layered semiconductor materials like

graphene and MoS2 have emerged as possible

candidates for post silicon technologies. Although

most present studies focus on their electrical properties,

their unique thermal properties make them very

interesting candidate for potential applications. It has

been proposed that the thermal conductivity of

graphene is highest ever measured in any material. For

graphene on silicon-dioxide substrate the thermal

conductivity is about 500W/mK, which is much

higher than that of bulk gold. For suspended graphene

devices the value of thermal conductivity can be as

high as 5000W/mK1, almost ten timed higher than that

of any metal. In addition to an exceptionally large

thermal conductivity, graphene is highly conductive

even in very low carrier density regimes2 and hence has

extremely low levels of Johnson-Nyquist thermal noise

as compared to semiconductor based sensors. It also

has fewer kinds of defects and hence has intrinsically

low levels of 1/f noise3 arising out of thermal switching

of defects. It is relatively easy to make four terminal

measurements on graphene strips making contact

resistances much easier to deal with than for example

in carbon nanotubes which share almost all other

advantages of graphene. On the mechanical side,

suspended graphene flakes have been shown to have a

very high Young’s modulus (~ 1TPa) and to have a

much higher elasticity than membrane materials like

silicon nitrate commonly used in NEMS4.

Both graphene and single-walled carbon nanotubes

exhibit excellent electrical conductivities and large

specific surface areas. Calculations show that a

covalently bonded graphene/single-walled carbon

nanotube hybrid material would extend those

properties to three dimensions. There have been several

proposals for exploiting the unique properties of

graphene-CNT hybrid for making body of aircraft.

Such a hybrid would have a reasonably good electrical

conductivity – an important criterion for spacecraft

safety. The large thermal conductivity of the matrix

could provide a strong, light weight and thermally

conducting outer shell for reentry vehicles. A study of

the thermoelectric figure of merit of the material would

give an indication of the applicability of these materials

as waste energy harvesters.

The measured thermal conductivity of graphene on

SiO2 substrate, although very high, is still lower than

the theoretical predicted value. In order to understand

the factors that limit the thermal conductivity of

substrated graphene below the theoretical limit

extensive measurements of the fluctuations (or noise)

in thermal conductivity of graphene need to be carried

out. To the best of our knowledge no such studies have

been carried out till date – either in pristine graphene

or in graphene based matrices.

.

II. METHODS

We have taken two different approaches to measuring

the thermal conductivity of layered semiconducting

31st Annual In-House Symposium on Space Science and TechnologyISRO-IISc Space Technology Cell, Indian Institute of Science, Bangalore, 8-9 January 2015

materials. In the first approach, we have optimized a

technique to fabricate microheaters that have extremely

large intrinsic thermal time constants. This technique is

essential for devices where the thermal conductivity is

less than that of the substrate (for example

Bi2Se3/Bi2Te3 on SiO2).This will enable us to detect

small changes in the thermal signature of single or few

layered materials. The thermal conductivity of layered

semiconductors vary over several orders of magnitude-

from about 1W/mK in the case of BiSe to about 5000

W/mK for suspended graphene devices. The

microheaters have to be optimized to handle this entire

dynamic range of thermal properties. Figure 1 shows

the SEM image of one such device.

Figure 1: Microheater for measuring thermal conductivity of

layered materials.

In the second approach we are measuring the thermal

conductivity of graphene and BiSe systems using the 3

Omega technique. This is useful for devices where the

conductivity of the sample is larger than that of the

substrate (for example - graphene on SiO2 substrate)

Data for a typical measurement on graphene single

layer is shown in Figure 2. The data is plotted as a

function of the number density of charge carriers

introduced in the system by a back gate. We note that

as the carrier density increases the electronic

contribution to thermal conductivity increases as is

expected from Wiedemann-Franz law.

1 A. Balandia et.al., Nano letts. 8, 902 (2008). 2 K. S. Novoselov et.al. Nature 438, 197 (2005). 3 P. Dutta et.al. Rev. Mod. Phys. 53, 497 (1981).

Figure 2: Electronic contribution to the thermal conductivity

of graphene

We are currently also studying the thermal

conductivity of MoS2 - a material which is interesting

for many practical applications. A typical image of the

device is shown in figure

Figure 3: Optical microscope image of multi-layer MoS2

device.

III. CONCLUSION

We are in the process of developing the necessary

techniques for measuring thermal properties of single

layered and few layered semiconducting materials. The

measurements are extremely challenging because in

most cases the thermal signal from the substrates can

be orders of magnitude larger than that from the sample

under test. We have done preliminary tests and are now

in the process of measuring thermal properties of

graphene, MoS2 and BiSe single and few layered

devices.

4 I. W. Frank et.al. J. Vac. Sci. Technol. B 25 2558

(2007

Project Number:ISTC/PPH/SE/325

Low Temperature Giant Magnetocaloric Effect in Magnetic Ferroelectric GdMnO3 Single Crystals

Aditya A. Wagh,P. S. Anil Kumar, and Suja Elizabeth

Department of Physics, Indian Institute of Science, Bangalore 560012, India

In a typical magnetic material,

magnetic sublattice is sensitive to external magnetic fields and, due to this coupling, the magnetic part of the entropy changes with variation of magnetic field. Magnetocaloric effect (MCE) is generally demonstrated by isothermal change in magnetic entropy or adiabatic change in temperature with change in magnetic field. At room temperature, GdMnO3 is paramagnetic and paraelectric.It exhibits complex magnetic phase diagram (H-T phase diagram).1, 2 At 42 K (TN(Mn)), paramagnetic phase transforms to sinusoidally-modulated collinear antiferromagnetic phase with modulation vector directed along the ‘b’ axis which is incommensurate in nature where no spontaneous electric polarization is observed.1,. The competing AFM and ferromagnetic exchanges in ab-plane cause magnetic frustration, which in turn is responsible for long wavelength modulated AFM spin structures at low temperature.3, 4 Near 23 K (Tlock), the system transforms to a CA-AFM phase with weak canting component of Mn3+ spins lying along ‘c’ axis.2 GdMnO3 is known to display AFM ordering of rare earth (Gd3+) sublattice when the temperature is lowered further.2

Single crystals of GdMnO3 were grown using optical float-zone method. A four-mirror image furnace (argon ambience; flow rate ~ 1 lit/min) was employed which yields crystals of typical length 36 mm at growth rate of ~ 5mm/h. A photograph of the grown crystal is shown in Fig.1.

Fig.1: Single crystal of GdMnO3 M-H isotherms were recorded along

the three crystallographic axes using zero-

field protocol as shown in Fig.2. Magnetization as a function of temperature data were extracted from measured M-H isotherms and used for the calculation of ΔSM.

Fig.2: (a) Typical set of zero field-cooled magnetization – field (M-H) isotherms recorded at short temperature intervals along (a) `c' axis, (b) `a' axis and (c) `b' axis. The temperature evolution of numerically calculated ΔSMfor the three axes is plotted in Fig.3. The magnitude of


ΔSMincreases prominently below Tlock transition and records the highest value in the proximity of 7 K. This is likely to occur due to enhanced polarized paramagnetic moment of Gd3+ from weak ferromagnetic component of the canted Mn3+ moment in this phase. Further decrease in the temperature leads to a sharp fall in ΔSMresulting in an asymmetric broad peak. An anomaly is observed in ΔSMin the form of a sharp peak at 5 K along `c' axis, but it is feeble in other axes. This feature becomes pronounced as the field is increased and is perceived to be due to spin reorientation of the rare earth sublattice. Above a critical field, along `b' axis, the ferroelectric phase (ab-cycloidal) stabilizes which then transforms to paraelectric phase with increase in temperature. This manifests in step-like decrease in the ΔSMvs T (shown by arrow in Fig.3.c. The field-induced

transformation (FOPT) to ferroelectric phase (i.e. from CA-AFM to ab-cycloidal phase) along 'b' axis is clearly discernible as a step-like feature in the plot of field variation of ΔSMFig3.d. The distinct step-like feature, observed along `b' axis, is absent along other axes.The calculated value ofΔSM is very large (~ 31.8 J/kg K) along 'c' axis for the field variation 0-80 kOe at 7 K. The anisotropy inΔSMVs T is not strong (At ~7 K, ΔSM = 29.1 and 25.7 J/kg K for 'a' and 'b' axes, respectively). From the phase diagram, 'c' and 'a' are 'easy' and 'hard' axes. However, at higher fields along 'b' axis the system exists in ferroelectric cycloid AFM phase. In the respective ab-cycloid spin structure, the 'b' axis could be considered the 'hard' axis. It is prudent to compare ΔSMvalues in various AFM phases.

Fig. 3:Temperature evolution of estimated ΔSM for field variation up to 80 kOe along different crystallographic axes: (a) `c'axis, (b) `a' axis and (c) `b' axis. (d) ) Field variation of ΔSM along `b' axis. The inset shows the magnified view of the low-field region. References [1] T. Kimura, et al.,Physical ReviewB 71, 224425 (2005). [2] J. Hemberger, et al., Physical Review B 70, 024414 (2004). [3] M. Mochizuki, N. Furukawa, Physical Review B 80, 134416 (2009). [4] J. Ribeiro, Ferroelectrics 368, 114 (2008).

1

Anomalous Piezoelectric Response in BaTiO3based Lead-free Piezoceramics

Ajay Kumar Kalyani, Kumar Brajesh, Rajeev Ranjan

Department of Materials Engineering, Indian Institute of Science, Bangalore-560012, Karnataka, India

Abstract

The effect of Zr, Hf and Sn substitutions in BaTiO3has been investigated at close composition intervals in the dilute concentration limit. Detailed structural analysis by x-ray and neutron powder diffraction revealed that merely 2 mol percent of Zr, Sn and Hf stabilizes a coexistence of orthorhombic (Amm2) and tetragonal (P4mm) phases at room temperature. As a consequence all the three systems show substantial enhancement in the longitudinal piezoelectric coefficient (d33), with Snmodification exhibiting the highest value ~425 pC/N.

I. INTRODUCTION

Research on lead free piezoelectric materials have received considerable momentum in the recent past because of serious environmental concerns with regard to the toxicity of the commercial piezoelectric, lead zirconate titanate (PZT), which contains ~60 weight percent of lead. Lead free compounds such as Na1/2Bi1/2TiO3 (NBT). (K,Na)NbO3 (KNN) and BaTiO3 (BT) have been extensively investigated in pure and modified form with renewed interest. Both BaTiO3 and KNbO3 exhibit the same sequence of phase transformations, namely cubic(C)

tetragonal (T) orthorhombic (O) rhombohedral (R) on cooling from high temperature [1]. Though the high Curie point of KN (Tc ~410 C) as compared to BT (Tc ~120 C) is attractive, the difficulties associated with the fabrication of dense ceramic bodies, among other issues, have not yielded encouraging piezoelectric properties in KN based systems. The best property has been reported for a textured Li and Sb modified (K,Na)NbO3 [2]. Very high electric field driven strain values have been reported for certain compositions in the quaternary (K,Na)NbO3-NBT-BT system [3]. BT based ceramics have received attention only in the recent few years after discovery of anomalous properties in the (Ba, Ca)(Ti, Zr)O3system [4]. The essential idea used by Ren et al is to bring about a triple point – morphotropic phase boundary (TMPB) state, i.e a coexistence of tetragonal, orthorhombic and cubic phasesclose to room temperature. Though this approach can drastically increase the piezoelectric property due to the system’s proximity to triple point criticality, the piezoelectric properties would drastically deteriorate by slight increase in temperature since one of the phase at the triple point is cubic phase and therefore non-ferroelectric in nature. From this standpoint, it is always desirable to have piezoelectrics with Curie point well above room temperature.

The dielectric and piezoelectric properties of modified and pure BaTiO3 ceramics have been documented by Jaffe and Jaffe [1]. Of particular interest are the compositional modifications which can induce inter-ferroelectric instability, and coexistence of ferroelectric phases at room temperature. The two inter-ferroelectric transitions, tetragonal-orthorhombic and orthorhombic-rhombohedral occur at ~ 0 C and -90 C in pure BaTiO3. A perusal of literature suggests thatZr [5], Sn [6]and Hf [7]substitution in BaTiO3increase both the inter-ferroelectric transition temperatures from below room temperature. This offers interesting opportunity to chemically tune the orthorhombic-tetragonal and rhombohedral-orthorhombic phase transition near to room temperature for enhanced piezoelectric properties mimicking a morphotropic phase boundary scenario.We have carried out detailed structural, ferroelectric and piezoelectric measurements on a series of compositionally modified (Zr, Sn and Hf) BaTiO3. It was found that 2 mole percent of the other two substituents,SnZr and Hf remarkably increase the piezoelectric property by stabilizing coexistence of ferroelectric tetragonal and orthorhombic phases at room temperature. A longitudinal direct piezoelectric coefficient of 425 pC/N was obtained for Sn modified BaTiO3.

II. EXPERIMENTAL

Solid solution of Ba(Ti1-xZrx)O3(BZT), Ba(Ti1-

xSnx)O3(BST), Ba(Ti1-xHfx)O3(BHT) were prepared by solid state route using high purity BaCO3, TiO2, ZrO2, SnO2, HfO2 compounds. Powders were milled in planetary ball mill (P5, Fritch) using acetone medium for 10-12 hrs. Milled powder were dried and calcined at temperature of 1100 C for 4hrs. Sintering was done by two step process first heated to 1300 for 4 hrs and then heated to 1500 C for 6hrs. X-ray powder diffraction was done using Bruker (D8, advance). Neutron powder-diffraction (NPD) experiment was carried out at the SPODI diffractometer at FRM II, Germany using a wavelength of 1.548 Å. Dielectric measurement were done on Novocontrol (Alpha AN) impedance analyzer. Measurement of longitudinal piezoelectric coefficient (d33) was carried using Piezotest PM 300 by poling the pellets at room temperature for ~ 1 hour at a field of ~ 2 kV/cm. Rietveld refinement was carried out using FULLPROF software[8].

Project No. ISTC/MET/RR/323


2

III. RESULTS AND DISCUSSION

Figure 1 shows thecomposition dependence of the longitudinal piezoelectric coefficient (d33) ofBZT, BST and BHTsystems. All the three systems exhibits substantial enhancement of the piezoelectric coefficient. In BST d33increases from 190 pC/N (for x=0) to ~270 pC/N at x = 0.01, reaching a maximum ~425 pC/N at x = 0.02. Then onwards the value exhibits saturation after a slight decrease.For BZT, fig 1(b), a similar enhancement in d33 is observed reaching a maximum of ~370 pC/N at x = 0.02. After a slight drop at x=0.03, d33is nearly constant in the range 0.03≤x≤0.06. For BHT (fig 1(c)) maximum d33 (~370 pC/N) was again obtained at x = 0.02.

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(b)

Mole % Sn

(a)

d 33 (p

C/N

)

Mole % Zr

Figure 1. Composition dependence of longitudinal piezoelectric coefficient (d33) in BaTi1-y XyO3. (a) X = Sn, (b) X = Zr and (c) X =Hf.

Fig 2 shows the temperature variation of relative permittivity of BZT, BHT and BST. For 2 mol % substitution all the three systems exhibit sharp dielectric anomaly corresponding to the tetragonal-cubic transition at Tc ~120 C. Interestingly the tetragonal-orthorhombic dielectric anomaly TT-O is shifted from 0 C to just above room temperature ~35 C for BZT (x=0.02) and BHT (x=0.02). This anomaly was not captured above room temperature in BST (x=0.02), which exhibits the highest d33 among the three systems but was clearly revealed for higher Snconcentration (4 mol %).

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Zr = 0.02 Hf = 0.02(a)

Figure 2 Temperature dependence of dielectric constant of BaTi1-yXyO3 (X = Zr, Hf, Sn). The insets show the magnified plot near room temperature

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Figure 3: Phase diagram of BaTi1-yXyO3 (X = Sn, Zr, Hf) constructed from the anomaly temperatures in dielectric studies.

Based on the dielectric anomaly temperatures measured for different compositions of the three systems under investigation, a phase diagram has been plotted in Fig. 3. The three substitutions Zr, Hf and Sn raise the orthorhombic-tetragonal (TO-T) and the rhombohedral-orthorhombic (TR-O) transition temperatures and decrease the tetragonal-cubic transition (Tc) temperature. The phase

3

45 46 65.8 66.5 83.5 84.0

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Figure 4. X-ray powder Bragg profiles of pseudo cubic 200c, 220c and 222c reflections of BaTi1-xXyO3 The arrows indicates the reflections from the respective phases ( O = orthorhombic reflection)

diagrams based on dielectric measurements suggest that the minimum concentrations of the substituents which can help stabilize the orthorhombic phase at room temperature is x=0.02 for BZT and BHT and x=0.04 for BST. Both BZT and BHT exhibits rhombohedral-orthorhombic dielectric anomaly above room temperature in the composition range 0.08<x≤0.011. This anomaly could not be captured in the Sn modified BaTiO3. From the trend of the three phase boundaries in the phase diagrams of BZT and BHTthe possibility of the merging of the boundaries can be anticipated somewhere in the composition range 0.11<x<0.15. Though this scenario may suggest the possibility of coexistence of four phases-cubic, tetragonal, orthorhombic and rhombohedral, as has been reported in a recent work by Yaoet al [9],this is not feasible from thermodynamic view point since for a (pseudo)binary systemGibb’s phase rule does not allow more than three phases to coexist in equilibrium at any given pressure.

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Fig 5 Rietveld fitted neutron powder diffraction patterns of (a) BaTi0.98Zr0.02O3 and (b) BaTi0.96Sn0.04O3 with tetragonal (P4mm) + orthorhombic (Amm2) phase coexistence model. The insets show magnified plot of two pseudocubic reflections 400c and 240c

Sinceas per the dielectric study the highest d33 in BST seems to occur in the tetragonal phase whereas in BZT and BHT in the orthorhombic phase, a unified structure-property correlation for all the three systems was not possible to establish. A detailed structural characterization was therefore carried out. Figure 4(a-g) shows the x-ray powder Bragg profiles of three pseudocubic reflections 200C, 220C and 222C for different compositions of the three systems.For sake of direct comparison, the corresponding Bragg profiles of unmodified BaTiO3 are also given (figure 4a).Interestingly, in contrast to the dielectric study which suggests a tetragonal phase for BST x=0.02, the XRD pattern of this composition revealed additional features (pointed with arrow) apart from the tetragonal peaks. Further, though the phase diagram suggests orthorhombic phase for BST with x=0.04 at room temperature, tetragonal peaks are also present along with the orthorhombic peaks for this composition. The same phase coexistence was found in both BZT and BHT with x=0.02. Thus irrespective of whether the tetragonal-orthorhombic criticality as revealed by the dielectric anomaly is above or below room temperature, the structural analysis clearly proves that all the three systems exhibit coexistence orthorhombic and tetragonal phases at room temperature for the composition (x=0.02). Fig. 5 shows Rietveld fitted neutron diffraction patterns of BZT (x=0.02) and BST (x=0.04) with tetragonal (P4mm) + orthorhombic (Amm2) phase coexistence model.The insetsshow magnified plot of fits of two high angle pseudocubic reflections 400c and 204c. For both the cases, if the structure were single

4

phase (either tetragonal or orthorhombic)the each of the pseudocubic 400Cand 204c profiles wouldbe expected to split into two peaks. The presence of the extra reflectionsand their satisfactory modeling confirms the chosen phase coexistence model. The refined structural parametersare given in tables 1 and 2.

The observation of enhanced piezoelectric response in a system comprising of two ferroelectric phases is analogous to PZT which exhibits anomalous piezoelectric response at the morphotropic phase boundary (MPB) compositions exhibiting coexistence tetragonal + rhombohedral/monoclinic ferroelectric phases. The anisotropic lowering of the energy barrier separating two different ferroelectric phases with different orientation of

the polarization vector near a temperature/composition/pressuredriven critical point provides low energy polarization rotation pathway [10]. Similar to BaTiO3, KNN also exhibits three polymorphic phase transitions (Tc ~410 oC, TT-O ~200 oC, and TO-R ~ -123 oC). In this system also, the enhanced electromechanical response is observed by tailoring the TT-

O, Li-substitution at the (K0.5Na0.5)site in KNN has been reported to lower the TT-Otransition temperature close to room temperature, and stabilizes the two ferroelectric (P4mm+Amm2) phases [11]. Similarly ‘Sb’ substitution at the Nb-site in KNN raises the TO-Rtransition temperature.

Table1: Refined structural parameters and agreement factors for Ba(Ti0.98Zr0.02)O3 using Tetragonal(P4mm) + orthorhombic(Amm2) phase coexistence models.

Space group: P4mm Space group: Amm2

Atoms x y z B(Å 2) x y z B(Å2)

Ba 0.000 0.000 0.000 0.46(5) 0.000 0.000 0.000 0.1(1)

Ti/Zr 0.500 0.5000 0.470(1) 0.3(1) 0.500 0.000 0.526(1) 0.31(9)

O1 0.500 0.5000 0.029(1) 0.31(6) 0.000 0.000 0.5114(9) 0.62(5)

O2 0.500 0.000 0.523(1) 0.37(1) 0.500 0.2491(4) 0.2464(5) 0.61(4)

a=3.99782(7) Å, c=4.03493(9) Å

v= 64.488(2) Å3, %Phase = 33(1)

a= 3.99597(5) Å, b=5.69071(8) Å,

c=5.67698(8) v=129.094(3) Å3, %Phase = 67(1)

Rp: 6.80, Rwp: 5.06, Rexp: 3.05, Chi2: 2.75

Table2: Refined structural parameters and agreement factors for Ba(Ti0.96Sn0.04)O3 using Tetragonal(P4mm) + orthorhombic(Amm2) phase coexistence models.

Space group: P4mm Space group: Amm2

Atoms x y z B(Å 2) x y z B(Å2)

Ba 0.000 0.000 0.000 0.5 (1) 0.000 0.000 0.000 0.07(9)

Ti/Sn 0.500 0.5000 0.471(2) 0.2(2) 0.500 0.000 0.495(2) 1.3(1)

O1 0.500 0.5000 0.024(1) 0.1(1) 0.000 0.000 0.498(2) 0.6(1)

O2 0.500 0.000 0.510(2) 0.5(1) 0.500 0.2577(8) 0.2371(5) 0.83(6)

a=4.00183(8) Å, c=4.02807(12) Å

v= 64.508(3) Å3, %Phase = 39(2)

a= 3.99980(8) Å, b=5.6857(1) Å,

c=5.6754(1) v=129.096(5) Å3, %Phase = 61(2)

Rp: 8.90, Rwp: 5.94, Rexp: 4.16, Chi2: 2.04

5

Thus the mechanism associated with the enhanced piezo-response in BaTiO3 and KNN based systems is primarily based on the similarity of the topology of their respective phase diagrams. While two different types of chemical substitutions are required to stabilize two different combinations of ferroelectric phasesat room temperature in KNN,in BaTiO3 the same substituent (either Zr, or Hf or Sn) does the job by varying the concentration. The first order nature of the P4mm-Amm2 and R3m-Amm2 transitions in these systems ensures their coexistence around the critical point. The temperature range of this coexistence islikely to be increased due to random elastic strain induced in the lattice by substitution of Zr/Hf/Sn at the Ti site. This explains why BST still exhibits the same phase coexistence as BZT and BHT for x=0.02 inspite of the difference in their respective critical temperatures.

IV. CONCLUSIONS

In conclusion, we have shown that merely 1-3 mole percent of Zr, Sn and Hf substitution in

BaTiO3considerably enhances the piezoelectric response of the system with Sn modification exhibiting the highest piezoelectric response of 425 pC/N. A detailed structural analysis by x-ray and neutron powder diffraction revealed that these dilute modifications stabilize a coexistence of ferroelectric phases, orthorhombic and tetragonal,at room temperature. It is this phase coexistence that is responsible for the anomalous piezoelectric response exhibited by these systems. Our results suggests the possibilities that there might be other elements as well which can enhance the piezoelectric properties of BaTiO3. More work needs to be carried out to explore this possibility.

Acknowledgement

RR thanks the ISRO-IISc Space Technology Cell for financial assistance.

References [1] B. Jaffe, Piezoelectric Ceramics (Elsevier Science,

(2012). [2] Y. Saito, H. Takao, T. Tani, T. Nonoyama, K. Takatori, T. Homma, T. Nagaya, M. Nakamura, Nature, 432, 84 (2004). [3] W. Jo, T. Granzow, E. Aulbach, J. Rödel, and D.

Damjanovic, J. Appl. Phys. 105, 094102 (2009). [4] W. Liu and X. Ren, Phys. Rev. Lett. 103, 257602(2009)

[5] G. H. Jonker, and W. Kwestroo, J. Am. Ceram. Soc. 41, 390 (1958). [6] G. A. Smolenskii, V. A. Isupov, Dokl. Akad. Nauk USSR, 96, 53 (1954).

[7] E. G. Fesenko, O. I. Prokopalo, Soviet Phys. Cryst. 6, 373 (1961)

[8] J. R. Carvajal, FULLPROF, A Rietveld Refinement and Pattern Matching Analysis Program, Laboratoire Leon Brillouin (CEA-CNRS), France.

[9] Y. Yao, C. Zhou, D. Lv, D. Wang, H. Wu, Y. Yang, and X. Ren, Europhys. Lett, 98, 27008 (2012).

[10] D. Damjanovic, Appl. Phys. Lett. 97, 062906 (2010). [11] Y. Dai, X. Zhang, G. Zhou, Appl. Phys. Lett, 90,

262903 (2007

[12] R. Zuo, J. Fu, D. Lv, and Y. Liu, J. Am. Ceram. Soc. 93, 2783 (2010).

1 ISTC/BES/TVR/313

Geospatial scenario based modelling of urban revolution in five major cities in India T.V. Ramachandra, Bharath H. A, Vinay S., Venugopal Rao K and. Joshi N V

Abstract - Urbanisation being irreversible and very rapid with fast growth of population and settlements during the last century, needs to be monitored and visualised for evolving strategies towards sustainable development approaches. This study visualises the growth of major Tier I cities of India such as Delhi, Mumbai, Pune, Chennai and Coimbatore, India through AHP Fuzzy based Cellular Automata Markov model. CA Markov model is considered to be one of most effective algorithm to visualise the growth of urban spatial structures. The analysis performed in previous studies such as spatial pattern of land use change in the area was considered and the future growth was modelled considering agents of growth applying fuzzy AHP CA-Markov model. The projection as predicted using the model was validated to obtain better validation and was then used to predict future projections. Modelling suggested a clear trend of various land use classes’ transformation in the area of urban built up expansions.

I. INTRODUCTION

Rapid urbanization is one of the most important factor affecting the local ecology and loss of biodiversity in India during the last two decades (Shivaramakrishnan et al., 2005; MOUD., India, 2011; Ramachandra et al., 2012; Bharath H. A., 2012; Ramachandra et al., 2014a). Urbanisation is a form of growth with implications of economic, social, and political forces and to the physical geography of an area (Sudhira et al., 2007; Ramachandra et al., 2014a). The sprawl takes place at the urban fringes resulted in radial development of the urban areas or development along the highways results in the elongated development of urban forms (Sudhira et al., 2003).

Dr. T V Ramachandra, Coordinator of Energy and Wetlands

Research Group (EWRG), Centre for Ecological Sciences (CES),

Indian Institute of Science (IISc), Bangalore.

[email protected]

Bharath H Aithal, is a Junior Research Fellow at Indian Institute

of Science (IISc). [email protected]

Vinay S, is a research scholar at Energy and Wetlands Research

Group (EWRG), Centre for Ecological Sciences (CES), Indian

Institute of Science (IISc). [email protected]

Joshi N V, Faculty at Centre for Ecological Sciences (CES), Indian

Institute of Science (IISc), Bangalore

Venugopal Rao K, is Group Head, Urban Studies &

Geoinformatics, National Remote Sensing Centre (NRSC), Indian

Space Research Organisation, Hyderabad, India.

[email protected]

It can also be defined as a finite cycle through which nations evolve to form industrially dominant regions, which further results in rural push and spreading of city towards outskirts (Ramachandra et al., 2013) also refers to urban sprawl.

This urban development in the fringes is called sprawl. The study on urban sprawl was attempted by various researchers across the globe (Batty et al., 1999; Torrens, 2000; Sudhira et al., 2004; Huang et.al 2007; Bhatta, 2009a, 2009b, 2010; Ramachandra et al., 2012). These sprawl areas do not have a fixed plan or process of development due to which the process of preparing visionary documents such as developmental plans, specific corridors of developments are being ineffective considering the fact that spatial patterns and dynamic behaviour of growth and also may be attributed to lack of skills and tools to help in informed, accurate decision making (Adhvaryu, 2011; Bharath H.A., et al., 2014). This can be improved and timely decisions may be enabled using technological improvements such as remote sensing and tools such as Geographic Information system (GIS).

Remote Sensing data acquired through space borne remote sensors enables a bird eye view of the landscape at low cost (Lillesand and Kiefer, 2005). The advantage of remote sensing data is to acquire repeated measurements of the same area on periodic basis which helps in detection and monitoring of LULCC and surveillance of problematic sites (Campbell, 2002). The analysis of changes at local, regional and global scales is possible through the collection of remote sensed data covering the larger spatial extent. Remote sensing aids in identification and assessment of land use patterns which is important for environmental management and decision making. Further it is essential to visualise and provide better planning strategies for future urban growth. This can be planned and visualised using various modelling techniques.

Traditional large-scale urban simulation approaches of early 90’s were based on theories, and suffered from significant weaknesses such as poor handling of space-time dynamics and too much generalisation of data. The integration of space, time, and attributes in modelling was further enhanced with the implementation of Cellular automata (CA) models (Allen 1997; Batty 1999; EPA 2000; Alberti and Waddell 2000). CA modelling is capable of addressing the spatial complexity with discrete time change. A


2 ISTC/BES/TVR/313

number of CA-based models of urban growth have produced satisfactory simulations of spatial urban expansion over time (Clarke et al., 1997; Leao et al. 2004; Bharath and Ramachandra, 2013; Ramachandra et al., 2013; Arsanjani et al., 2013).The main advantages of CA are simplicity, easy integration with raster GIS, and adaptability to various urban growth situations. CA models can realistically generate and represent complex patterns through the use of simple rules and considering its neighbouring properties since these models operate on basis of cell states, size, neighbourhood and transition rules (White and Engelen 2000). This communication presents the Land use change modeller (Bharath H.A. et al., 2013) and Fuzzy AHP based CA (Bharath H.A et al., 2014) models implemented to visualise the urban growth in five Tier I cities in India.

II. DATA AND METHOD

Temporal remote sensing data of Landsat TM and ETM+ downloaded from GLCF were preprocessed to correct geometrical and radiometrical accuracy USGS (http://www.usgs.gov). This was further used to analyse and model LULC changes. Remote sensing data were supplemented with the Survey of India topographic maps (of 1:50000 and 1:250000 scale), which were used to generate base layers of the administrative boundary, drainage network, Road network etc. Slope map was extracted using ASTER data (30 m) downloaded from USGS (www.usgs.gov). Ground control points (GCPs) and training data were collected using pre calibrated Global Positioning System (GPS) and virtual online spatial maps such as Bhuvan and Google Earth. GCPs were useful in geometric correction of remote sensing data. Census data (1991, 2001 and 2011) was used to capture population dynamics. Modelling of urbanization and sprawl involved: i) Remote Sensing data acquisition, geometric correction, field data collection, ii) Classification of remote sensing data and accuracy assessment using GRASS, iii) Land use analysis, iv) Identification of agents and development of attribute information, v) Prediction: Designing scenarios of urban growth and calibrating the model to find out the best weights based on the influence on the neighborhood pixels vi) Accuracy assessment and validation of the model, vii) Prediction of future growth based on validated data. Land use analysis was carried out using supervised pattern classifier - Gaussian maximum likelihood algorithm based on probability and cost functions (Duda et al., 2000). Land use with gradient analysis results were further used in Modelling. These results can be accessed in previous working literatures

(Ramachandra et al., 2014a, 2014b, 2014c, 2014d, Chandan et al., 2014). These data was used in Modelling and visualizing the growth of these cities.

III. MODELLING USING FUZZY AHP-CA

Using the combination of Fuzzy Logic, Analytical Hierarchical Process (AHP), Multi Criteria Evaluation (MCE), Markov chains and Cellular Automata (CA). Agents of urbanisation such as roads, industries, educational institutions, bus stands, railway stations, metro, population, etc. were normalized. Conservation regions as per city development plan (CDP) water bodies were considered as constraints. The fuzzy based analysis is used to normalize the contributing factors between 0 and 255, where 255 showing the maximum probability of change and 0 indicating no change, for different land uses. The normalized agents were taken as input to AHP to determine the weights of driving factors using pair wise comparisons ith weights as Eigen vectors. The weights analysed and calibrated through AHP is verified using measured consistency ratio (CR). CR below 0.1, the model is consistent and used for subsequent processes.

These weights along with the factors of growth are combined along with the constraints to obtain site suitability maps for different land uses using equation below

LC =

∗ ∑ ∗

……. (1)

Where LC is the linear combination of weights, n is the number of factors, D decision factor, W is the weight of the factor.

The Markov chains are used to determine the change probability between two historical datasets to derive the growth in the future scenarios based on different criteria’s. The Markovian transition matrix indicates the probability of the particular land use being converted to other land uses on single time step.

Criteria Factors and Constraints Without

CDP as a

constraint

Slope, Distance from roads, Distance to

industries, Distance to Bus stops and Railway

stations, Distance from metro, Distance from

educational institutions, Population Density

With CDP Slope, Distance from roads, Distance to

industries, Distance to Bus stops and Railway

stations, Distance from metro, Distance from

educational institutions, City Development

Plan, Population Density

Table 1: Criteria’s for simulating and predicting urban sprawl

The cellular automata based on the site suitability and the transition matrix is used to spatially predict the changes in land use based on current land use at every

3 ISTC/BES/TVR/313

single time step, based on the neighbouring pixels. Two scenarios were designed to predict the land use changes as shown in table 1.

Validation of the simulated datasets of were performed with classified datasets through kappa indices, as a measure of agreement. Once these data and agents are trained and validated, data is used to model and simulate for the year 2030 (ten years) with definite time steps.

IV. RESULTS

Geo-visualisation of urbanisation of five tier I cities are depicted in fig.1 to fig.5 and results are provided in tables 2 to 6 respectively. The cities on an average would grow by 1.5 to over 2 times the current state in next decade. By 2025, it is predicted that built up area in these cities and surroundings, grows over 57% (Delhi), 27% (Mumbai), 45.8% (Chennai), 50% (Pune) and 37% (Coimbatore) respectively. The various drivers of growth for different cities are as in annexure 1. In all these cases, spatially it could be understood that the CDP if implemented properly would play a major role in curtailing the unsustainable growth of the city in its limits, while some growth still takes place at the outskirts. Prime factors of growth include the transportation network, industrialisation, and educational sector.

Modelling growth of Delhi

Predicted 2017 Predicted 2024

Predicted 2031 Fig.1: Predicted landscape dynamics of Delhi

Year Built up Vegetation Water Others

2017 45.80 17.98 1.25 34.97

2024 57.37 8.77 1.18 32.68

2031 70.86 3.76 1.19 24.18 All units as percentage area

Table 2: Predicted landscape dynamics of Delhi

Modelling growth of Mumbai


Fig.2: Predicted landscape dynamics of Mumbai


2020 25.83 9.09 44.52 20.56


Table 3: Predicted landscape dynamics of Mumbai

Modelling growth of Chennai

Predicted 2026

Fig.3: Predicted landscape dynamics of Chennai



Table 4: Predicted landscape dynamics of Chennai

4 ISTC/BES/TVR/313

Modelling growth of Pune



Fig.4: Predicted landscape dynamics of Pune

Year Built up Water Vegetation Others

2016 37.78 1.75 16.37 44.11

2019 41.64 1.75 20.16 36.45

2022 47.89 1.75 20.16 30.20


Table 5: Predicted landscape dynamics of Pune

Modelling growth of Coimbatore


Fig.5: Predicted landscape dynamics of Coimbatore

Year Built up Water Vegetation Others

2023 32.64 0.29 17.14 49.94


Table 6: Predicted landscape dynamics of Coimbatore

V. CONCLUSION

This study demonstrates the application of temporal remote sensing data and Geoinformatics in mapping and understanding of urban dynamics. Advance geo-visualisation of urban growth would aid in decision making towards sustainable cities with basic infrastructure and amenities. Identification of regional factors that are most likely to influence a land-use changes has improved the accuracy of prediction. The predictions of land use/cover changes through CA-Markov model suggest a continual increase in urban settlements with a decline in local ecology and natural vegetation covers. The prediction using CA help to design sustainable urban transportation system.

VI. ACKNOWLEDGEMENT

We are grateful to ISRO-IISc Space Technology Cell, Indian Institute of Science and NRDMS division, Ministry of Science and Technology, DST, Government of India for the financial support. We thank USGS Earth Resources Observation and Science (EROS) Center for providing the environmental layers and Global Land Cover Facility (GLCF) for providing Landsat data.

VII. Publications

1. Ramachandra, T. V., Bharath, H. A. and Sowmyashree, M.V., 2014. Urban structure in Kolkata: metrics and modelling through geo-informatics, Appl Geomat 6(22): DOI 10.1007/s12518-014-0135-y

2. Ramachandra, T. V., Bharath, H. A. and Sowmyashree, M.V., 2014. Monitoring spatial dynamics of urbanisation in Ahmed city, Textile hub of India, SPATIUM International review, 31(2):85-91

3. Ramachandra, T. V., Bharath S., Bharath, H. A. 2014. Spatio-temporal dynamics along the terrain gradient of diverse landscape, Journal of Environmental Engineering and Landscape Management, 22(1): 50–63 doi:10.3846/16486897.2014.808639.

4. Ramachandra, T.V., Bharath H. Aithal and Sowmyashree M V., 2014. Monitoring urbanization and its implications in a mega city from space: Spatiotemporal patterns and its indicators, Journal of Environmental Management http://dx.doi.org/10.1016/j.jenvman.2014.02.015

5. Ramachandra, T. V., Bharath, H. A. and Barik, B., 2014. Urbanisation Pattern of Incipient Mega Region in India, TEMA - Journal of Land Use, Mobility and Environment, 7(1): 83-100.

6. Ramachandra, T. V., Bharath, H. A. and Sowmyashree, M.V., 2014. Urban footprint of Mumbai - the commercial capital of India, Journal of Urban and Regional Analysis, VI (1): p. 71 – 94

7. Ramachandra, T. V., Bharath, H. A., 2014. Geo-visualisation of Urbanisation in Greater Bangalore, India Geospatial Digest, September 2014, pp 12-18 http://www.geospatialworld.net/Paper/Application/ArticleView.aspx?aid=31130#sthash.NZScwjHH.dpuf

5 ISTC/BES/TVR/313

8. Ramachandra, T. V., Bharath, H. A. and Sowmyashree, M.V., 2013. Analysis of spatial patterns of urbanisation using geo-informatics and spatial metrics, Theoretical and Empirical Research in Urban Management, 8(4):5-24

9. Ramachandra, T.V., Bharath, H. A., and Vinay S, Land Use Land Cover Dynamics in a Rapidly Urbanising Landscape, SCIT Journal, Volume XIII, August 2013, XIII(1):1-13

10. Bharath, S., Rajan, K.S., Ramachandra, T.V., 2013. Land Surface Temperature Responses to Land Use Land Cover Dynamics. Journal of Geoinformatics and Geostatistics: An Overview 1(4):1-10. doi:10.4172/2327-4581.1000112.

11. Ramachandra, T.V., Meera, D.S., and Alakananda B., 2013. Influence of Catchment Land Cover Dynamics on the Physical, Chemical and Biological Integrity of Wetlands, Environment & We -International Journal of Science & Technology - (EWIJST), pp 8(1): 37-54

12. Ramachandra T. V., Sreejith K. and Bharath H. A., 2014. Sector-Wise Assessment of Carbon Footprint across Major Cities in India, Assessment of Carbon Footprint in Different Industrial Sectors, Volume 2, Muthu S. S. (ed.), Eco-Production, DOI: 10.1007/978-981-4585-75-0_8, Springer Science Business Media Singapore, 2014.

13. Ramachandra, T. V., Shwetmala, K., Dania, T.M., 2014. Carbon footprint of solid waste sector in Greater Bangalore, India, In: Assessment of Carbon Footprint in Different Industrial Sectors (ed. S.S. Muthu), Volume 1, Eco-Production, DOI: 10.1007/978-981-4560-41-2_11, Springer Science, Singapore Pp 265-292

VIII. References 1. Campbell, J. B., 2002. Introduction to Remote

Sensing (3rd Edn). Taylor & Francis, London, UK, 605.

2. Lillesand, T. M., Kiefer, R. W., 2002. Remote Sensing and Image Interpretation, 4th ed. John Wiley and Sons, 215–216.

3. Ramachandra, T.V., Bharath, H.A., Barik, B., 2014c. Urbanisation Pattern of Incipient Mega Region in India, Tema. Journal of Land Use, Mobility and Environment, 7(1), 83-100.

4. Ramachandra, T.V., Bharath, H. A., Sowmyashree, M. V., 2014d. Urban Footprint of Mumbai - The Commercial Capital of India, Journal of Urban and Regional Analysis, Vol. 6(1), 71-94.

5. Ramachandra, T.V., Bharath H. Aithal and Durgappa D. Sanna, 2012. Insights to Urban Dynamics through Landscape Spatial Pattern Analysis., International Journal of Applied Earth Observation and Geoinformation, Vol. 18, Pp. 329-343

6. Ramachandra, T.V., Bharath, H.A., Sowmyashree M. V., 2014a. Monitoring urbanization and its implications in a mega city from space: Spatiotemporal patterns and its indicators, Journal of Environmental Management, accepted, in press, doi:10.1016/j.jenvman.2014.02.015

7. Ramachandra, T.V., Bharath, H.A., Sowmyashree M. V., 2014b. Urban Structure in Kolkata: Metrics and

Modeling through Geo-informatics, Applied Geomatics, accepted, in press.

8. Chandan, M.C., Bharath, H.A., Ramachandra, T.V., 2014. Quantifying urbanisation using geospatial data and spatial metrics-a case study of madras, In proceedings of Lake 2014: Conference on Conservation and Sustainable Management of Wetland Ecosystems in Western Ghats, 13th -15th Nov., Uttara Kannada, India

9. Bharath, H. A., Vinay, S., Ramachandra, T.V., 2014. Landscape dynamics modelling through integrated Markov, Fuzzy-AHP and Cellular Automata, in the proceeding of International Geoscience and Remote Sensing Symposium (IEEE IGARSS 2014), July 13th – July 19th 2014, Quebec City convention centre, Quebec, Canada. Available at http://www.igarss2014.org/Papers/ViewPapers_MS.asp?PaperNum=1010

10. Bharath, H.A., Bharath, S., Sannadurgappa, D., Ramachandra, T. V., 2012, Effectiveness of landscape Spatial Metrics with reference to the Spatial Resolutions of Remote Sensing Data, Proceedings of India Conference on Geo-spatial Technologies & Applications 2012, IIT Bombay, Mumbai, India, 12-14 April, 2012.

11. Bharath, H.A., Vinay, S., Ramachandra, T.V., 2013. Prediction of Land use dynamics in the rapidly urbanising landscape using land change modeller In proceedings of Fourth International Joint Conference on Advances in Engineering and Technology, AET 2013, December 13-14, NCR Delhi, India.

12. MOUD. 2011.Urban development management for the formulation of the twelfth five year plan (2012–2017): Report of the working group on capacity building for the twelfth plan. New Delhi

13. Sivaramakrishnan, K.C., Kundu, A., Singh, B.N. 2005. Handbook of Urbanization in India, Oxford University Press, New Delhi, India.

14. Ramachandra, T.V., Bharath, H. A., Vinay, S., Joshi, N. V., Kumar, U., Venugopal, R. K., 2013. Modelling Urban Revolution in Greater Bangalore, India , 30th Annual In-House Symposium on Space Science and Technology, ISRO-IISc Space Technology Cell, Indian Institute of Science, Bangalore, 7-8 November 2013.

15. Sudhira H. S., Ramachandra T. V., Subrahmanya, M. H., Bala, 2007. Urban Sprawl Management: Need for an Integrated Spatial Planning Support System, Technical Report: 119, Centre for Ecological science, Indian Institute of science, Bangalore.

16. Sudhira, H.S., Ramachandra, T.V., Raj, K.S., Jagadish, K.S., 2003. Urban Growth Analysis using Spatial and Temporal Data, Photonirvachak, Journal of Indian Society of Remote Sensing, 31(4), 299-311.

17. Batty, M., Xie, Y., Sun, Z., 1999. Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23(3), 205–233

18. Torrens, P. M., 2000. How cellular models of urban systems work, Centre for Advanced Spatial Analysis, University College London, Working Paper Series, Paper 28.

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19. Bhatta, B., 2009a. Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India. International Journal of Remote Sensing, 30(18):4733–4746.

20. Bhatta, B., 2009b. Modeling of urban growth boundary using geoinformatics. International Journal of Digital Earth, 2(4):359–381.

21. Bhatta, B., 2010. Analysis of urban growth and sprawl from remote sensing data. Berlin, Heidelberg: Springer-Verlag.

22. Sudhira, H.S., Ramachandra, T.V. and Jagadish, K.S., 2004. Urban sprawl: metrics, dynamics and modelling using GIS”, International Journal of Applied Earth Observation and Geoinformation, 5(1):29-39.

23. Allen, J., Lu, K., 2003. Modeling and prediction of future urban growth in the Charleston region of South Carolina: A GIS-based integrated approach. Ecology and Society, 8(2), 2

24. EPA, 2000. Projecting land-use change: A summary of models for assessing the effects of community growth and change on land-use patterns. EPA/600/R-00/098, Office of Research and Development, Washington DC, USA.

25. Alberti, M., Waddell, P., 2000. An integrated urban development and ecological simulation model. Integr. Assess. 1(3), 213-227.

26. Arsanjani, J. J., Helbich, M., Kainz, W., Darvishi Boloorani, A., 2013. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int. J. of Appl Earth Observation and Geo., 21, 265–275.

27. Clarke, K. C., Hoppen, S., Gaydos, L., 1997. A self-modifying cellular automation model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24(2), 247–261

28. Leao, S., Bishop, I., and Evans, D., 2004. Simulating urban growth in a developing nation’s region using a cellular automata-based model. J. Urban Plann. Dev., 3(145), 145–158.

29. White, R., Engelen, G., 2000. High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Comput. Environ. Urban Syst., 24(5), 383–400.

30. Adhvaryu, B., 2011. The Ahmedabad urban development plan-making process: A critical review. Planning Practice and Research, 26(2), 229–250

Annexure

Road DEM CPD

Annexure 1: Factors and constraints of growth for Delhi

Road DEM CPD

Annexure 2: Factors and constraints of growth for Mumbai

7 ISTC/BES/TVR/313

Road and railway DEM CPD

Annexure 3: Factors and constraints of growth for Chennai

Road DEM CPD

Annexure 4: Factors and constraints of growth for Pune

Road DEM CPD Annexure 5: Factors and constraints of growth for Coimbatore

Quantum communication and security usingentangled states

Ankur Raina and Shayan G. SrinivasaDept. of Electronics Systems Engineering

Indian Institute of Science, Bengaluru, IndiaEmail correspondence: [email protected]

Chithrabhanu P., Aadhi A., Shashi P., R. P. SinghDivision of Quantum Optics

Physical Research Labs, Ahmedabad, IndiaEmail correspondence: [email protected]

Abstract—This research report has two main parts relatedto the theoretical and experimental investigations into quantumcommunications and security. In the first part, we consider superdense coding over the noisy bit flip channel with a shared Bell pairand show that the encoding scheme is perfectly secure using theinformation-theoretic criterion. We also extend the super densecoding idea to the tripartite system. In the second part, using athree particle hyper entangled state, we set up an experimentalplatform for illustrating the idea of multiparty quantum keydistribution protocol using photons. Our results are useful forrealizing the fundamental limits of transmission and informationsecurity over quantum channels with an eye towards experimentalrealization of the same via photonics.

I. INTRODUCTION

Super dense coding [1], a well known quantum protocolwas one of the first few pioneering discoveries in quantuminformation processing, that exploits the advantage of quantumentanglement. Ever since the discovery, quantum entanglementis considered as a resource in quantum information processing.It lets users pre-sharing an entangled pair of qubits, commu-nicate two classical bits. The protocol as given by Bennettand Wiesner is as follows - An entangled pair of qubits, alsocalled the Bell pair, is prepared and distributed to two nodes.One of the nodes performs unitary encoding and transfersits qubit to the other node via noiseless quantum channel.The receiving node performs measurement and decodes theinformation conveyed. However, there is one caveat. Thequantum channel transferring the qubit from the source to thedestination is assumed to be noiseless. In practice, no channel,classical or quantum is physically noiseless.

Quantum communication using entanglement is increas-ingly receiving attention and is an actively pursued topic ofcurrent research. In this work, we consider the super densecoding over the noisy bit flip channel. The quantum advantageof using entanglement is brought out. We also extend the ideaof super dense coding to a tripartite system initially in the GHZstate. We assume that the GHZ state is somehow prepared anddistributed to three parties namely A, B and C. We address thequestion whether 3 classical bits can be communicated usingsuch a tripartite state.

With entanglement between two quantum bits, protocolshave been demonstrated for teleporting an unknown quan-tum state [2], super dense coding of information [1] and

This is an ongoing collaboration between IISc and PRL funded byISTC/EED/SGS/311. The first part of the work is from IISc and the secondpart is from PRL.

secure communication [3]. We describe a hyper-entangled stateformed by orbital angular momentum (OAM) and polarizationdegree of freedom and use it for multiparty quantum key dis-tribution (QKD) protocol. The scheme is being experimentallyinvestigated via photonics.

The report is organized as follows. In sections 2 and 3,we describe super dense coding over the bit flip channel forbipartitite and tripartite states respectively. We also presentinformation security results over noisy quantum channels. Insection 4, we describe the construction of hyper entangledstates. We describe the experimental set up for state preparationin section 5 followed by the description of the QKD protocolin section 6 and conclusions.

II. SUPER DENSE CODING OF BELL STATES THROUGH ABIT FLIP CHANNEL

We present the protocol for communication between nodesA and B through a quantum bit flip channel [4]. The action ofthe quantum bit flip channel with parameter p on any state Φis given by [5]

N (Φ) = pσXΦσX + (1− p)Φ. (1)

We assume that a fully entangled bipartite state namely theBell pair φ+ is shared between nodes A and B.To encode classical information, A encodes its qubit usingPauli matrices σi, i ∈ 0, 1, 2, 3 [5]. A chooses I, σ1, σ2, σ3to respectively communicate two bits 00, 01, 10, and 11.Based on what A chooses, we get the following Bell pairsshared between A and B.

φ± =00± 11√

2, ψ± =

01± 10√2

.

The encoding operation by A is described using densitymatrices as follows:

ρi = (σi ⊗ I) ρ (σi ⊗ I)† , (2)

where ρ = φ+φ+, the initially shared Bell pair.

A. Calculation of Holevo capacity for a bit flip channel

We know from [6], that capacity of a classical channelis obtained by maximizing the mutual information betweencommunicating nodes, over all input distributions. When itcomes to the quantum scenario, the situation is very interesing.There exist notions of classical capacity and quantum capacity.Morever, there is an additional resource which can be used,namely entanglement. In this way four types of capacities have


been defined [7]. Holevo [8], Hausladen et al. [9], gave theexpression for classical capacity of a quantum channel usingquantum states. We are interested in the classical capacity of abit flip channel with entanglement shared between nodes. Wehave what is known as the HSW theorem, which states theclassical capacity, also called the Holevo capacity of a quantumchannel N , using the ensemble pi, ρi at the encoder, is givenby,

χ(pi, ρi) = S

((N ⊗ I)

(∑i

piρi))

−∑i

piS

((N ⊗ I)(ρi)

), (3)

where S denotes the Von Neumann entropy. It can be shownthat the uniform distribution also maximizes the Holevo capac-ity for the noisy quantum bit flip channel. The Holevo capacityevaluates to the following expression:

χ = 2− h(p). (4)

Observing (4), we find it is very akin to the capacity of a clas-sical binary symmetric channel with a cross over probabilityof p. When the channel is most random (p = 0.5), we get acapacity of zero for the classical channel but a capacity of 1 bitfor the quantum channel. There seems to be a clear advantagein using entangled quantum states for communication!

B. Connections to perfect security

Classically, Shannon [10] defined informationally secureencryption for message M and encrypted message E as

I(M ;E) = H(E)−H(E|M) = 0. (5)

That is, message should be statistically independent of theencrypted message for the encryption to be perfectly se-cure. The necessary condition for perfect secrecy implies thatH(K) ≥ H(M), where K represents the random variable forthe key. Boykin and Roychowdhury [11] provide conditionsfor perfect secrecy of qubits. In that work, condition for secureencryption of n qubits is formalized. The protocol is secureif for every message state ρ, the encrypted state, ρc is themaximally mixed state, i.e.,∑

k

pk Uk ρU†k =

I2n2n

. (6)

Note that the encoding by A gives

3∑i=0

pi(σi ⊗ I)ρ(σi ⊗ I)† =3∑i=0

piρi =I44.

Thus, this scheme leads to a perfectly secure encryption [11].It can be shown that the encrypted state does not lose itsmixedness even after it goes through a noisy bit flip channel.Infact, it is the unitality of the quantum channels that keepsthe perfect secrecy intact.

III. SUPER DENSE CODING USING TRIPARTITE STATES

We now explore tripartite system with prior entanglement.This is a natural extension from the bipartite case and helpsus take a peep into the world of higher dimensional Hilbertspaces. Considerable amount of research has been done onthe GHZ state [12], [13] and its preparation [14], [15]. Goingforward, one can think of having a network N nodes withprior entanglement [16] among more than two nodes. With thisinsight in mind, various communication scenarios like pointto point communication, multicast communication, broadcastcommunication gain prominence in the quantum world. Thelinks between various nodes in a physical setting would benoisy and information carrying capacities of such links wouldbe of immediate interest. The GHZ state is defined as

ψABC =000 + 111√

2.

A. Protocol for the noiseless case

N1

N2

A

B C

Fig. 1. Super dense coding over tripartite state.

Referring to Figure 1, A and B together encode theirqubits and send them over noiseless quantum channels N1 andN2 respectively. Upon receiving the two qubits, C performsmeasurement in an appropriate basis. We show that it ispossible to send 3 classical bits by joint encoding by A andB. A chooses one of the two unitary operations namely I, σ1and B chooses one of the four unitary operations namelyI, σ1, σ2, σ3 to encode their respective qubits. Without lossof generality, let A encode first followed by B. The encodingcan be represented mathematically by

ρ(A)i = (σi ⊗ I ⊗ I) ρABC (σi ⊗ I ⊗ I)†. (7)

ρi = ρ(B)i = (I ⊗ σi ⊗ I)ρ

(A)i (I ⊗ σi ⊗ I)†. (8)

Note that (7) represents the joint state after encoding byA and (8) represents the joint state after encoding by B.Depending upon the pair of encodings by A and B, thejoint state ends up being one of the eight orthonormal states.Hence, measurement in this basis gives one of the aboveeight possible outcomes. This brings us to summarizing ourprotocol as:

If A wants to communicate 1 bit and B wants to communicate2 bits then,1. A chooses either I or σ1 to respectively transmit 0 or 1.2. B chooses among I ,σ1, σ2, σ3 to respectively transmit00, 01, 10, 11.3. Both A and B send their qubits to C, which performsmeasurement in the appropriate basis.4. Based on the measurement outcome, C unambiguouslyrecovers three bits of classical data.5. The roles of A and B can be interchanged if A wants tocommunicate 2 bits and B wants to communicate 1 bit.

IV. HYPER ENTANGLED QUANTUM STATES

We consider a hyper-entangled state formed by OAMand polarization degree of freedom and use it for multipartyQKD protocol. In this protocol, one person can distribute twoindependent quantum keys with two different parties at thesame time.

Consider a system of particles in such a way that oneparticle is entangled to all other particles in different degreesof freedom (DOF). Let us consider a system consisting of threephotons as described in Fig. 2. Here, photon 2 is entangled withphotons 1 and 3 in different degrees of freedom namely OAMand polarization respectively. The polarization state of photon1 and the OAM state of photon 3 are arbitrary or unknown.

1 2 3

Entangled in OAM Entangled in Polarization

Unknown Polarization Unknown OAM

Fig. 2. The physical description of the defined quantum state.

Since OAM of a photon can have infinite values, one canhave higher dimensional entangled states. We take an arbitrarytwo dimensional subspace of the infinite dimensional OAMbasis as |l〉, |l′〉.

The described state can be prepared using a pair ofHadamard and CNOT gates in different DOFs which corre-spond to polarization and OAM. The initial states of three

1

2

3

123

Polarization

OAM

Fig. 3. Schematic diagram for preparation of the initial state.

particles can be written as

|1〉 = |ξp〉1|l〉1 ; |2〉 = |H〉2|l〉2 ; |3〉 = |H〉3|χo〉3 (9)

|ξp〉 is the unknown state of polarization of photon 1, while|χo〉 is the unknown OAM state of photon 3. |H〉 representshorizontal and |V 〉 represents vertical polarization states of aphoton. The schematic diagram is given in Fig. 3. To entanglephotons 1 and 2 in OAM, a Hadamard gate (Ho) on photon1 and subsequent CNOT gate on photon 1 and 2 are applied.Both gates act in OAM degree of freedom. Similarly, anotherHadamard (Hp) acting on photon 2 and a CNOT betweenphotons 2 and 3 entangle them in polarization. Here bothgates are acting on the polarization states of photons. Afterperforming the gate operations mentioned above, the final statebecomes,

|Ψ〉123 = |ξp〉1 ⊗ (|l, l′〉12 + |l′, l〉12)⊗ (|HV 〉23 + |V H〉23)|χo〉3.(10)

This state corresponds to a description given in Fig 2.

V. EXPERIMENTAL SCHEME FOR STATE PREPARATION

We are proposing a method in which initially two photonsare entangled in OAM and another photon is in an independentpure state of OAM and polarization. By polarization gateoperations in one of the entangled photons and the independentphoton, one can arrive at the described state. Experimentalscheme for generation of the state is given in Fig. 4.

BBO

SM

Pol

C NotHWP

1 V

! " Hoe # $%

123&

1

3

2

Fig. 4. Schematic experimental set up for the state preparation. SM- Simon-Mukunda gadget, HWP- half wave plate, BBO- second order nonlinear crystal(Beta Barium Borate) .

To generate the described state, we start with a Type Ispontaneous parametric down conversion of light in a secondorder nonlinear crystal that results a pair of photons entangledin OAM [17]. A vertically polarized optical vortex beam ofazimuthal index 1 has been considered as pump. The statecorresponding to this pair of photons is given by

|Ψ〉12 =+∞∑

m=−∞cm|m〉1|1−m〉2 ⊗ |H〉1|H〉2 (11)

For the ease of experimental realization, we reduce theinfinite dimensional entangled state to a simple two-qubitentangled state by grouping all even and odd OAM states andrewrite the expression for the OAM state in Eq. 11 as

+∞∑m=−∞

cm(|m〉1|1−m〉2) =+∞∑

k=−∞

c2k(|2k〉1|1− 2k〉2) +

+∞∑k=−∞

c2k+1(|2k + 1〉1| − 2k〉2).

(12)

We define a transformation from the general OAM spaceto the even-odd OAM space as

g(|u〉1|v〉2) = f(|u〉1)f(|v〉2), where

f(|x〉) =

|E〉, for even x;

|O〉, for odd x.

Note that g is nonlinear, but f is linear. Applying g on theleft-hand-side of Eq. 12 yields 1√

2(|E〉1|O〉2 + |O〉1|E〉2).

From the conservation of OAM, we have∑+∞k=−∞(c2k)2 =∑+∞

k=−∞(c2k+1)2 = 12

∑+∞m=−∞(cm)2 = 1

2 . Thus, we canrewrite Eq. 11 as

|Ψ〉12 =1√2

(|E〉1|O〉2 + |O〉1|E〉2)⊗ |H〉1|H〉2. (13)

Note that the photons are entangled in even/odd OAMstates. Let photon 1 pass through a Simon-Mukunda polarizinggadget which can convert its polarization to any arbitrary state[18], [19]. Photon 2 pass through a half wave plate at π8 whichperforms a Hadamard operation. Thus polarization state ofparticle 1 is encoded as the unknown state a|H〉1 + b|V 〉1and state of photon 2 is encoded as 1√

2(|H〉2 + |V 〉2). Action

of HWP on photon 2 is a Hadamard operation. Thus, the statebecomes

|Ψ〉12 =1√2

(|E〉1|O〉2 + |O〉1|E〉2) (a|H〉1 + b|V 〉1)⊗1√2

(|H〉2 + |V 〉2). (14)

Now, consider photon 3 with unknown superposition state inOAM and with definite polarization. Its state can be expressedas,

|Ψ〉3 = (α|q〉3 + β|1− q〉3)⊗ |V 〉3 (15)

where q is an integer. A polarization CNOT gate [20] is appliedon photon 2 (control) and 3 (target). This operation leads topolarization entanglement between photon 2 and 3. Thus, thethree particle state will become

|Ψ〉123 =1

2(a|H〉1 + b|V 〉1) (|E〉1|O〉2 + |O〉1|E〉2)

(|HV 〉23 + |V H〉23)(α|q〉3 + β|1− q〉3)(16)

This is the proposed three particle entangled state given in Eq.10

VI. QUANTUM KEY DISTRIBUTION

Quantum key distribution is essential for secure communi-cation to share a confidential message between two indentedparties, namely Alice and Bob. A QKD protocol can beimplemented by a single photon source or entangled photonsource [21], [22]. Entangled photons with one to one photoncorrelations are better source for QKD than a single photonsource. This strong correlation helps to share the key and getthe information about eavesdropping [23]. QKD is perfectlysecure, subject to all kinds of attacking strategies [24]. Thereare various protocols for QKD with the utilization of entangledphotons in different degrees of freedom such as polarization,OAM, and time-bin [25], [26], [27], [28]. Entanglement basedprotocols have been studied and implemented since long be-cause of its interesting properties such as strong correlation andnon-locality. Multipartite entanglement increases the codingcapacity and security of the key. It also provides multipartycommunication through the same channel [29].

Here we are proposing a multiparty QKD protocol usingthe entangled system described in section IV. We take a similarstate as in Eq. 10 given by

|Ψ〉123 = |ξp〉1 ⊗ (|00〉12 + |1− 1〉12 + | − 11〉12)⊗(|HH〉23 + |V V 〉23)|χo〉3, (17)

where |0〉, |1〉 and | − 1〉 are OAM states of photons.

Here we take a three dimensional subspace of infinitedimensional OAM entangled state produced by SPDC processwith Gaussian beam as pump instead of even/odd entangledstate. This state can be used for multiparty QKD. The advan-tage of this state is that it can generate two sets of independentkeys, one for Alice-Bob and another for Alice-Charlie. Inordinary three particle QKD, all the three parties will share thesame key and one can not get the key without the help of other[30]. So if Alice wants to pass some information to Bob, notto Charlie, this protocol is no longer useful. We have resolvedthis problem with the use of a quantum state mentioned in Eq.17.

Source+1

-1

0

D1

D2

D3D10

D11 BobCharly

Hologram

D9

D8

D4 D5 D6

+1

+1-1 0

0

-1 D7

Alice

Polarizing

Beam splitter

OAM sorter

Detector

13

2

HWP

Fig. 5. Schematic diagram for quantum key distribution between Alice-Bob and Alice-Charlie. Alice- Bob has Polarization entanglement and Alice-Charlie has OAM entanglement. Di - detectors, HWP - half wave plate, PBS- polarizing beam splitter.

The experimental implementation of the protocol is shownin Fig. 5. Bob, Alice and Charlie will receive the particles1, 2 and 3 respectively. Alice’s photon is entangled withBob’s photon in polarization and it is entangled with Charlie’sphoton in OAM. Bob’s and Charlie’s photons do not have anycorrelation between them. Alice will measure both OAM andPolarization states, Bob will measure polarization and Charliewill measure OAM of their received photons.

Alice and Bob share their keys by polarization measure-ments. They randomly measure polarization state of theirphotons in the following set of basis (αi = 0, 22.5, 45,67.5), (βi = 22.5, 45, 67.5, 180) respectively. Thesemeasurements can be done by a half wave plate and polarizingbeam splitter. The pairs of angles used by Alice and Bob forwhich the sum is 0 and 180 will give perfect correlationbetween them. The data corresponding to these correlatedphotons have same bits and can be used for the key. Alice

and Bob will compare their polarization measurement basisfor key distribution as well as for security of the key. Aftersufficient number of measurements 4/16 of the data are usefulfor key, two sets of 4/16 of the data are used to check CHSHinequalities (S and S′) and the remaining 4/16 are discarded.

Alice and Charlie share their keys by OAM correlationof entangled photons. Here we follow the key sharing schemeused in Ref [31]. In this scheme Alice and Charlie measure theOAM state of their photons in three different randomly chosenbases A1, A2, A3 and C1, C2, C3 respectively. This is doneby a pair of shifted holograms and an OAM sorter. The set ofbases (A3 and C3) has perfect correlation and the coincidencecorresponding to it can be used to generate the key. Alice andCharlie will compare their hologram settings for QKD andsecurity check. In total there are 9 possible measurements.After enough number of measurements, 1/9 of the produceddata can be used for the key. For Bell-type inequality check,4/9 of the data will be used which confirms the security of thekey and the remaining 4/9 of the data are useless.

If Ekert protocol [3] is used in a pairwise fashion, thenaround 8n pairs of photons (in practice, little more than 8n) arerequired to establish a pair of secret keys of length n betweenAlice and Bob and between Alice and Charlie. In our approach,around 6n pairs of photons would be required for the samepurpose. Hence, in terms of number of photons required, ourscheme is 33% more efficient than Ekert protocol.

On the other hand, if the same number of photons are usedin Ekert and our protocol and if the target key length is alsothe same, then because of more entanglement resource, ourprotocol would have more redundancy and hence can toleratemore noise. It is easy to see that the security in each degreeof freedom is equivalent to that of the Ekert protocol.

The security of the protocol is mainly checked by theviolation of Bell’s inequality test. All the three parties shouldcheck Bell like inequality with their data in order to checkeavesdropping. If there is violation of inequality the entan-glement is preserved and there is no eavesdropping in thechannel. The CHSH parameters S and S′ for photons entangledin polarization DOF are given by [32]

S = E(α1, β1)− E(α1, β3) + E(α3, β1) + E(α3, β3) (18a)

S′ = E(α2, β2) +E(α2, β4) +E(α4, β2)−E(α4, β4) (18b)

with

E(α, β) =R12(α, β) +R1′2′(α, β)−R12′(α, β)−R1′2(α, β)

R12(α, β) +R1′2′(α, β) +R12′(α, β) +R1′2(α, β)(19)

where R12, R1′2′ , R12′ and R1′2 are the coincidencesP (D11, D9 + D8 + D7), P (D10, D4 + D5 +D6), P (D11, D4 +D5 +D6) and P (D10, D9 +D8 +D7)respectively.

For any local realistic theory, the CHSH parameters S, S′ ≤2. Non-local nature of the entanglement will violate any ofthese inequalities and this violation can be used for checkingthe security of the key shared by Alice and Bob. The combi-nation of α and β for Bell’s inequality test and for the keydistribution is given in Table I.

Alice α1 α2 α3 α4

Bobβ1 S Key S ×β2 × S′ Key S′

β3 S × S Keyβ4 Key S′ × S′

TABLE I. DESCRIPTION OF DATA USAGE CORRESPONDING TOALICE’S AND BOB’S MEASUREMENT ANGLES. HERE S AND S′ ARE USED

FOR SECURITY CHECK THROUGH CHSH INEQUALITY AND × IS THEDISCARDED DATA.

Particles of Alice and Charlie have OAM correlation, so itwill violate the following Bell’s inequality for 3 dimensionalcase [31], [33]

S =P (A1 = C1) + P (A2 = C1 − 1) + P (A2 = C2) + P (A1 = C2)

− P (A1 = C1 − 1)− P (A2 = C1) + P (A2 = C2 − 1)+

P (A1 = C2 + 1) ≤ 2, where (20)

P (Aa = Cb+k) =2∑j=0

P (Aa = j, Cb = (j+k)Mod 3). (21)

The shifts of holograms are chosen in such a way thatthey maximally violate the Bell-type inequality. j=0,1,2 corre-sponds to the detection of OAM states 0, 1 and -1 respectively.The coincidence measurements with the combinations of P(D1,D6+D9), P(D1, D5+D8), P(D2, D4+D7), P(D2, D6+D9),P(D2, D5+D8), P(D2, D4+D7), P(D3, D6+D9), P(D3, D5+D8)and P(D3, D4+D7) are required for the key distribution and tocheck the security of the key using Eq. 20 and 21.

VII. CONCLUSION AND FUTURE WORK

We evaluated the Holevo capacity of the bit flip channelexisting between two entanglement sharing nodes. We alsopoint out that the encoding scheme ensures perfect secrecyunaffected by the noisy bit flip channel. We extended theideas of super dense coding to three nodes with a briefpractical motivation for the generalization. Theoretical under-standing into the limits of transmission capacity and securityare important for quantum communication channels. From anexperimental angle, we looked into the possibility of a newentangled state and its application in multiparty QKD. Withthe new QKD protocol, a common sender can share differentand independent keys with many parties. Our eventual goal isto unify the theoretical investigations within an experimentalframework via novel quantum coding schemes we are buildingto demonstrate the feasibility of a quantum communicationsystem.

REFERENCES

[1] C. Bennett and S. J. Wiesner, “Communication via one- and two-particleoperators on Einstein-Podolsky-Rosen states,” Phys. Rev. Lett. vol. 69,pp. 2881-2884, 1992.

[2] C. H. Bennett, G. Brassard, C. Crepeau, R. Jozsa, A. Peres, and W. K.Wootters. Teleporting an unknown quantum state via dual classical andeinstein-podolsky-rosen channels. Phys. Rev. Lett., 70:1895, 1993.

[3] A. K. Ekert. Quantum cryptography based on bell’s theorem. Phys. Rev.Lett., 67:661–663, 1991.

[4] A. Raina and S. G. Srinivasa, “Quantum communication over bit flipchannels using entangled bipartite and tripartite states,” IEEE. Allerton.Conf. Control. Computers and Comm. , Oct. 2014.

[5] M. A. Nielsen and I. L. Chuang, Quantum computation and QuantumInformation, Cambridge University Press, 2010.

[6] T. M. Cover and J. A. Thomas, Elements of Information Theory, Wiley,New York, 2006

[7] C. H. Bennett, P. W. Shor, J. A. Smolin and A. V. Thapliyal, “Entanglement-Assisted Capacity of a Quantum Channel and the ReverseShannon Theorem,” IEEE Trans. Inf. Theory, vol. 48, pp. 2637-2655,2002.

[8] A. S. Holevo, “The capacity of the quantum channel with general signalstates,” IEEE Trans. Info. Theory., vol. 44, pp. 269-273, 1998.

[9] P. Hausladen, R. Jozsa, B. Schumacher, M. D. Westmoreland and W. K.Wooters, “Classical information capacity of a quantum channel,” Phys.Rev. A, vol. 54, p. 1869, 1996.

[10] C. E. Shannon, “Communication theory of secrecy systems,” Bell Syst.Tech. J., vol. 28, pp. 656-715, 1949.

[11] P. O. Boykin and V. Roychowdhury, “Optimal encryption of quantumbits,” Physical Review A, vol. 67, p. 042317, 2003.

[12] J. Pan and A. Zeilinger, “Greenberger-Horne-Zeilinger-state analyzer,”Phys. Rev. A, vol. 57, pp. 2208-2211, 1998.

[13] C. Eltschka and J. Siewert, “Entanglement of Three-Qubit Greenberger-Horne-Zeilinger–Symmetric States,” Phys. Rev. Lett., vol. 108, p. 020502,2012.

[14] D. Bouwmeester, J. Pan, M. Daniell, H. Weinfurter and A. Zeilinger,“Observation of Three-Photon Greenberger-Horne-Zeilinger Entangle-ment,” Phys. Rev. Lett., vol. 82, pp. 1345-1349, 1999.

[15] L. Xin, L. Qing-Hong, F. Guang-Yu, W. Yue-Yuan and L. Shu-Tian,“Resonant interaction scheme for GHZ state preparation and quantumphase gate with superconducting qubits in a cavity,” Chinese Phys. B,vol. 23, p. 020311, 2014.

[16] Y. Xia, J. Song, H. Song, “Remote preparation of the N-particle GHZstate using quantum statistics,” Optics Communications, vol. 277, pp.219-222, 2007.

[17] S. Walborn, A. de Oliveira, R. Thebaldi, and C. Monken. Entanglementand conservation of orbital angular momentum in spontaneous parametricdown-conversion. Phys. Rev. A, 69:023811, 2004.

[18] R. Simon and N. Mukunda. Minimal three-component su (2) gadgetfor polarization optics. Phys. Lett. A, 143:165–169, 1990.

[19] Salla Gangi Reddy, Shashi Prabhakar, A. Aadhi, Ashok Kumar, MeghShah, R. P. Singh, and R. Simon. Measuring the mueller matrix of anarbitrary optical element with a universal su(2) polarization gadget. J.Opt. Soc. Am. A, 31(3):610–615, Mar 2014.

[20] X. H. Bao, T. Y. Chen, Q. Zhang, J. Yang, H. Zhang, T. Yang, and J. W.Pan. Optical nondestructive controlled-not gate without using entangledphotons. Phys. Rev. Lett., 98:170502, 2007.

[21] A. Beveratos, R. Brouri, T. Gacoin, A. Villing, J. P. Poizat, andP. Grangier. Single photon quantum cryptography. Phys. Rev. Lett.,89:187901, 2002.

[22] T. Jennewein, C. Simon, G. Weihs, H. Weinfurter, and A. Zeilinger.Quantum cryptography with entangled photons. Phys. Rev. Lett., 84:4729,2000.

[23] D. Bouwmeester, A. K. Ekert, and A. Zeilinger. The physics of quantuminformation, volume 38. Springer Berlin, 2000.

[24] E. Waks, A. Zeevi, and Y. Yamamoto. Security of quantum keydistribution with entangled photons against individual attacks. Phys. Rev.A, 65:052310, 2002.

[25] R. Ursin, F. Tiefenbacher, T. Schmitt-Manderbach, H. Weier, T. Scheidl,M. Lindenthal, B. Blauensteiner, T. Jennewein, J. Perdigues, P. Trojek,B. Omer, M. Furst, M. Meyenburg, J. Rarity, Z. Sodnik, C. Barbieri,H. Weinfurter, and A. Zeilinger. Entanglement-based quantum commu-nication over 144km. Nat. Phys., 3:481–486, 2007.

[26] D. S. Naik, C. G. Peterson, A. G. White, A. J. Berglund, and P. G.Kwiat. Entangled state quantum cryptography: Eavesdropping on theekert protocol. Phys. Rev. Lett., 84:4733, 2000.

[27] M. Mafu, A. Dudley, S. Goyal, D. Giovannini, M. McLaren, M. J. Pad-gett, T. Konrad, F. Petruccione, N. Lutkenhaus, and A. Forbes. Higher-dimensional orbital-angular-momentum-based quantum key distributionwith mutually unbiased bases. Phys. Rev. A, 88:032305, 2013.

[28] A. Martin, F. Kaiser, A. Vernier, A. Beveratos, V. Scarani, and

S. Tanzilli. Cross time-bin photonic entanglement for quantum keydistribution. Phys. Rev. A, 87:020301, 2013.

[29] J. Kempe. Multiparticle entanglement and its applications to cryptog-raphy. Phys. Rev. A, 60:910–916, 1999.

[30] W. Tittel, H. Zbinden, and N. Gisin. Experimental demonstration ofquantum secret sharing. Phys. Rev. A, 63:042301, 2001.

[31] S. Groblacher, T. Jennewein, A. Vaziri, G. Weihs, and A. Zeilinger.Experimental quantum cryptography with qutrits. New J. Phys., 8:75,2006.

[32] J. F. Clauser and M. A. Horne. Proposed experiment to test localhidden-variable theories. Phys. Rev. Lett., 23:880–884, 1969.

[33] D. Collins, N. Gisin, N. Linden, S. Massar, and S. Popescu. Bellinequalities for arbitrarily high-dimensional systems. Phys. Rev. Lett.,88:040404, 2002.

Dr. Sumanta Bagchi is Assistant Professor at Centre for Ecological Sciences, Indian Institute of Science (CES-IISc), Bangalore 560012, India. [email protected] Ekta Gupta is a research fellow at CES-IISc. Karthik Murthy is a Ph.D. student at CES-IISc

Project number: ISTC/BES/SB/332

Investigation of vegetation changes in the arid Trans-Himalayan ecosystem of northern India

Sumanta Bagchi, Ekta Gupta, Karthik Murthy

Abstract – Understanding ecosystem responses to current and projected climate change is important for sus-tainable land-use policies. The cold and arid Trans-Himalayan ecosystem is experiencing changes in regional climate (precipitation and tempera-ture) amidst concerns over land-degradation. We are assessing vegeta-tion growth and distribution using sat-ellite imagery to investigate the nature and severity of degradation. We ana-lyzed changes in Normalized Differ-ence Vegetation Index (NDVI), for phenological and biomass indicators in the watersheds of major wetlands in Ladakh region of northern India. The results will help resolve long-standing concerns over climate-induced deserti-fication in the Trans-Himalaya.

I. INTRODUCTION Central Asian highlands are a vast ex-panse of mountainous terrain that in-cludes of Trans-Himalaya in northern India. These represent arid ecosystems of high anthropological, biogeochemical, ecological, and hydrological significance (1-3). But they are also highly suscepti-ble to ongoing and projected changes in regional climate (4, 5). The Trans-Himalayan ecosystem covers large ex-panses of arid and semiarid rangelands in Ladakh (India), Spiti (India), Tibet (China) and Mustang (Nepal), and is ex-

periencing the effects of changes in both precipitation and temperature. There are widespread concerns over land-degradation and desertification that have adverse effects on biodiversity as well as on human livelihoods (5, 6).

The general climatic trend over the last three decades in this region, and more broadly across the western Hima-layan alpine shrub and meadow biome (5) is towards: (i) increasing mean an-nual temperature, (ii) decreasing winter precipitation (November-February), and (iii) increasing summer precipitation (August-September). Although there may be a net increase in total annual pre-cipitation, counter-intuitively, there is also evidence for desertification. It re-mains unclear whether and how climatic changes may play a major role in land degradation and desertification. The ex-tent, intensity, and magnitude of land degradation also remain poorly docu-mented.

In this project, we are analyzing temporal patterns of change in vegeta-tion phenology and biomass in the Trans-Himalayas using satellite-derived indices (Normalized Difference Vegeta-tion Index, NDVI). Initially we investi-gated changes in watersheds of major wetlands in Ladakh region, as these sup-port the highest densities of livestock grazers, and are likely highly susceptible to vegetation degradation.

II. METHODS


We used available NDVI data from MODIS for 500 m resolution. We ex-tracted NDVI data from 16-day interval MODIS images between 2004 and 2013. From this we extracted pixels from dif-ferent major watersheds in Ladakh, namely: Tso-Kar, Tso-Moriri, and Pan-gong-Tso. We screened these pixels based on additional information (pixel quality and data reliability).

Vegetation growth, as indicated by NDVI scores, follows a well-known double-sigmoidal pattern over an annual cycle (January to December, Fig. 1). We used nonlinear regression models in R 3.0.3 software to estimate the following parameters for each year: time of onset of growth, rate of increase after onset, peak vegetation biomass, rate of decline after peak, and duration of growth sea-son.

0 50 100 150 200 250 300 350

0.0

0.2

0.4

0.6

0.8

1.0

Fig. 1: Illustrative example for vegetation growth

Julian day (Jan-Dec)

ND

VI s

core

peak

rate of increaserate of decrease

onset end

III. RESULTS & DISCUSSION

Here we show results for the Tso-Kar watershed (Fig. 2). Key results of nonlinear regression models for 2003 are summarized in Table 1. In general, the statistical models perform well in esti-mating four of the parameters, but need further improvements in estimates of onset of growth season. In future work, we will compare estimates across time.

0 50 100 150 200 250 300 350

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Fig. 2: Tso-Kar watershed, 2003

Julian day (Jan-Dec)

ND

VI s

core

Table 1. Model parameters estimated by nonlinear (double-sigmoidal) regression for five parameters of vegetation growth.

Parameter Estimate 95% Confi-

dence limits

Peak growth 0.8 0.6-1.6

Rate of increase 0.01 day-1 0.009-0.013

Onset of growth 272nd day 215-355

Rate of decrease 0.03 day-1 0.02-0.04

End of growth 289th day 281-301

References

1. G. Miehe et al., Palaeogeography, Palaeoclimatology, Palaeoecology 276, 130 (2009).

2. S. Bagchi, M. E. Ritchie, Ecol. Lett. 13, 959 (2010).

3. W. R. Boos, Z. Kuang, Nature 463, 218 (2010).

4. X. Liu, B. Chen, Int J Climatol 20, 1729 (2000).

5. U. B. Shreshta, S. Gautam, K. S. Bawa, PLoS One 7(5), e36741 (2012).

6. W. Shaohong, Y. Yunhe, Z. Du, Y. Qinye, J Geogr Sci 17, 141 (2007).

ISTC/CSS/STP/0339 Page 1

Study on Self-assembly of Donor-Acceptor-Donor Molecular Materials and Melamine

Nileshi Saraf, Joydeep Dhar and Satish Patil* Solid State and Structural Chemistry Unit, Indian Institute of Science, Bangalore, India 560012

Email: [email protected]

Abstract

In the present work, the interaction between donor-acceptor-donor molecular oligomers (TTB) and melamine is studied with the help of various spectroscopic and electron microscopy techniques. TTB is a π -conjugated molecule which has donor-acceptor-donor (D-A-D) structure and can be used as an active chromophore in solar cell devices. However, we observed that the self-assembled ribbon like morphology of TTB results in low PCE due to phase separation within the active layer of bulk heterojunction solar cells. Melamine is introduced to disrupt the intermolecular hydrogen bonding within the TTB molecules which results in change in optical properties. The FTIR and NMR analysis suggests the presence of hydrogen bonding between the barbituric unit of TTB and amine group of melamine. AFM studies show the morphological variations in the films after adding melamine in different ratios.

Introduction

The donor-acceptor-donor (D-A-D) based small molecules and polymers have been extensively investigated for applications in organic FETs and solar cell devices.1-3 In this context, our group explored self-assembly approach of barbituric acid TTB (D-A-D) molecules in organic solar cells and field-effect transistors.4 Barbituric acid based TTB molecule is synthesized by Knoevenagel condensation of terthiophenecarbaldehyde and barbiturate appended pyran derivative as discussed in our earlier studies.5 The barbiturate and thiophene units act as donor and acceptor group, respectively. This molecule undergoes inter-molecular hydrogen bonding due to the presence of acidic proton (N−H) and electronegative oxygen (C=O) of barbiturate functional group. The H-bond mediated self-assembled morphology of TTB results in long ribbon like structures which might be disrupted by introducing melamine as shown in Fig. 1.

Figure 1: Plausible intermolecular H-bonding between TTB and Melamine



Results and Discussions

The interaction between TTB and melamine was examined with the help of various characterization techniques. Photophysical properties were investigated by UV-vis spectroscopy and fluorescence studies. Atomic force microscopy (AFM) was used to observe morphological changes in the film after adding melamine. FTIR and NMR results demonstrate the presence of hydrogen bonding between the molecules of melamine and TTB.

A) NMR spectroscopy The NMR study of TTB and TTB-

melamine at different molar ratio (1:0.25, 1:0.5, 1:1, 1:2, 1:4) is shown in the Fig. 2. The samples were prepared in CDCl3. It can be seen that there is an apparent shift in the peak (~7.54) of proton of the barbiturate unit present in TTB with the addition of melamine. The maximum downfield shift (~7.57) with the broadening of the peak-width is observed at 1:1 molar ratio of TTB/melamine indicating strongest H-bonding interaction. Further, the peak position slightly shifts upfield and broadens with further increase in melamine concentration. This suggests that with further increase in melamine concentration number of H-bond increases, but the strength of the H-bond decreases.

Figure 2: NMR analysis of TTB and TTB-Melamine with varying molar ratio of melamine.

B) FTIR analysis

Figure 3: FTIR spectra of TTB and TTB-Melamine

The FTIR analysis of TTB and TTB-melamine composite (Fig. 3) was performed in KBr pellet method. It was observed that the C-N stretching at 1357 cm-1 and 1295 cm-1 in TTB molecules diminished after adding melamine. The peak due to C=O stretching (1620 cm-1, 1640 cm-1) and –NH bending (amide II) (1496 cm-1) in TTB shifts at higher frequency after addition of melamine due to the formation of H-bond between TTB and melamine.

C) Optical Properties

The thin film normalized absorption spectra of TTB and TTB/melamine at different molar ratio are shown in Fig. 4(a). The band at 390 nm corresponds to the π-π* transition and the band at 517 nm is due to the intramolecular charge transfer from the donor to the acceptor moiety in TTB. It can be seen that there is no shift in the absorption maxima of TTB with gradual addition of melamine. The thin film emission spectra (Fig. 4(b)) shows that the fluorescence intensity varies with varying melamine concentration. The intensity is maximum at 1:1 molar ratio followed by 1:2 whereas 1:4, 1:2 and TTB shows similar emission intensity. Therefore, the results of


the fluorescence study indicate that at 1:1 molar ratio the favorable interaction between TTB and melamine efficiently restricts the self-quenching phenomenon of TTB.

(a)

(b)

Figure 4: (a) Normalized Absorption and (b) Emission Spectra of TTB and TTB-Melamine at different molar ratio of melamine in thin film

D) AFM analysis

The p-type Si (100) wafers were pre-cleaned by repeated sonication in acetone followed by piranha treatment. The wafers were then thoroughly rinsed with distilled water and dried with nitrogen flow. Oxygen plasma treatment was done for 15 min before depositing films in order to make the surface hydrophilic to ensure the uniformity of the deposited films. Thin films of TTB and TTB-melamine dissolved in chlorobenzene

were spin-coated onto pre-cleaned Si wafers at 1000rpm for 60 sec. The AFM image of TTB (1mg/ml) as shown in Fig. 5 shows nanoribbon-like structure with width around 60nm and height ranging from 5-15 nm (Fig. 5(b)).

Figure 5(a): AFM analysis of TTB dissolved in chlorobenzene (1mg/ml)

Figure 5(b): Depth profile of AFM image of TTB dissolved in chlorobenzene (1mg/ml)

Melamine in the molar ratio of 1:0.05,

1:0.1, 1:0.2, 1:0.4 was added in TTB and changes in the morphology is studied as shown in Fig. 6(a-d). It can be seen that as the concentration of melamine is increased, the TTB network is becoming denser. Melamine assists the TTB molecule to form a better interdigitated network enhancing the area of coverage.


(a) (b)

(c) (d)

Figure 6: AFM images of TTB-Melamine at (a) 1:0.05, (b) 1:0.1, (c) 1:0.2 and (d) 1:0.4 molar ratios.

E) DSC measurements

Figure 7: DSC thermogram of TTB and TTB-Melamine

with different molar ratio of melamine

The thermal properties of TTB and

TTB-Melamine were investigated by using differential scanning calorimetry (DSC). It was observed that TTB shows a higher melting point of around 237°C which is due to the intermolecular hydrogen bonding. DSC results as shown in Fig. 7 suggests that as melamine concentration is increased, the melting point shifts to lower value with minimum of 174°C in 1:1 molar ratio due to

the disruption of strong H-bonding between the TTB molecules. The crystallization behavior in cooling cycle was not observed.

Conclusion and Future Plan

In conclusion, we have investigated the morphological, thermal and optical properties of TTB and TTB-Melamine composite. The results reported here clearly states that melamine disrupts the self-assembly between the TTB molecules. The TTB-Melamine in a ratio of 1:1 shows promising behavior for further use in solar cells with strongest hydrogen bond interaction. Further studies on its electronic properties are under progress in order to investigate the utility of these materials for solar cells.

Acknowledgement

We sincerely acknowledge funding from ISRO-STC.

References

1. Garnier F.; Hajlaoui R.; Yassa A.; Srivastava P. Science 1994, 265, 1684–1688

2. Burroughes J.; Bradley C; Brown A.; Marks R.; Mackay K.; Friend R.; Burns, P.; Holmes A.; Nature 1990, 347, 539–540.

3. Liang, Y.; Feng, D.; Wu, Y.; Tsai, S. T.; Li, G.; Ray, C.; Yu, L.; J. Am. Chem. Soc. 2009, 131, 7792–7799.

4. Bhaskar, R.; Tandy, K.; Horecha, M.; Formanek, P.; Stamm, M.; Gevorgyan, S.; Krebs, F.; Kiriy, A.; Meredith, P.; Burn, P.; Namdas, E.; Patil, S.; J. Phys. Chem. C 2011, 115, 14369–14376.

5. Bhaskar, R.; Stephen, M.; Ali, F.; Patil, S.; J. Phys. Chem. C 2013, 117, 9129−9136.

High-resolution Image restorationand enhancement using GPU

Nithish Divakar and R. Venkatesh Babu

Abstract—The Weighted Nuclear Norm Minimisation fordenoising is an effective denoising algorithm which has a verygood denoising performance at high noise levels. But it requiresa huge amount of computational time. Here we propose amethodology by which the computationally intensive part ofWNNM is accelerated using GPUs. The acceleration requirescomputation of singular value decomposition of large number ofaverage sized matrices. We also discuss a better way to place datain GPU memory so that it results in high memory bandwidth.

I. INTRODUCTION

A digital image can be considered as a discrete represen-tation of continuous 2D signal. A generic noisy image modelcan be expressed as follows.

y(p) = x(p) + η(p)

where x is original image,y is the noisy image and η is thenoise that gets mixed. Image denoising algorithms attempt toobtain an approximation x of the the signal x, when only thesignal y and some knowledge of noise η is known.

Image denoising using weights nuclear norm minimisationis a denoising algorithm which shows a very good recoveryrate even at high noise levels [1], but WNNM denoising re-quires huge computational time. Here we propose an approachwhich can accelerate this algorithm using GPUs.

II. RELATED WORK

Many state of the art denoising algorithms exist for imagedenoising, most, differing only in their model of what noiseis. From the assumptions that natural images constitutes piece-wise smooth functions, image denoising algorithms like theones given by Lindenbaum et al. in [2] uses smoothening filterto remove noise. But this approaches fails to preserve imageedges as their noise model includes all sudden variations inthe signal.

Non Local means algorithm suggested by Buades et al.in [3] removes noise by taking contribution from only thosepixels which are similar to the key pixel.

BM3D algorithm proposed by Dabov et al. in [4] computesneighbourhood of blocks in transform domain and collabora-tive filtering is used to estimate pixel intensities.

Although the non local neighbourhood model appears tobe concrete in estimating the noise model, it is intrinsicallydepending on the fact that a major portion of original image

Nithish Divakar is masters student in the SERC De-partment, Indian Institute of Science, Bangalore, [email protected]

R. Venkatesh Babu is an assistant professor in the SERC Department, IndianInstitute of Science, Bangalore, India [email protected]

details can still be extracted despite the corruption from noise.This assumption fails at higher noise levels.

Low rank matrix approximation based image denoisingmethods uphold there denoising efficiency even at higher noiselevels. Weighted nuclear norm minimisation based denoisingproposed by Gu et al. in [1] is such a strategy which usesweighted reduction of data variability along different dimen-sions to reduce noise.

Although this method shows good denoising performance,the algorithm’s heavy resource consumption in terms of com-putation time makes it unusable for application that require onthe line processing. This research effort is to improve WNNMdenoising algorithm’s execution time.

III. WEIGHTED NUCLEAR NORM MINIMISATION FORIMAGE DENOISING

Weighted nuclear norm minimisation is a problem in lowrank matrix approximation theory [5], which uses a nuclearnorm as the objective function for low rank matrix completion.The problem can be stated as follows

X = arg minX‖Y −X‖2F + ‖X‖w,∗

w is a weight vector and ‖X‖w,∗, is defined to be dotproduct of the singular values of X and the weight vector.The algorithm is throughtly described in Gu et al. [1]

WNNM is used in image denoising by constructing a matrixwhose columns are vectorised patches which are obtained froma local neighbourhood of a key patch. Local neighbourhoodof each patch is defined as the set of patches which are closerto it in terms of some distance measure. Usually, `2 normdistance is used.

Denoising is essentially constructing the low rank approxi-mation of the original matrix. WNNM helps us in achievingthis.

IV. PROPOSED APPROACH

The most computationally intense part of the WNNMalgorithm is to compute the singular value decomposition ofthe neighbourhood matrix for evech key patch. But the com-putation corresponding to one key patch is totally independentof another.

The key part of the proposed approach is a technique tocompute all the required SVDs on a GPU device. Since theneighbourhood matrix is not very large, the usual algorithmsfor computing singular value decomposition does not providean optimum performance as they have been designed to handlelarge matrices.


More over, the WNNM algorithm requires computation ofSVDs for a large number of matrices.

V. ACCELERATION USING GPUAn easy approach for computing all the required SVDs

would be to simply offload the computation to GPU suchthat a single thread compute SVD per matrix. The executiontimings of this approach can be seen in Table I. This approachhas many possible improvement because it uses only a smallfraction of the thread pool available.

A better idea would be to use multiple threads to computethe SVDs in parallel, leading to 2 levels of parallelism.

We have used the lowest number of threads which haveimplicit synchronisation between them. This atomic set ofthreads are called as warps. The hardware on which ourexperiments were conducted restricts this number to be 32.But the following methodology can be easily adapted to anyother hardware specification.

Our specific problem structure advocates us to use a dif-ferent algorithm for SVD computations. Biorthogonalisationalgorithm [6] for svd computation offers a faster and moreunifrm memory access pattern when used in parallel thanstandard bi-diagonalisation algorithm.

Biorthogonalisation procedure was first proposed byHestenes in [6]. A good discussion about better accuracy ofbiorthogonalisation procedure and its implementation can befound in [7]. The Complete algorithm is given in Figure 1.

1: procedure BIORTHOGONALISATION(A)2: [a1|a2| . . . |an] = A conv = false . Columns of A3: V = In di = aTi ai ∀i = 1, . . . , n . 4: while 30 or less iteration and conv = false do5: for Each column pair (ai, aj) : i < j do6: if |aTi aj | > mε

√didj then .

7: Compute the givens rotation angles8: Rotate column vector through the angle9: Update corresponding dot products

10: end if11: end for12: Set conv = true if no columns were updated13: end while14: if conv=true, then set Σii =

√di and ui = aiΣii .

15: U = [u1|u2| . . . |un]16: end procedure

Fig. 1. SVD decomposition by Biorthogonalisation

A. Data distribution in GPU memory for optimum perfor-mance

The statements marked by “” in Figure 1 can be executedin parallel by a a set of GPU threads. During the comutation,because of the structure of the algorithm, adjacent threads inthe warp access elements of a olumn i matrix together. Thissuggest us to use a stacked transpose storage of matrices inthe memory for faciliting non strided memory access. So wewill use a P ×N ×M order stacking where P is number ofmatrices and M ×N is matrix dimensions.

VI. EXPERIMENTAL RESULTS

All experiments were carried out in a system with IntelXeon CPU E5-1650 v2 processor and Tesla K20 GPU cardwith 5 GB of GDDR5 memory.

The patch size used by the algorithm is 9. So the dimensionof the vectorised patches is fixed at 81 components. Also, thenumber of SVDs to be performed throughout the algorithmalso stays the same at 7056 for an image size of 256× 256.

The running times are calculated for matrix size which thealgorithm encounters during a successful run.

TABLE IEXECUTION TIME OF BOTH SINGLE AND 32 THREAD PARALLEL SVD

COMPUTATION

Problem size (m× n× p) One Thread 32 Threads81× 130× 7056 10.4893s 1201ms81× 120× 7056 9.5463s 954ms81× 110× 7056 8.0076s 800ms81× 100× 7056 7.3253s 653ms81× 90× 7056 6.9263s 490ms81× 80× 7056 5.0035s 383ms81× 70× 7056 4.5902s 271ms

A. Final speedup

The original algorithm spent about 575.739 seconds for allrequired SVD computations. Our method is able to performsame amount of comutation in 9.504 seconds. Thus we haveobtained about 60x acceleration in required decompositionpart.

VII. CONCLUSION

The results from the experiments suggested that WNNMmethod has lots of potential to become a fast and efficientdenoising algorithm. We were able to reduce the time taken forthe most computationally intensive part of the algorithm by 60times. If other parts of the algorithm are equally accelerated,WNNM can become a fast algorithm indeed.

REFERENCES

[1] S. Gu, L. Zhang, W. Zuo, and X. Feng, “Weighted nuclear normminimization with application to image denoising,” in IEEE Conf. onComputer Vision and Pattern Recognition, 2014.

[2] M. Lindenbaum, M. Fischer, and A. Bruckstein, “On gabor’s contributionto image enhancement,” Pattern Recognition, vol. 27, no. 1, pp. 1–8, 1994.

[3] A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for imagedenoising,” in Computer Vision and Pattern Recognition, 2005. CVPR2005. IEEE Computer Society Conference on, vol. 2. IEEE, 2005, pp.60–65.

[4] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising bysparse 3-d transform-domain collaborative filtering,” Image Processing,IEEE Transactions on, vol. 16, no. 8, pp. 2080–2095, 2007.

[5] I. Markovsky and K. USEVICH, Low rank approximation. Springer,2012.

[6] M. R. Hestenes, “Inversion of matrices by biorthogonalization and relatedresults,” Journal of the Society for Industrial & Applied Mathematics,vol. 6, no. 1, pp. 51–90, 1958.

[7] Z. Drmac, “Implementation of jacobi rotations for accurate singular valuecomputation in floating point arithmetic,” SIAM Journal on ScientificComputing, vol. 18, no. 4, pp. 1200–1222, 1997.

*Corresponding Author, Email: [email protected]. IISC-ISRO STC Project Code Number: ISTC/MAE/SC/334 Part of this work appeared as Technical Publication Nos. ASME GTINDIA 2014-8340 in the 2014 ASME GT India Conference, New Delhi

Turbulence-transport-chemistry interaction in statistically planar premixed flames in near isotropic turbulence

Harshavardhana A. U, Swetaprovo Chaudhuri* and K. N. Lakshmisha

Department of Aerospace Engineering, Indian Institute of Science, Bangalore, INDIA

Abstract - Turbulence-transport-chemistry interaction plays a crucial role on the flame surface geometry, local and global reaction rates, and therefore, on the propagation and extinction characteristics of intensely turbulent flames encountered high speed combustors. The aim of the present work is to understand these interaction effects on the flame surface. This will be useful in developing an insight into the annihilation and extinction mechanism of lean premixed flames, interacting with near isotropic turbulence. As an example case, lean premixed H2-air mixture is considered with detailed chemistry effects in Direct Numerical Simulations (DNS). The work is carried out in two cases where a statistically planar flame interacts with near isotropic turbulence of different turbulence intensities (Case 1 ReT = 146 and Case 2 ReT = 351). A recently proposed Flame Particle Tracking (FPT) technique was used for this purpose. Flame particles are surface points residing and comoving with an iso-scalar surface within a premixed flame. Tracking flame particles allows us to study the evolution of propagating surface locations uniquely identified with time along with the evolution of various properties at those locations. In this work, using DNS and FPT we study the flame speed, reaction rate and transport histories of such flame particles residing on iso-scalar surfaces. An analytical expression for the local displacement flame speed (Sd) is derived, and the contribution of transport and chemistry on the displacement flame speed is identified. An examination of the results of both the cases leads to a conclusion that the cause of variation in Sd may be attributed to the effects of turbulent transport and curvature.

I. INTRODUCTION

Turbulent combustion can be understood to a greater extent in terms of propagating iso-scalar surfaces particularly for studying the phenomena such as turbulent mixing and reactions on these surfaces [1]. This approach is followed by many researchers working on the turbulent combustion by using detailed, reduced and/or single step chemistry to model the reactions.

The previous studies on the statistically planar premixed flames considered an iso-scalar surface of mass fraction [2-3] and reaction progress variable [4]. In the work of Echekki et al. [2-3] the effects of strain rate, and curvature are examined on the propagation of an iso-scalar surface of mass fraction of CH4. Further, in [4] the effects of non-

unity Lewis number were assessed on the behaviour of the displacement flame speed. These studies used either reduced or single step chemistry to model the reactions. In spite of important insights gained from these studies, the focus was on understanding the global effects of the turbulence flame interaction and the local effects are not studied.

The effects of turbulent mixing and dispersion are understood to a great extent with Lagrangian type of flow description. However, a semi-Lagrangian approach is an attractive way to study the interaction of premixed flame with turbulence. A brief of this approach is described below.

In the recent work of S. Chaudhuri [5], a new technique is proposed to study the local effects on the flame surface by


tracking the evolution of discrete locations on an iso-scalar surface within a premixed flame and is called as Flame Particle Tracking (FPT). Further, the FPT technique is used to study the kinematic effects such as strain rate and curvature on propagation characteristics of the premixed flame.

In the present work utilizing the FPT technique, the interaction between

turbulence-transport-chemistry is studied by considering statistically planar premixed flames interacting with near isotropic turbulence with two turbulence intensity levels namely,

1. Low turbulence intensity level (Case 1, ReT = 146)

2. High turbulence intensity level (Case 2, ReT = 351)

Figure 1 Computational domain of statistically planar flame in near isotropic turbulence. a) 2D schematic and b) 3D domain showing flame surface with black dots representing the flame particles

II. COMPUTATIONS

A. Direct Numerical Simulations

In the present work, three dimensional Direct Numerical Simulations of lean, premixed, H2-air mixture are performed using the open source PENCIL CODE [6]. The combustion chemistry part in the pencil code is implemented by Babkovskaia et al. [7], where the Navier-Stokes, energy and species equations are solved in the compressible form for a turbulent, reacting mixture. Further, for both the cases the combustion chemistry is modelled by using detailed H2-air mechanism developed by Li et al. [8], consisting of 19 elementary reactions and 9 species. The inlet conditions namely, temperature (T) = 310 K, pressure (P) = 1 atm, equivalence ratio () = 0.81 and Lewis number (Le) = 0.84 (diffusivity

calculated with that of the deficient species H2), are maintained same for both cases. Also, all the variables pertaining to both the cases are listed in Tab. 1.

B. Flame Particle Tracking Algorithm

The DNS data obtained from the above simulation serves as an input to the FPT algorithm. On a given iso-scalar surface various parameters are calculated for each flame particle and for the iso-surface. The FPT algorithm is described briefly in the following paragraph and readers can refer to [5] for further details. The position of the flame particle at any time instant (t) is governed by,

where, ([], ) is the flow velocity; (

[], ) is the local displacement flame speed of the iso-surface and ([], ) local unit normal. Each of these variables are dependent on () at time t.

In the FPT technique, a constant property surface which propagates into the flow is chosen and the discrete locations on the surface are marked and are called as “flame particles”. This flame particles are surface points [1] on a reacting propagating surface. The evolution of

flame particles with time obeys the Eq. (1). The particle locations are advanced with the time and the co-ordinates of the flame particles at time level t+t is obtained from the previous time level t using the triangle-ray intersection algorithm. The noticeable distinction between the present method and the previous works on particle tracking is that, the flame particles are reactive and they are confined to a specific iso-scalar surface.


A. Statistically planar flames

In [5] two statistically planar flame cases (Case 1 and Case2) experiencing different turbulent intensity levels were investigated using FPT. It was found that the tangential strain rate and also, curvature increased to large values towards the end of the flame particle lifetime (Here, the negative is used to indicate that the flame surface is concave towards reactants. A positive value indicates that flame surface is convex towards reactants). These observations were attributed to the

reorientation of local surface normal w.r.t. the most extensive component of the principal strain rate. This leads to persistently increasing tangential strain rate, cusp formation and further leading to annihilation of the flame surface. It was also shown that the local displacement flame speed Sd sharply increased towards the end of flame particles. In recent computational work [9], similar phenomena was also observed, albeit with single step chemistry.

Figure 2 Displacement flame speed variation with normalized time for Tiso = 1493K (a) Case 1 (b) Case 2

(a) (b)

(1)

Figure 3 (a) Heat flux (b) Diffusion flux and (c) Heat release rate variation with normalized time for Tiso = 1493K and Case 1

Figure 4 (a) Heat flux, (b) Diffusion flux and (c) Heat release rate variation with normalized time for Tiso = 1493K and Case 2

Figure 5 Magnitude of gradient of T variation with normalized time (a) Case 1 and (b) Case 2 for Tiso = 1493K

For an iso-temperature surface the analytical expression for Sd is as follows,

In the Figs. 2 to 5, the red lines represent the variation in the property for individual particles and the black line with circles is the ensemble average of the property over all the particles at that instant.

Figures 2a and 2b shows the

variation of Sd with , for Case 1 and

Case 2, respectively for the iso-temperature surface of Tiso = 1493K. Here, , is the life time of the flame particle. It is total duration between the instant at

which the particle is placed on the iso-surface to the time instant at which it is lost. It is observed from Fig. 2a that the Sd

remains constant up to , ≈ 0.8 and

increases rapidly towards the end. To assess this behavior, individual terms of Sd equation (Ref. Eq.(2)) are analyzed.

The first term of the Sd equation is the heat flux term and it shows an increase with the normalized time, as shown in Fig. 3a. The Figs. 3b and 3c shows the behavior of

(a)

(a)

(c) (b)

(b)

(a)

(b) (c)

=1

||. () +

1

−ℎ

()

diffusion flux term and the heat release rate term, respectively. These terms show a decreasing trend with normalized time signifying negative contribution to the Sd. Further, the |∇| in the denominator of the expression, as plotted in Fig. 5a, shows a decreasing trend with normalized time. The negative contribution of diffusion flux and heat release rate to Sd is overcome by the drop in |∇|, leading to an overall increase in resultant. Since, the |∇| can be related to the curvature, it can be argued that for a constant temperature surface the increase in Sd is due to curvature and heat focusing effects.

Similar to the Case 1 of Tiso=1493K, Fig. 2b shows the variation of Sd for the Case 2 for the same iso-surface. Here though the Sd value increases continuously from the initial time itself, the variation

however, is more steep from , ≈ 0.8

onwards. To understand this behavior of Sd, again the individual terms of Eq. (2) are analyzed. The behavior of individual terms pertaining to Case 2 and Tiso = 1493K are shown in Fig. 4 and Fig. 5b. The nature of heat flux, diffusion flux and heat release rate is shown in Fig. 4a, 4b and 4c respectively. Further, Fig. 5b shows the variation in magnitude of gradient of T with normalized time. Similar to the Case 1, the analysis of the individual terms shows that the increase in Sd towards the end of particle life time can be attributed to the heat focusing and curvature effects.

Thus, from the study presented in this paper, it can be concluded that the increase in Sd is universal for the constant temperature iso-surfaces towards the end of the flame particle lifetime. Further, the increase in Sd is mainly attributed to pure curvature and heat focusing effects.

IV. CONCLUSIONS AND FUTURE WORK

In the present work, by using flame particle tracking algorithm, the flame surface annihilation and propagation of statistically planar premixed flames is studied. The increase in the displacement flame speed towards the end of the particle lifetime is universal irrespective of the turbulence intensity level and initial condition of the flame surface. This

increase in Sd is attributed to decrease in |∇| which influences the Sd through the curvature increase to large negative values.

The FPT technique is an important tool in understanding the local phenomena of the flame surface. Further, FPT technique will be employed to understand the flames in the shear layers in high speed flows. The stabilization of flames at high

Case 1 Case 2

Grid 256x128x128 256x128x128

Domain (cm) 1x0.5x0.5 1x0.5x0.5

∆ 9 9

⟨⟩ 300 500

(cm/s) 192 503

146 351

(cm) 0.119 0.109

() 627 217

() 28 13

() 52 12

(K) 310 310

0.81 0.81 Flame

Thickness (μm)

361 361

Δ() 4 2.5

() 680 292

Table 1 List of Parameters

speeds is a challenging task as the flow time scales are very small compared to chemical time scales. The phenomenon such as local to global extinction transition

is very crucial to understand the flame stabilization. The FPT technique offers us elegant way to study this extinction transition.

REFERNCES

1. Pope, S., 1988. “The evolution of surfaces in turbulence”. International journal of engineering science, 26(5),pp. 445–469.

2. Echekki, T., and Chen, J. H., 1996. “Unsteady strain rate and curvature effects in turbulent premixed methane-air flames”. Combustion and Flame, 106(1), pp. 184–202.

3. Echekki, T., and Chen, J. H., 1999. “Analysis of the contribution of curvature to premixed flame propagation”. Combustion and Flame, 118(1), pp. 308–311.

4. Chakraborty, N., and Cant, R. S., 2005. “Influence of lewis number on curvature effects in turbulent premixed flame propagation in the thin reaction zones regime”. Physics of Fluids (1994-present), 17(10), p. 105105.

5. Chaudhuri, S., 2014. “Life of flame particles embedded in premixed flames interacting with near isotropic turbulence”. Proc. Combust. Inst. (2014),http://dx.doi.org/10.1016/j.proci.2014.08.007.

6. http://www.nordita.org/software/pencil-code/.

7. Babkovskaia, N., Haugen, N., and Brandenburg, A., 2011. “A high-order public domain code for direct numerical simulations of turbulent combustion”. Journal of computational physics, 230(1), pp. 1–12.

8. Li, J., Zhao, Z., Kazakov, A., and Dryer, F. L., 2004. “An updated comprehensive kinetic model of hydrogen combustion”. International Journal of Chemical Kinetics, 36(10), pp. 566–575.

9. Andrei N. Lipatnikov, Vladimir A. Sabelnikov, S. N., and Hasegawa, T., 2014. “Unburned mixture fingers in premixed turbulent flames”. Proc. Combust. Inst. (2014),

http://dx.doi.org/10.1016/j.proci.2014.06.081.

Thermally Activated Unimolecular Decomposition Mechanisms of Dimethylnitramine-Aluminum (DMNA-Al) and Dimethylnitramine-Zinc

(DMNA-Zn) Complex

Anupam Bera and Atanu Bhattacharya*

Department of Inorganic and Physical Chemistry, Indian Institute of Science, Bangalore –560012, India *[email protected]

Abstract In this report we discuss the effects of metal atoms in the ground state decomposition mechanisms of dimethylnitramine (DMNA), which is the simplest nitramine molecule, by means of quantum chemical calculations. For the present study, we have considered two important metal atoms, aluminum (Al) and zinc (Zn). The decomposition pathways of isolated DMNA, DMNA-Al and DMNA-Zn have been explored at the MP2/6-31G(d) level of theory. The reaction pathways of the isolated DMNA, DMNA-Al, and DMNA-Zn are also compared and contrasted in this report. MP2 level of theory predicts that isolated DMNA can follow N-NO2 dissociation as well as nitro-nitrite isomerization pathways; however, N-NO2 bond dissociation pathway is associated with the lowest activation energy barrier. DMNA-Al complex, on the other hand, shows significantly different decomposition pathway: it involves several steps, such as Al-O bond dissociation, and then N-N bond dissociation followed by an isomerization. DMNA-Zn complex exhibits, first, N-N bond dissociation, and after transforming to a very stable dimethyl (µ2-nitro) amine zinc complex it undergoes a Zn-O bond dissociation which leads to the similar nitrite isomerization product and finally NO elimination. In contrary to the DMNA results, NO elimination is found to be the lowest energy pathway for both DMNA-Al and DMNA-Zn complexes.

Introduction

For a long time, molecular energetic materials (EMs), produced by mixing oxidizer and fuel constituents into one molecule, (e.g., nitroglycerine, RDX, HMX, etc.), have been the centre of attraction for high energy propellant applications. However, recently, novel composite metalized energetic materials (mEMs),[1] containing metal particles (e.g., Fe, Al, etc.) and traditional molecular EMs, have drawn significant attention due to their superior performance in advanced propellant applications.[2] They are found to release more than twice as much energy as the best molecular explosives do.[3] As compared to the “conventional” molecular EMs, mEMs offer the possibility of faster energy release, more complete combustion and greater control over performance. Burn rates of metalized energetic propellants can be accelerated by controlling the size of the constituent metal particles. Tuning the surface-to-volume ratio of the metal particles, the rate of chemical reactions of composite mEMs can also be controlled.1

Enhanced performance of mEM is often accounted for the exothermicity of oxide formation reaction for constituent metal during combustion. For an example, heat of formation of alumina from aluminum (4Al+3O2→2Al2O3) is -1676kJ/mol.[4] During burning of molecular EMs in mEM, the Al particles are oxidized and

Project # ISTC/CIP/AB/333


the exothermic heat is added to the total heat of reaction. This thermodynamic explanation of superior performance of mEM over molecular EM is well accepted in energetic material community. However, another part of this problem has never been seriously considered: the presence of metal (particle) surface can also alter the decomposition mechanisms and dynamics of molecular EMs. How does metal surface alter decomposition mechanisms and dynamics of molecular EMs? How does metal surface transfer energy to the molecular EMs during burning? These questions have mostly remained unanswered, thus far. In the quest of the answers to these questions, this article presents theoretically predicted decomposition pathways of a simple analogue nitraminmolecule and its complex with aluminum atom.

RDX(hexahydro-1,3,5-trinitrotriazine), HMX (Octahydro-1,3,5,71,3,5,7-tetrazocine, C4H8N8O8) and CL(2,4,6,8,10,12-hexanitro-2,4,6,8,10,12hexaazaisowurtzitane) are well known nitramine (N-NO2)-based energetic materials. Their chemical structures are depicted in Figure 1. These molecules possess a number of N(nitramine energetic moieties. Due to the complex structures of nitramine energetic molecules, often a structurally simple analmolecule dimethylnitramine (DMNA, see Figure 1 for structure), containing only one Nenergetic moiety, is subjected to the laboratory experiment to explore intrinsic decomposition mechanisms and dynamics of a single nitramine moiety. [5]. To date, thermally activated decomposition (which occurs on ground electronic state surface) of isolated DMNA has been investigated experimentally [6]theoretically [7]; however, thermal decomposition of DMNA-Al and DMNAcomplexes, which can be considered to be a simple analogue molecule of metalized nitramine energetic material, is hithertounexplored. Furthermore, a comparative study of the decomposition mechanisms oDMNA, DMNA-Al, and DMNA-Zn complexes at the ground electronic state will enable us to predict how presence of metal atom can change/alter the decomposition behavior of single nitramine moiety.

the exothermic heat is added to the total heat of reaction. This thermodynamic explanation of superior performance of mEM over molecular EM is well accepted in energetic material community. However, another part of this

er been seriously considered: the presence of metal (particle) surface can also alter the decomposition mechanisms and dynamics of molecular EMs. How does metal surface alter decomposition mechanisms and dynamics of molecular EMs? How does metal

ansfer energy to the molecular EMs during burning? These questions have mostly remained unanswered, thus far. In the quest of the answers to these questions, this article presents theoretically predicted decomposition pathways of a simple analogue nitramine molecule and its complex with aluminum atom.

trinitro-1,3,5-1,3,5,7-tetranitro-) and CL-20

2,4,6,8,10,12-hexaazaisowurtzitane) are well known nitramine

energetic materials. Their chemical structures are depicted in Figure 1. These molecules possess a number of N-NO2 (nitramine energetic moieties. Due to the complex structures of nitramine energetic molecules, often a structurally simple analogue molecule dimethylnitramine (DMNA, see Figure 1 for structure), containing only one N-NO2 energetic moiety, is subjected to the laboratory experiment to explore intrinsic decomposition mechanisms and dynamics of a single nitramine

thermally activated decomposition (which occurs on ground electronic state surface) of isolated DMNA has been investigated experimentally [6] and

however, thermal Al and DMNA-Zn

complexes, which can be considered to be a simple analogue molecule of metalized nitramine energetic material, is hitherto-unexplored. Furthermore, a comparative study of the decomposition mechanisms of isolated

Zn complexes at the ground electronic state will enable us to predict how presence of metal atom can change/alter the decomposition behavior of

Figure 1: Structure of RDX, HMX, DMNA

In the present work, ground electronic

state decomposition pathways of isolated DMNA and DMNA-Al complex have been explored utilizing second order MØller(MP2) perturbation theory with 6set. The decomposition pathways osystems are compared and contrasted. Isolated DMNA exhibits N-NO2 bond dissociation (rendering NO2 product) as the lowest energy pathway; however, both DMNADMNA-Al complexes exhibit a novel lowest energy decomposition pathway, which is to nitro-nitrite isomerization; however, the pathway finally yields NO as a final decomposition product.

Theoretical Procedure All geometry optimizations at the

ground state of DMNA, DMNADMNA-Zn are performed at the MP2/6level of theory using Gaussian 09.[8] MP2/631G(d) was previously found to be optimum level of theory to estimate activation barriers for DMNA system. [9] The transition state is confirmed by performing frequency calculation. The unstable normal mode of vibration atransition state shows an imaginary frequency and an intrinsic reaction coordinate (IRC) calculation starting from the relevant transition state is indicative of the related pathway. The basis set superposition error (BSSE) associated with the NO and NO2 elimination pathways are corrected by counterpoise method, as

Figure 1: Structure of RDX, HMX, CL20 and

In the present work, ground electronic state decomposition pathways of isolated

Al complex have been explored utilizing second order MØller-Plesset (MP2) perturbation theory with 6-31(d) basis set. The decomposition pathways of these two systems are compared and contrasted. Isolated

bond dissociation product) as the lowest energy

pathway; however, both DMNA-Zn and Al complexes exhibit a novel lowest

energy decomposition pathway, which is similar nitrite isomerization; however, the

pathway finally yields NO as a final

All geometry optimizations at the ground state of DMNA, DMNA-Al, and

Zn are performed at the MP2/6-31(d) theory using Gaussian 09.[8] MP2/6-

31G(d) was previously found to be optimum level of theory to estimate activation barriers for DMNA system. [9] The transition state is confirmed by performing frequency calculation. The unstable normal mode of vibration at the transition state shows an imaginary frequency and an intrinsic reaction coordinate (IRC) calculation starting from the relevant transition state is indicative of the related pathway. The basis set superposition error (BSSE) associated

elimination pathways are corrected by counterpoise method, as

implemented in Gasssian09. Transition states for both the NO and NO2 eliminations are also performed through potential energy scanning. A relaxed potential energy scan is performed using Z-matrix by changing the scan variable (respective bond distance).

Results and Discussion

As previous experimental investigations on the thermal decomposition of gas phase isolated DMNA [6] shows that DMNA can dissociate through two plausible mechanisms: NO2 elimination and nitro-nitrite isomerization followed by NO elimination, in the present work we have explored these two reaction channels for isolated DMNA, and DMNA-Al and DMNA-Zn complexes. The results are presented and contrasted below. DMNA: The reaction pathways with relative energies (in Kcal/mole) and structures of the optimized geometries at different critical points for DMNA are depicted in Figure 2. Relative energies are given in Table 1. Previously, decomposition pathways of DMNA in the ground electronic state was explored by Thomson and coworkers [7(a)] at MP2/6-311G(d,p) level of they and NO2 elimination was predicted to be the lowest energy dissociation channel as compared to nitro-nitrite isomerization, which is in concordance with the present results. The nature of the transition state (TS) associated with the nitro-nitrite isomerization dissociation channel of DMNA has been a subject of intense discussion for a long time. [9] Both tight and loose TSs on the ground electronic state surface of DMNA are localized at MP2 level. The activation energy barrier to the loose TS is calculated to be 69 kcal/mol, significantly lower in energy than that of tight TS, as predicted at the MP2/6-31G(d) level. NO2 elimination barrier, however, is calculated to be 30 kcal/mol, which is significantly lower in energy than that of both isomerization TSs.

Figure 2: Decomposition pathway of DMNA in

the ground electronic state, as revealed at the

MP2/6-31G(d) level of theory. The relevant

energies are in Kcal/mole.

DMNA-Al: Figure 3 shows decomposition pathway of DMAN-Al complex and the relative energies are given in Table 1, as predicted at MP2/6-31G(d) level of theory. Optimized geometry of DMNA-Al complex at different critical points are also depicted the figure. Figure 3 shows that the Al atom is located below the N-NO2 plane: this complexation finally results in a non planar geometry of the N-NO2 moiety in DMNA-Al complex (Opt1), which is in contrast to that of DMNA. The Al atom shows bonding with both oxygen atoms (of NO2 moiety) and perhaps with the (N1) nitrogen atom (of N-NO2 moiety). Furthermore, figure 3 clearly shows that decomposition of DMNA-Al follows a number of steps, which can finally render NO and NO2 eliminations. However, NO elimination channel shows the lowest energy pathway.

The first transition state (Ts1), in figure 3, suggests Al-O bond dissociation with an activation energy of 23 kcal/mole. This transition state is connected to an intermediate (Opt2) in the forward direction, which is again connected to a transition state (Ts2) for N-N bond dissociation. Following this transition state DMAN-Al complex can be converted to an isomeric form (Opt3). This two-step

0

20

40

60

80

100

En

erg

y(K

ca

l/m

ole

)

24

0

69

94

30

NO elimination

39

NO2 elimination

Opt1

Ts1

Ts2

Opt3

isomerization pathway of DMNA-Al complex is similar to a nitro-nitrite isomerization channel of isolated DMNA. Interestingly, the overall isomerization process is an exothermic process in the case of DMNA-Al complex and is endothermic process for isolated DMNA molecule. The energy barriers associated with NO elimination and the NO2 elimination with respect to Opt3 are estimated to be 13 and 43 kcal/mol, respectively, which undoubtedly indicates that the NO elimination following isomerization is the lowest energy pathway. Here note that NO2 elimination is predicted to be the lowest energy dissociation channel for isolated DMNA.

Figure 3: Decomposition pathway of DMNA

complex in ground electronic state, as revealed

at the MP2/6-31G(d) level of theory. The

relative energies are given in Kcal/mole.

Potential energy surface scan is

performed along the O1-Al1 bond to explore NO2 dissociation reaction coordinate starting from the Opt3 (see Figure 3) of DMNAcomplex. Scan results are depicted in Figure 4. This figure shows that the energy increases with the increase of O1-Al1 bond length; however, after certain O1-Al1 bond distance (~2.8 Å), energy starts decreasing due to shortening of the other O2-Al2 bond distance. Thus, in attempt to remove NO2 moiety, we have found that, due to strong interaction of Al and O atoms, NO

Al complex is nitrite isomerization channel of

isolated DMNA. Interestingly, the overall isomerization process is an exothermic process

Al complex and is endothermic process for isolated DMNA

barriers associated with elimination with

respect to Opt3 are estimated to be 13 and 43 kcal/mol, respectively, which undoubtedly indicates that the NO elimination following isomerization is the lowest energy pathway.

elimination is predicted to be the lowest energy dissociation channel for

Figure 3: Decomposition pathway of DMNA-Al


31G(d) level of theory. The


Potential energy surface scan is bond to explore

dissociation reaction coordinate starting from the Opt3 (see Figure 3) of DMNA-Al complex. Scan results are depicted in Figure 4.

his figure shows that the energy increases with Al1 bond length; however,

Al1 bond distance (~2.8 Å), energy starts decreasing due to shortening of the

Al2 bond distance. Thus, in attempt to e have found that, due to

strong interaction of Al and O atoms, NO2

elimination is unlikely to occur on the ground electronic state surface: this conclusion is, however, reflected clearly in Figure 3, in which NO elimination pathway is shown to be energetically the most favorable process as compared to the NO2 elimination.

Figure 4: Potential energy surface scan from

Opt3 along O1-Al1 bond elongation reaction

coordinate for DMNA-Al complex

DMNA-Zn: The DMNA-Zn complex shows geometry similar to that of DMNAwhere the metal Zn is located below the Nplane, rendering non-planar NFigure 5 shows decomposition pathway of DMAN-Zn complex and relevant relative energies are given in Table I, as predicted at MP2/6-31G(d) level of theory. As illustrated in Figure 5, first, DMNA-Zn complex can follow a transition state associated with Ndissociation from Opt1, which results in a very stable geometry dimethyl (µ2

complex. This geometry is stabilized by 44 kcal/mole because of formation of two stable Zn-O bonds. Thereafter, the complex undergoes Zn-O bond dissociation which leads to the nitronitrite isomerization followed by NO elimination. The isomerized produexothermic in nature compared to starting geometry both for DMNA-Al and DMNAcomplex. From the energy profile in Figure 5 it is evident that the NO elimination is endothermic for DMNA-Zn system.

elimination is unlikely to occur on the ground electronic state surface: this conclusion is, however, reflected clearly in Figure 3, in which NO elimination pathway is shown to be

cally the most favorable process as elimination.

Figure 4: Potential energy surface scan from

Al1 bond elongation reaction

Al complex.

Zn complex shows geometry similar to that of DMNA-Al complex, where the metal Zn is located below the N-NO2

planar N-NO2 moiety. Figure 5 shows decomposition pathway of

Zn complex and relevant relative in Table I, as predicted at

31G(d) level of theory. As illustrated in Zn complex can follow a

transition state associated with N-N bond dissociation from Opt1, which results in a very

2-nitro) amine zinc complex. This geometry is stabilized by 44 kcal/mole because of formation of two stable

O bonds. Thereafter, the complex undergoes O bond dissociation which leads to the nitro-

nitrite isomerization followed by NO elimination. The isomerized product is exothermic in nature compared to starting

Al and DMNA-Zn complex. From the energy profile in Figure 5 it is evident that the NO elimination is

Zn system.

Figure 5: Decomposition pathway of DMNA


at the MP2/6-31G(d) level of theory. The


Comparison: Energy profile diagrams for both DMNA-Al and DMNA-Zn are illustrated together in Figure 6 for comparison. Both complexes exhibit non-planar N-NOThe starting geometries with different bond angles and bond distances are given in the figure 6. The initial steps of decomposition of these two complexes, however, are different. The first transition state for DMNA-Al is associated with Al-O bond dissociation, whereas, that same for DMNA-Zn is N-N bond dissociation. In spite of an exhaustive search of Zn-O bond dissociation transition state, we have not found any Zntransition state for DMNA-Zn, similar to DMNA-Al. This difference leads to different geometries at Opt2 state for these two complexes. However, both complexes, in the end, are showing nitro-nitrite isomerization and NO elimination as the ultimate decomposition product but their pathways to reach the products are different. DMNA-Al exhibits Albond dissociation followed by Ndissociation which results in nitroisomerization product. On the other hand, presence of transition metal like Zn, Ndissociation becomes the first step, yielding dimethyl (µ2-nitro) amine zinc complex. Then it undergoes Zn-O bond dissociation which leads to the nitro- nitrite isomerized product and

Figure 5: Decomposition pathway of DMNA-Zn

mplex in ground electronic state, as revealed

31G(d) level of theory. The


Energy profile diagrams for both Zn are illustrated

together in Figure 6 for comparison. Both NO2 moieties.

The starting geometries with different bond angles and bond distances are given in the figure 6. The initial steps of decomposition of these two complexes, however, are different. The first

Al is associated with O bond dissociation, whereas, that same for

N bond dissociation. In spite of O bond dissociation

transition state, we have not found any Zn-O Zn, similar to

Al. This difference leads to different geometries at Opt2 state for these two complexes. However, both complexes, in the

nitrite isomerization and NO elimination as the ultimate decomposition product but their pathways to reach the ultimate

Al exhibits Al-O bond dissociation followed by N-N bond dissociation which results in nitro-nitrite isomerization product. On the other hand, presence of transition metal like Zn, N-N bond

st step, yielding nitro) amine zinc complex. Then it

O bond dissociation which leads nitrite isomerized product and

finally NO elimination occurs. The isomerization is exothermic in nature for DMNA-Al and DMNA-Zn comthe subsequent NO elimination is exothermic for DMNA-Al but endothermic for DMNAsystem. Table1: All the energies (Kcal/mole) are

calculated with respect to the energy of Opt1, at

the MP2/6-31G (d) level of theory. Ts1 for

DMNA is N-N bond dissociation and for DMNA

Zn but for DMNA-Al is Al-O bond dissociation

Whereas Ts2 is N-N bond dissociation for

DMNA-Al and Zn-O for DMNA

Critical

Points

DMNA

DMNA

Al

Opt1 0

Ts1 94

Opt2 24

Ts2 _

Opt3 _

NO

elimination

39

NO2

elimination

30

finally NO elimination occurs. The isomerization is exothermic in nature for

Zn complexes; however, the subsequent NO elimination is exothermic for

Al but endothermic for DMNA-Zn

All the energies (Kcal/mole) are

calculated with respect to the energy of Opt1, at

31G (d) level of theory. Ts1 for

N bond dissociation and for DMNA-

O bond dissociation

N bond dissociation for

O for DMNA-Zn complex.

DMNA-

Al

DMNA-

Zn

0 0

23 12

21 -44

33 -26

-20 -32

-7 28

23 40

Figure 6: Comparative study of the

decomposition mechanisms of DMNA

DMNA-Zn complexes in ground electronic state,

as revealed at the MP2/6-31G(d) level of theory.

The relative energies are given in Kcal/mole.

Conclusions

In the work presented in this article, the ground state decomposition reaction pathways are calculated for isolated DMNA, DMNAand DMNA-Zn complexes at the MP2/6level of theory. Theoretical results suggest that DMNA can follow N-NO2 bond dissociation as well as nitro-nitrite isomerization pathways; however, N-NO2 bond dissociation pathway is associated with the lowest activation energy barrier. DMNA-Al complex, on the other hand, shows significantly different multidecomposition pathway: first, Aldissociation, then N-N bond dissociation followed by an isomerization and finally NO elimination. Our calculations also show that NO elimination reaction pathway (followed by nitronitrite isomerization) of isolated DMNA is energetic unfavorable at the ground electronic state; but similar isomerization is favorable for DMNA-Al complex. Therefore, the decomposition behavior of nitramine en

Figure 6: Comparative study of the

decomposition mechanisms of DMNA-Al and

Zn complexes in ground electronic state,

31G(d) level of theory.

The relative energies are given in Kcal/mole.

In the work presented in this article, the ground state decomposition reaction pathways are calculated for isolated DMNA, DMNA-Al,

es at the MP2/6-31G(d) level of theory. Theoretical results suggest that

bond dissociation as nitrite isomerization pathways;

bond dissociation pathway is associated with the lowest activation energy

Al complex, on the other hand, shows significantly different multi-step decomposition pathway: first, Al-O bond

N bond dissociation followed by an isomerization and finally NO elimination. Our calculations also show that NO

imination reaction pathway (followed by nitro-nitrite isomerization) of isolated DMNA is energetic unfavorable at the ground electronic state; but similar isomerization is favorable for

Al complex. Therefore, the decomposition behavior of nitramine energetic

moiety is predicted to be altered completely in presence of Al atom. Furthermore, overall reaction of DMNA decomposition is found to be endothermic; however, that of DMNAcomplex shows overall exothermic reaction. Similar behavior can be anticipcomplex metalized nitramine energetic materials.

Now by changing the metal atom from a non-transition metal aluminum to a transition metal zinc what change can we anticipate in the decomposition of DMNA? DMNAalso shows isomerization and finally NO elimination in concordance with DMNAhowever, on the contrary with DMNADMNA-Zn complex shows first Ndissociation and then metal-O bond dissociation. Furthermore, the NO elimination process for DMNA-Zn system is found to bnature as compared to DMNApresent results show that only metal atoms can alter the decomposition of nitramine moieties, but also nature of metal atoms can have significant effect on the decomposition of nitramines. Our next goal is to experimentally investigate these changes using ultrafast spectroscopy.

References

[1] (a) Fried, L.E.; Manaa, M.R.; Pagoria,

P.F.; Simpson, R. L. Annu. Re

Res., 31 (2001) 291-321. (b) Tilton, T.M.; Gash, A.E.; Simpson, R.L.; Hrubesh, L.W.; Satcher, J. H.; Poco, J.FJ. Non Cryst. solids, 285 (2001) 338

[2] Ramaswamy, A. L. Expansion and Shock waves

131-137. [3] (a) Politzer, P.; Lane, P.; Grice, M. E.

Phys. Chem. A., 105(2001)(b) Swihart, M. T.; Catoire, L. Flame, 121 (2000), 210-222.

[4] Snyder, E.P; Seltz, H. J. Am. Chem.

Soc., 67 (1945) 683-685. [5] Guo, Y. Q ; Greenfield, M

Bhattacharya, A ; Bernstein, E. R. Chem. Phys. 127 (2007) 154301

moiety is predicted to be altered completely in presence of Al atom. Furthermore, overall reaction of DMNA decomposition is found to be endothermic; however, that of DMNA-Al complex shows overall exothermic reaction. Similar behavior can be anticipated for more complex metalized nitramine energetic

Now by changing the metal atom from a transition metal aluminum to a transition

metal zinc what change can we anticipate in the decomposition of DMNA? DMNA-Zn complex

on and finally NO elimination in concordance with DMNA-Al; however, on the contrary with DMNA-Al,

Zn complex shows first N-N bond O bond dissociation.

Furthermore, the NO elimination process for Zn system is found to be endothermic in

nature as compared to DMNA-Al system. Thus present results show that only metal atoms can alter the decomposition of nitramine moieties, but also nature of metal atoms can have significant effect on the decomposition of

t goal is to experimentally investigate these changes using ultrafast

(a) Fried, L.E.; Manaa, M.R.; Pagoria, Annu. ReV. Mater.

321. (b) Tilton, T.M.; Gash, A.E.; Simpson, R.L.;

W.; Satcher, J. H.; Poco, J.F.

285 (2001) 338-345 Combustion,

Expansion and Shock waves., 36(2000)

(a) Politzer, P.; Lane, P.; Grice, M. E. J.

105(2001) 7473-7480. (b) Swihart, M. T.; Catoire, L. Combust.

222. J. Am. Chem.

; Greenfield, M ; ; Bernstein, E. R. J.

127 (2007) 154301-10.

[6] (a) Stewart, P. H.; Jeffries, J. B.; Zellweger, J. M.; McMillen, D. F.; Golden, D. V. J. Phys. Chem., 93 (1989) 3557. (b) Nigenda, S. E.; McMillen, D. F.; Golden, D. M. J. Phys. Chem. , 93 (1989) 1124. (c) Lloyd, S. A.; Umstead, M. E.; Lin, M. C. J. Energ. Mater., 3 (1985) 187. (d) Flournoy, J. M. J.

Chem. Phys., 36 (1962) 1106. (e) Lazarou, Y. G.; Papagiannakopoulos, P. J. Phys. Chem. 94 (1990) 7114. (f) Lazarou, Y. G.; Papagiannakopoulos, P. Laser Chem. 13 (1993) 101.

[7] (a) Velardez, G. F.; Alavi, S.; Thompson, D. L. J. Chem. Phys. 123 (2005) 074313. (b) Sumpter, B. G.; Thompson, D. L. J. Chem. Phys. 86 (1987) 3301. (c) Sumpter, B. G.; Thompson, D. L. J. Chem. Phys. 88(1988) 6889.

[8] Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G. A.; Nakatsuji, H.; Caricato, M.; Li, X.; Hratchian, H. P.; Izmaylov, A. F.; Bloino, J.; Zheng, G.; Sonnenberg, J. L.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Montgomery, J. A., Jr.; Peralta, J. E.; Ogliaro, F.; Bearpark, M.; Heyd, J. J.; Brothers, E.; Kudin, K. N.; Staroverov, V. N.; Kobayashi, R.; Normand, J.; Raghavachari, K.; e Rendell, A.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Rega, N.; Millam, N. J.; Klene, M.; Knox, J. E.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Zakrzewski, V. G.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Dapprich, S.; Daniels, A. D.; Farkas, Ö.; Foresman, J. B.; Ortiz, J. V.; Cioslowski, J.; Fox, D. J.A. Gaussian 09,

Revision D.01,Inc,Wallingford CT,(2009)

[9] (a) Bhattacharya, A.; Guo, Y. Q.; Bernstein, E. R. J. Phys. Chem. A 113 (2009) 811. (b) Bhattacharya, A.; Guo, Y. Q.; Bernstein, E. R. Acc. Chem. Res. 43 (2010) 1476-85.

Investigation of spatial and temporal dynamics of vegetation

in semi-arid-ecosystems11

Sumithra Sankaran, Sabiha Sachdeva, Ashwin Viswanathan and Vishwesha

Guttal (PI)

Centre for Ecological Sciences, Indian Institute of Science, Bangalore, 560012

Abstract:Semi-arid ecosystems can exhibit striking vegetation patterns, which may have no characteristic size of patchiness. Elucidating local scale processes that generate these macroscopic patterns is of fundamental ecological importance as well as in being able to forecast the future dynamics of these highly vulnerable ecosystems. Recent studies have shown that, these patterns can be explained by local facilitative interactions with global competition for resources and have suggested that these patterns can be considered signatures of a system’s resilience. However, no alternative models that could potentially explain similar patterns have been investigated. In this work, we compare and contrast the predictions of a spatially explicit model of vegetation in which both, facilitation and competition, occur at local scales with that of the previous models. We characterize spatial patterns by using the distribution of patch sizes and power spectrum analyses. We show that power-law distribution of patch sizes can result from local competitive constraints combined with local facilitative interactions, that a global scale of competition is not necessary. Further we also find that power law patch size distributions are not necessarily indicative of high resilience but can be even observed in systems approaching regime shifts, depending on the scale of underlying interactions. We find no means of discerning

1Project number: STC/P‐315.

the scale of processes in a system based on results from power spectrum analyses. I. INTRODUCTION Semi-arid ecosystems support critical flora, fauna together with large livestock and human populations. Under threat due to increasing anthropogenic activities and climate change, they are prone to regime shifts, i.e., abrupt changes in their states and thus, potentially leading to desertification [1]. Vegetation of these ecosystems may exhibit striking self-organized patterns ranging from regular periodic patterns such as spots, labyrinths to irregular “scale-free patchiness”. Recent studies based on mathematical models and computer simulation studies argue that spatial patterns of vegetation in semi-arid ecosystems may offer signatures of resilience and its vulnerability to perturbations [2, 3. 4, 5]. In this context, understanding the local processes that generate large scale self-organized spatial patterns in semi-arid ecosystems and how these patterns are affected by stressors are questions of both basic and applied ecological significance. Many ecological interactions between species of the same trophic level (such as between different plants) can be broadly classified into two fundamental types‚ competition and facilitation. The relative scales over which interactions of each type are operational could potentially determine the nature of the spatial patterns observed in the landscape (Levin 1992).Some of the early work in this area, in late 1990’s and early 2000’s, developed various mathematicalmodels to explain these vegetation patterns. A key ecological assumption of these models was that of local positive feedback that helps


increased germination of seeds in the vicinity of other plants because of reduced evaporation and/or increased infiltration of water. However, competition sets in at local yet relatively longer spatial scales since plants draw water towards them from nearby regions. Two key studies[3, 4] published in 2007 then showed, using computer simulation models, that scale-free distribution of patchiness in vegetation can be explained by assuming local or near-neighbor facilitative interactions amongst plants in combination with a global scale of competition (motivated by studies showing that rainfall constrains global control of tree cover). Although the competition in resource limited regions such as semi-arid ecosystems can indeed extend to long distances, the assumption of a global scale constraint seems unrealistic. Moreover, analyzing a single metric of spatial pattern suchas patch-size distribution, could potentially be misleading; for instance, we know from the literature on percolation transitions in physics that patch sizes can exhibit scale-free features even in an entirely non-interacting system.Given the implications of such spatial patterns on the stability of these systems, we ask: (1) Can local processes (such as

competition for resources and facilitation among nearby plants) alone, in the absence of global constraints, explain scale-free patchiness of vegetation patterns found in semi-arid ecosystems?

(2) Can we discern scale of interactions by analyses of spatial patterns, for example those available from aerial imagery?

II. METHODS We analyse two cellular automata based spatially explicit stochastic models, differing in their interaction rules; specifically, the length scales over which competitive interactions operate. We compare various characterizations of spatial patterns resulting from these two models, henceforth called Kéfi’s and Lubeck’s models, along their respective phase diagrams.Both models assume a discrete space where each lattice cell represents a small spatial scale that could be occupied by an individual (plant). Each cell has an associated probability of switching from one state to another, such as plant or no-plant state. Competition and facilitation are captured by making these transition rates/probabilities conditional on the states of other cells in the lattice. Note that the two models are qualitatively similar in that the average density/vegetation cover as a function of birth ratecan show discontinuous or continuous transitions (for high and low death rates respectively) from vegetated to barren state. Kéfi’s model: In this model, meant to capture the biology of semi-arid ecosystems, there are three possible states for each cell: vegetated, empty and degraded. Local facilitation interaction is incorporated via a transition rate of degraded to empty-inhabitable cells which positively depends on the proportion of vegetated cells in the neighbourhood of the focal degraded cell. Competition is assumed to occur on a global scale and is incorporated by making the transition rate (denoted by ‘b’) of empty to vegetated cells depend inversely on the mean global vegetation density in the lattice. ‘b’ is also

constrained by other biological factors such as aridity of the landscape. Mortality rate (denoted by ‘m’) is the rate at which vegetated cells transition to occupied cells. It is the density independent parameter and captures biological factors such as extent of grazing in the landscape. (See [3] for detailed description of the model and its parameters). Lubeck’s model: This model, also known as tri-critical directed percolation model and based on stochastic process popularly known as contact process in the Mathematics literature, was developed in the Physics literature to investigate nonequilibrium phase transitions. Here, we employ the model with ecological interpretation of its rules. Each lattice cell can occur in two possible states corresponding to occupied and unoccupied by a plant. At each discrete time step, a randomly chosen vegetation site becomes empty with a probability (denoted by p), or an empty cell in the vicinity of that vegetated site undergoes a transition to a vegetated state with another probability. Positive local feedbacks are incorporated in this model through the increased probability (denoted by q) of transitioning from unoccupied to occupied cell as a consequence of the neighbourhood of the parent cell. Competition plays out through the reduction in available (empty) cells nearby the focal plant for propagation of its seeds as the overall density increases. In simple terms, this model assumes local facilitation and local competition to determine vegetation growth. (See [7] for detailed description of the model and its parameters).

We simulate these models in two dimensional discrete space of 1024 x 1024 grids. We use power spectrum analyses and calculate patch size distributions (using Maximum Likelihood Estimate methods) to characterize the resulting spatial patterns. III. RESULTS The vegetation patterns realised from Kefi’s model, across the parameter space (all values of ‘m’), show a power law distribution of patch sizes away from the transition (high values of ‘b’), which becomes increasingly truncated as the system approaches the critical point (or as ‘b’ is decreased) [as already shown by 3,4]. Vegetation patterns realized from Lubeck’s model also show power law patch size distributions, however, only when parameter values correspond to a discontinuous regime shift (q=0.91) and only when the system is very near the critical point (b=0.2951) (Fig. 1; Table 1). This shows that only local processes can also explain scale-free patch size distributions. Additionally it also challenges the reliability of patch size distributions as an indicator of approaching regime shifts.

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can also from only local scales of competition coupled with local positive feedbacks. Further, the scale of interactions in the system can also not discerned from power spectrum analyses of the spatial organization of the vegetation. We therefore conclude that it may not be easy to distinguish the origin or details of processes by analyzing spatial vegetation patterns with these metrics alone. We speculate that temporal dynamics of spatialpatterns may yet offer signatures of interaction scales in the system as the response to perturbations of a system with global competitive constraints will, in all likelihood, differ from that with solely local constraints. It is also highly probable that natural ecosystems, due to continuous disturbances, are seldom found in their steady states. We now plan to study the non-steady state behaviours of Kefi and Lubeck’s models to investigate the effect of noise on the spatial patterning of vegetation in ecosystems with varying scales of processes.

References

[1]Scheffer et al, 2001, Catastrophic regime shifts in ecosystems, Nature, Vol 413, 591-596.

[2] Rietkerk, et al, 2004, Self-organized patchiness and Catastrophic Shifts in Ecosystems, Science, Vol 305, 1926-1929.

[3] Kefi et al, 2007, Spatial vegetation patterns and imminent desertification in Mediterranean aris sytems, Nature, Vol 449, 213-218.

[4] Scanlon et al, 2007, Positive feedbacks promote power-law clustering of Kalahari vegetation, Nature, Vol 449, 209-213.

[5] Guttal & Jayaprakash, 2009, Spatial variance and spatial skewness: leading indicators of regime shifts in spatial ecological systems, Theoretical Ecology, 2: 1-11.

[6] Kefi, Guttal, et al, 2014, Early warning signals of ecological transitions: Methods for spatial patterns, Plos ONE., Vol 9: e92097.

[7] Lübeck 2006, Tricritical directed percolation, Journal of Statistical Physics, Vol 123,193-224.

ISTC/CSS/AP/312 Project Status Report

Development of Semiconductor Nanocrystals for Photovoltaics

Biswajit Bhattacharyya, Rekha Mahadevu and Anshu Pandey

Abstract: Photovoltaics require the

development of Semiconductors with

precisely tuned optical properties. Here

we report the development of materials

with very precise tuning of radiative

lifetimes and anomalous band gaps. The

synthesis of this novel class of materials

will motivate precise engineering of

material properties in order to optimize

device performance.

INTRODUCTION

Control over emission rates is of

fundamental importance to a number of

areas of research and technology. Long

spontaneous decay rates are extremely

desirable in photovoltaics. A number of

strategies to regulating the rates of

spontaneous decay already exist. Most of

these techniques –plasmonics, photonic

crystals and cavities, high dielectric media,

etc. – exclusively focus on tuning photon

density of states. Advances in colloidal

chemistry have enabled the synthesis of

semiconductor quantum dots with a wide

range of formulations, compositions and

morphologies. This flexibility naturally

provides several potential routes for

manipulation and control over the physical

properties of these materials. While

considerable attention has been devoted

towards utilizing QD structure to control

properties such as blinking, multiexciton

decay, luminescence efficiency, etc., the

deliberate use of structure to manipulate

radiative decay dynamics of single

excitons remains an untapped possibility.

In this article we demonstrate that dopants

may be used to regulate the density of

states available for excitonic

recombination. This provides a very

straightforward route to regulating the

radiative rates of carrier recombination in

QDs.

Copper is a known substitutional

impurity in II-VI QDs. When introduced

into II-VI materials such as ZnSe and

CdSe, Copper is known to exclusively

exist in a +2 oxidation state. The 3d9

configuration of Cu+2 substitutional

impurities has been demonstrated to

exhibit exchange with semiconductor host,

causing the emergence of diluted magnetic

semiconducting character in doped QDs.

Being Jahn-Teller ions, substitutional Cu+2

species are EPR (electron paramagnetic

resonance)–silent due to a combination of

spin-orbit and electron-lattice coupling. In

a II-VI lattice in particular, Cu+2 ions give

rise to an emission band that arises from

the radiative transition of a conduction

band electron into the copper d9 level.

From this level, the electron is believed to



relax nonradiatively into the valence band

hole. This process is highlighted in Figure

1b. The CB-Cu radiative transition (thin

arrow) presents a significantly weaker

transition dipole than the band edge

excitonic decay (thick arrow). Both

processes are however emissive and lead

to the emission of a photon along with the

de-excitation of the quantum dot.

Despite its inherent weakness, the CB-Cu

transition is behaviorally very different

from other radiative decay channels

available within a quantum dot (figure 1c).

Regulation of the amount of copper doped

into a semiconductor lattice provides a

direct handle on the available density of

states for the radiative relaxation of the

conduction band electron. A typical copper

doped QD shows both band edge as well

as dopant related emission(Fig 2a). While

band edge emission typically occurs with a

lifetime of the order of 10 ns, the copper

emission band is typically seen to decay in

about 500 ns(Fig 2b). The doping levels

for the Cu+2 impurity in II-VI QDs

typically correspond to the presence of

one-two dopant ions in QDs.

QDs were characterized through electron

microscopy, X-ray diffraction (XRD) and

Inductively Coupled Plasma-Optical

Emission spectroscopy (ICP-OES).

Studies of QD composition by ICP-OES,

Figure 1 a. Spontaneous emission involves the relaxation of an excited electron to its ground state. Material properties like density of states do not play a role in the process. b. A copper containing QD has two alternate pathways for radiative exciton recombination; either direct recombination with a band edge hole or else through the copper center. c. Enhancing the number of copper atoms in the QD increases the number of available channels for radiative recombination, speeding up radiative decay.

revealed that these QDs were composed of

a cadmium-copper-zinc selenide alloy. By

varying the molar ratio of copper acetate to

zinc and cadmium acetates during

synthesis, the copper inclusion may be

tuned from 0% to 35%. Figure 3a shows

the XRD patterns of a sample containing

copper, Zinc and Cadmium (ICP-OES

analysis on cations basis). While the XRD

patterns are indeed consistent with a Zinc

Blende material, the positions of the peaks

of signal do not correspond to either pure

ZnSe or to pure CdSe.

Rather, the peak positions are intermediate

to ZnSe and CdSe, and correspond to a

semiconductor alloy. A quantitative

statement may be made using Vegard’s

law.


Figure 2 a. Absorption and emission spectrum of a 1% doped Cu doped CdZnSe QD. b. Emission decay kinetics of the host and the copper band.

Copper and Zinc have similar ionic radii

and structure factors. This allows us to

write the lattice parameter of the alloy as a

weighted mean of Zinc Selenide and

Cadmium Selenide lattice parameters. This

leads us to expect a lattice parameter of

>0.5 nm for the alloy. Figure 3a also

presents a simulated pattern based on this

expectation. The excellent agreement

between the simulated pattern of the alloy

and the pattern that is observed in practice

as well as the absence of features

corresponding to potential impurity phases

such as CdSe, ZnSe or CuSe in our

patterns prove that these QDs are indeed

pure CdCuZnSe alloys. Figures 3b and 3c

show TEM images of QDs containing 1%

and 10% Cu (measured relative to the total

cation content). Consistent with the XRD

patterns, TEM images reveal a

homogenous distribution of particles, with

no evidence of extraneous phases. These

analyses thus suggest successful

substitution of a large fraction of the Cd

and Zn ions by copper.

We studied the electronic structure

of these materials under a probabilistic

model. In particular, the probability of

cluster formation is expected to determine

the electronic and optical properties of the

materials. The probability of formation of

such clusters may be evaluated in the case

of a completely random placement of

copper ions. In this situation, the

probability of occurrence of two nearest

neighbor copper ions is proportional to x2,

where x is the mole fraction of copper. In a

similar manner, the probability of

occurrence of a cluster of size s is

proportional to xs.

Of significant importance for our

discussion is the cluster connectivity or

degeneracy. Consider for instance the

simple case of a quasi-spherical

arrangement of copper ions. In such a

situation, if V is the volume of such a

cluster, then the radius of such a cluster is

related to V1/3. Of course, the number of

atoms in the cluster is related to V. i.e.

∝ . If we consider the addition of one

more atom into the cluster, the ways of

doing so are linked to the surface area of

the cluster, viz. V2/3 or /. When

dealing with mole fractions, one can

similarly relate N to x. This simple

argument illustrates two important points:


firstly, the cluster formation probability is

dependent on the connectivity of a cluster

with one lesser atom. Secondly, this also

illustrates that for a spherical arrangement,

a cluster is expected to have a sublinear

dependence of available cluster

morphologies on the cluster size.

In any practical situation the

occurrence of a spheroidal cluster is

extremely unlikely. Instead, a cluster with

much higher surface area is expected

simply because of a high degeneracy and

more available conformations. In this

situation, one can write ∝

, where s is the size of the cluster. The

probability expression for a cluster of size

s is given by . We are of course

interested in the total probability of

formation of any sized cluster. This is

given by = ∑ ~ ∫

.

This expression leads to a series of the

form. =

−

() + ⋯

. For

sufficiently small x, only the first term

contributes. It is further evaluated to be

~[() − 2]

~

.

Therefore, for small x, the total probability

of the occurrence of a cluster of any size is

related to

. Any property that is affected

by the formation of clusters of copper may

therefore be expected to have the

dependence +

on the mole

fraction. Here A and B are constants that

relate the variation of a particular property

to the probability of a cluster. This

expression is used to generate the fit to

experimental fluorescence data.

In most samples the decays are

adequately described by biexponentials. A

mean photon lifetime may be extracted by

considering the areas under various parts

of the PL decay curve. We therefore define

the photon lifetime by ∑ ∗

∑ =

∑

∑ . Here n

are the number of photons under a

particular component of the fit, while t is

the associated time constant. An increase

in the number of copper centers causes a

direct shortening of sample lifetimes. By

changing the copper inclusion levels from

1% to 31% the lifetimes from 600 ns to 1

ns.

In most systems the exciton

lifetime arises from the interplay of

radiative and nonradiative rates. Simply

increasing the nonradiative decay channels

in any system therefore also speeds up

exciton decay, although of course, this is

of little practical or scientific value. We

therefore turn to investigate the actual

causes of speeding up of the excitonic

decay rates in our systems.


Figure 3. a. Observed and Simulated XRD

patterns of CdCuZnSe QDs along with

standard patterns of CdSe (top) and ZnSe

(bottom). b. TEM micrograph of QDs

with 1% and c. 10 % Cu. Scale bars are 20

nm.

For example, consider the PL

kinetics of a sample with a >30% inclusion

of copper. The sample exhibits a quantum

yield of 17 %, and an exciton lifetime of

1.4 ns. The sub-unity quantum yield could

possibly originate from the presence of

non-emissive QDs in the ensemble or else

from the existence of a non-radiative decay

mechanism in all the QDs. In the former

event, the spontaneous lifetimes are simply

equal to the observed excitonic lifetimes.

Even in the latter case, with a 17%

quantum yield, the spontaneous emission

lifetime is estimated to be 8 ns. While the

actual spontaneous lifetime is expected to

lie somewhere between these two

estimates, even the upper bound is two

orders of magnitude lower than the

typically observed lifetimes in copper

containing QDs. We thus confirm that our

procedure represents the development of a

route to tune spontaneous emission

processes in QDs.

The procedure presented thus far

provides a straightforward recipe for

shortening spontaneous emission lifetimes

in QDs. We now turn to a method to

lengthen the same. While shortening

lifetimes relied on the propensity of

individual copper ions to offer alternate

decay channels, longer lifetimes may be

attained by regulating electron-copper

overlap. We start with ZnSe QDs where

copper ions are doped into the QD core.

An electron extracting type-II CdS shell is

now grown over the core. This leads to

reduced overlap between the conduction

band electron and the copper impurity,

causing increase in the copper lifetimes.

Figure 4a provides a schematic to describe

this effect. Aliquots were obtained during

the process of shell growth.

We analyzed sample composition

in each case and observed no variation in

Copper:Zinc ratio, indicating that no

copper escaped from the QDs during the

growth process. Figure 5b shows the

variation of the emission kinetics of these

materials as a function of energy of the

emission maximum. As anticipated from


the straightforward arguments relating to

electron-impurity overlap, we observe that

the emission slows down substantially.

Figure 4a. Schematic of the change in

impurity-electron ovelap in a core/shell

structure. b. Variation of exciton lifetimes

as a function of emission maximum. c. A

combination of these approaches allow us

to tune the emission maxima of these

materials from 1 ns to over 1 s.

Growth of CdS over the Zinc Selenide

core allows for tuning of the emission

lifetime from 650 ns to 1600 ns. We also

note that a shifting of the emission

maximum can by itself cause the slowing

down of emission. The estimated

magnitude of this effect is represented by a

dashed line in Figure 4b. The variation in

excitonic lifetimes clearly occurs both due

to a red shifting of the emission band as

well as the reduced carrier-impurity

overlap. The methods suggested here

nevertheless allow us to develop materials

that show a remarkable tuning of excitonic

lifetimes, from 1 ns to over 1600 ns

(Figure 4c).

CONCLUSIONS

The development of custom

chromophores and sensitizers is of

paramount importance for the next

generation of devices. Copper based

materials are of particular significance due

to the recent demonstration of feasibility of

these substances in charge transfer

processes. By employing dopants, we have

been able to significantly enhance the

concepts of idealized photovoltaic

chromophores that we have developed

during the course of this project. Future

studies will aim to apply these concepts in

practical devices containing ZnTe/CdS and

doped materials.

REFERENCES

(1) Bhattacharyya B. et. al. (Submitted)

(2) Mahadevu, R.; Yelameli, A. R.; Panigrahy, B.; Pandey, A., Controlling Light Absorption in Charge-Separating Core/Shell Semiconductor Nanocrystals. ACS Nano 2013, 7, 11055-11063.

Die Level 3D Packaging of Hybrid Systems N. P. Vamsi Krishna and Prosenjit Sen

Centre for Nano Science and Engineering, Indian Institute of Science, Bangalore

Abstract – The aim of this work1 is to develop and optimize design rules and processing technologies required for 3-D die level packaging of hybrid systems including MEMS components. In this paper we report the development of the dielectric filler process which works as the intermediate layer and isolates the various chips. We also report the initial results in using conductive printing to obtain electrical interconnects between layers.

I. INTRODUCTION

Transistor scaling has led to ever smaller chips with larger device density in integrated circuits. The reduction in size is reaching a saturation where further reduction of device dimensions is limited by fundamental problems. Further reduction in system weight and volume is possible through realizing that the existing technologies arrange dies in a horizontal fashion on either a PCB or a multichip module [1-2]. The semiconductor industry is seeking solutions in 2.5D-3D systems using vertical integration of devices using thru-silicon via (TSV) technology. The technology being developed is however expensive and available only to the big fabrication facilities. The technology that has been developed also does not accommodate MEMS devices with moving parts, which often have more severe restrictions in packaging specifications. In order to address the above issues we propose to develop processing technologies which will allow system developers to access 3D packaging through post processing on individual dies. The technology developed will also seek to address issues related to integration of MEMS devices

1 Project#: ISTC0336 (2014-2017)

with moving parts. An example of such a 3D packaged hybrid system is shown below.

Figure 1: A 3D integrated hybrid system

II. APPROACH AND RESULTS

The proposed technology for optimal vertical stacking will require the thinning of dies from standard 500-600 um thickness to less than 150 um thickness. This thinning process is expected to have no effect on the performance of electronic chips. For MEMS devices with structural layer less than 100 μm this thinning process will not change its performance. The die thinning can be carried out using back-grind and chemical mechanical polishing process. Wafer and die thinning process is a well-established process in industry and we will bypass this step by fabricating our dummy dies on thinned wafers.

Figure 2: Dielectric filler processing

The process starts with performing a die attach to a handle substrate. The handle substrate can be pre-patterned to provide interconnects as shown in Figure 3. This layer also contains the alignment marks for subsequent die attach steps.


Figure 3: Layer 1 mask for handle substrate

A 150 μm thick cover slip is used as a dummy die in the current process. The dummy die is also patterned using lift-off or etching to form the metal contact pads, devices and interconnects. The die is then aligned and bonded to the handle layer using UV curable epoxy EPO-TEK- UJ 1190. The aligned wafers are exposed to an UV lamp to harden the epoxy.

Figure 4: Wafer temperature under the UV lamp

Figure 4 shows the rise in temperature of the wafer during the epoxy exposure step. The temperature remains low at less than 70 °C.

Figure 5: Handle with bonded die.

After the bonding a spin coated dielectric is used to fill and planarize the substrates. For this project we are currently using SU-8 2150 [3]. The SU-8 is spin coated at 3000 rpm to coat and planarize the 150 μm. The SU-8 is soft baked at 65 °C for 7 mins and at 95 °C for 15 mins. The temperature was ramped to reduce stress issues. After the soft bake the wafers were allowed relax at room temperature for 15 mins.

Figure 6: Spin coated dielectric filler SU-8

The wafers were exposed using a mask aligner to 450 mJ/cm2. This was followed by post exposure bake at 65 °C and 95 °C for 7 and 15 mins respectively. The exposed wafers were developed for 16 mins. The initial runs faced issues with poor adhesion of SU-8 on the substrate. This lead to SU-8 crack and peel of due to stress in the SU-8 layer as seen in Figure 7. This issue was solved by improving the adhesion of SU-8 to the glass surface using a 2 min RIE O2 plasma treatment.

Figure 7: Crack and peel off in the SU-8 layer.

Interconnections between the handle wafer and the dummy die can be made in several ways. Currently we are considering jet printing of a silver paint and screen printing of conductive paste. Figure 8 shows a SU-8 planarized sample in which interconnects have been printed using

the jetting of silver ink using a Diamatix [4] printer. As is seen in the image the lines are rough and at some places discontinuous. We are in the process of improving the printing process parameters such as number of drops, number of runs and the surface condition of the SU-8.

Figure 8: Interconnects fabricated using jest printing of silver ink.

III. CONCLUSIONS AND FUTURE WORK

We have been able to optimize the dielectric filler process that is essential in die level 3D assembly. We are currently optimizing interconnect printing technology. Following optimization of the interconnect technology we will work on demonstrating a 3 layer stack of interconnected devices. We also intend to explore several other dielectric filler materials. Inspite of several attempts we were unable to obtain photo-pattern-able polyimide due to export control. We intend to try PDMS as another option.

REFERENCES

[1].http://en.wikipedia.org/wiki/System_in_pack

age

[2]. http://www.amkor.com/go/SiP

[3].http://www.microchem.com/pdf/SU-

82000DataSheet2100and2150Ver5.pdf

[4].http://www.fujifilmusa.com/products/industrial_inkjet_printheads/

Project #ISTC 0317

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Abstract- This paper describes the design and development of a

small volume liquid dispenser that is capable of real-time

measurement of the mass of liquid dispensed. These features of

the dispenser are enabled by appropriate design of the three

subsystems of the dispenser, namely, the droplet generation

sub-system, the droplet mass measurement sub-system and the

droplet deposition sub-system. The droplet generation system

comprises a micro-pipette with a silvered surface along with a

syringe. The droplet mass measurement system enables

measurement of the volume of liquid dispensed by measuring

the resulting shift in resonant frequency of the micro-pipette.

The droplet deposition system employs electro-hydrodynamic

pulling and comprises a 330V voltage supply. All the three

systems are designed and fabricated. The developed overall

system is demonstrated to generate, measure and dispense

liquid droplets of volume down to about 260pL.

I INTRODUCTION

Microdispensing is the technique of producing liquid

dosages in volumes less than one microlitre.

Microdispensing finds applications in drug delivery, inkjet

printing, cell biology research, microelectromechanical

systems (MEMS), lubrication in automobiles, precision

deposition of soldering paste in electronic circuits industry,

among others. The continuing miniaturization in almost all

technical areas drives a constant need to improve the

microdispensing technologies. Such requirements include

the need to dispense smaller amounts of adhesive, liquid, oil,

grease and a multitude of other media reliably, accurately,

both in dosage and placement, and within small time.

The precise positioning and quantity of the dispensed fluids

such as glue and reagents have a great influence on the

overall quality of the final product.

Dispensing can be broadly considered to be of two types,

namely, contact type dispensing and the non-contact type

dispensing. In contact dispensing, the drop forms at the exit

of a nozzle, and is deposited by contact while the drop is still

on the nozzle. In non-contact dispensing, the drop formed at

K. R. Sreejith and G. R Jayanth are with the Department of

Instrumentation and Applied Physics. e-mail:[email protected],

jayanth@ isu.iisc.ernet.in, Phone: 22933197.

the end of a nozzle is deposited on a target area that is

situated relatively far away from the nozzle. Thus, the drop

separates from the nozzle before it hits the target area.

Compared to non-contact type dispensing, contact type

dispensing has a few important disadvantages. Since the

nozzle which forms the droplet has to touch the target area,

the target could be damaged. This is especially true with

micro-fabricated parts, such as MEMS structures. Further,

since viscous liquid forms threads, reproducibility in the

amount of liquid dispensed liquid amounts is poor. Thus,

owing to increasing requirements in regards to cycle time

and accuracy in almost all areas of production, non-contact

dispensing has steadily gained in importance. The different

technologies available for dispensing liquids in a

non-contact manner include piezoelectric [1], direct liquid

displacement [2], and pyroelectrodynamic shooting [3].

Here too, an important requirement to deposit droplets in a

controlled manner is to measure the volume of the liquid

precisely. The technologies that have been employed to

measure mass or volume of liquid dispensed include optical

techniques [4] capacitive type measurement [5][6], and flow

sensor based measurement[7].

This paper reports the design and development of a

non-contact type droplet dispenser based on

electrohydrodynamic pulling. The dispenser is capable of

real time measurement of the mass of liquid dispensed and

thus, facilitates precise control of the mass of the liquid

deposited.

The rest of the paper is divided as follows: Section II

describes the principle and provides an overview of the

construction of the droplet dispenser. Section III describes

the development of each of the subsystems that constitute the

droplet dispenser. Section IV describes the results of liquid

dispensing.

II PRINCIPLE OF OPERATION

Fig. 1 is a schematic showing the construction of the

droplet dispenser. The experimental setup consists of a

syringe attached with a micropipette. The syringe can be

actuated to form a liquid drop at the tip of the micropipette.

An optical beam deflection system is employed to measure

the mass of the liquid droplet formed at the end of the syringe

A Small Volume Droplet Dispenser based on

Electrohydrodynamic Pulling

K. R. Sreejith, and G. R. Jayanth


Project #ISTC 0317

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in real-time. Finally, a high voltage power supply is used to

deposit liquid droplet on to the target.

Fig. 1: Schematics showing the construction of the droplet dispenser.

To generate a droplet, the piston of the syringe is actuated

using a suitable actuator, such as a piezo- or a thermal-

actuator, to produce a liquid drop of volume in the range

nanoliters-picoliters. In order to measure the mass of the

droplet formed, the micro-pipette is excited and the resulting

motion is measured by means of optical beam deflection.

The change in resonant frequency of the micropipette upon

formation of a droplet is employed to determine the mass of

the droplet. After the formation of a droplet of the desired

volume, a potential difference is applied between the droplet

and the target. The liquid droplet is attracted towards the

conductive target due to coulomb force of attraction and gets

deposited on the target. By controlling the duration of

application of potential difference, the quantity of liquid

dispensed can be controlled. While the target is expected to

be electrically conductive, droplets can also be deposited on

insulating targets by mounting them on a conductive

substrate. The design and development of the entire system

is described in Section III.

III DESIGN AND DEVELOPMENT OF THE DROPLET DISPENSER

Section II reveals that the overall droplet dispenser can be

divided into three sub systems, namely, the droplet

generation system, droplet mass measurement system and

the droplet deposition system. This section describes the

design and development of each of these subsystems.

A. Design and development of the droplet generation system

The main objective of the droplet generation system is to

generate liquid droplet of volumes in the range of nanoliters

to picolitres and subsequently facilitate their deposition. In

order to generate liquid droplets of such low volume, a

conventional syringe attached with micropipette is

employed. Since the volume of the liquid and the dynamic

characteristics of the micro-pipette are decided by its

geometry, the performance of the system depends on the

ability to adequately control the taper and tip diameter of the

micro-pipette during its fabrication process. Accordingly, a

micro-pipette puller was designed and developed in order to

achieve control over these parameters. In order to facilitate

deposition of the droplet by means of electrohydrodynamic

pulling, the surface of the micropipette was rendered

conductive by coating it with silver using Brashear’s

process. The optimization and subsequent evaluation of the

pipette puller is described in Section III A(a). The process

employed to coat the pipette is described in Section III A(b).

(a) Development of a pipette puller for fabrication of glass

micro-pipettes

(a)

Ball screw

Pulley

Timer belt

HolderChannel

(b) (c)

(d)

Fig. 2: (a) Schematic showing the construction of the micro-pipette puller.

(b)-(d) Photographs showing the construction of the pipette puller.

Fig. 2(a) is a schematic showing the principle behind the

operation of the pipette puller [8]. The pipette puller heats a

glass tube to a temperature hT over a length h such that

hT exceeds the softening temperature sT of glass. It is

subsequently pulled in opposite directions along the

longitudinal axis of the tube, viz., the X-axis, by means of

two motion stages. One stage is capable only of linear

motion along the X-axis and assists in pulling the pipette,

while the other is capable of three-axis in-plane motion, viz.,

linear motion along X- and Y-axes and angular motion about

Z-axis, and thereby assists also in bending the pipette. The

one-axis motion stage employs a guiding channel to restrict

the motion of the pipette holder along the axis of the channel,

viz., the X-axis. The three-axis stage and the one-axis stage

are connected by an inextensible wire such that, upon

actuation, the two stages can be retracted in opposite

directions at identical velocities. When the two motion

stages are pulled in opposite directions with velocity v, the

length p of the region that gets pulled is that whose

temperature exceeds sT . If the time constant of cooling of

the micropipette after emerging from the heated region is τc,

p is given by

Project #ISTC 0317

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2 ln hp h c

s

Tv

T (1)

To control the taper of the pipette, the pulling velocity v is

controlled to be sufficiently small so that p h , or

equivalently that the pulling velocity satisfies

2 ln hh c

s

Tv

T . The resulting dependence of taper on

h is obtained by assuming that the pipette possesses nearly

uniform cross-sectional area A within the region h . Assuming that the cross section preserves its shape during

pulling, i.e., the ratio of the inner diameter to the outer

diameter din/d= η remains constant, the profile d(x) of the

outer diameter of the pulled micropipette is given by

d(x) = d0 exp(− x/ ℓh) (2)

If 'v' is the velocity with which the pipette is being pulled

and’t’ is the time of pulling, x = vt. Thus, the taper of the

micropipette is controlled by controlling the length h

through which the glass capillary tube is heated. To achieve

active control over the length of capillary tube that is being

heated, the capillary tube is passed through a ceramic tube.

By adjusting the position of the ceramic tube relative to the

coils, the number of heater coil turns that are exposed to the

glass capillary tube can be controlled. To control the tip diameter, the glass tube is pulled for a

duration t1 after which the heater is switched off. Continued

pulling fractures the glass tube in the middle, thereby

creating two micropipettes of identical diameters d1 given by

d1 ≈ d0 exp(−vt1/ ℓh). Thus, the time t1 at which the heater is to

be switched off in order to achieve a tip diameter d1 is given

by

1)0 1( / ) ln( /ht v d d (3)

In the experiments, the glass capillary was heated for 135

seconds and then pulled. Experiments were carried out with

various values of h ranging from 4mm to 16 mm. The

profile of the pipettes obtained in each case is shown in Fig

3(a). Figure 3(b) plots ln [d(x)/d0] as a function of x, where x

is the distance from the base of the micro-pipette and d0 is the

original diameter of the glass capillary, and shows that the

dependence is linear in each case. Further, the slopes of the

four lines decrease monotonically with increased h , in

accordance with the theoretical result in Eq. (2). Figure 4(a)

is a micrograph showing tips of diameter d1 ranging from

500 μm down to 20 μm, obtained for values of t1 ranging

from 10s to 25s. The figure demonstrates that the tip

diameter reduces monotonically with increased t1. Figure

4(b) plots ln [d1/d0] as function of t1 and demonstrates a

linear relationship between the two in accordance with the

theoretical result obtained in Eq. (3).

Fig 3. (a) Micrograph showing the profiles of pipettes pulled with different

values of h and (b) plot of ln [d(x)/d0] as function of the distance x.

Fig 4. (a)Micrograph showing the tip diameters of pipettes obtained for

different values of t1. In all cases h was set to be 16 mm. (b) Plot of ln [d1/

d0] as function of the time t1.

(b) Silver coating of the fabricated micro-pipettes

After pulling a pipette of the required taper and tip

diameter, the pipette is coated with silver in order to render

its surface conductive. Silver coating is done by means of

Brashear’s process. Brashear’s process facilitates

deposition of silver almost uniformly on any glass substrate.

It is a chemical process in which large surface area can be

coated in less than 15 minutes. The steps involved in

Brashear's process involve first dissolving equal amounts of

silver nitrate and sodium hydroxide in water. Next, the two

solutions are mixed so that a black precipitate of silver oxide

forms. Third, ammonia is added until the precipitate

re-dissolves. Fourth, sugar is added and stirred until it

dissolves in this solution. Finally, the article to be coated is

placed in this solution while the solution is gently heated.

This results in deposition of silver on the article. Fig. 5 is a

micrograph showing a micropipette coated with silver.

500μm Fig 5. Micrograph showing a micropipette coated with silver

Project #ISTC 0317

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B. Droplet mass measurement system

In order to measure mass of the liquid droplet, the shift in

resonance frequency caused by the droplet is measured. The

undamped resonant frequency of the micropipette is a

function of its stiffness k and effective mass m and is given

by 0 k m . Thus, for a small change in mass m , the

corresponding change in resonant frequency is given by

0 m1

2 m

(4)

Thus, as m increases, the resonant frequency of the

micropipette gets reduced. If the density of the liquid is

known, it is possible to determine the volume of the droplet

formed at the tip of the micropipette. In the proposed system,

the resonant frequency is obtained by exciting the

micro-pipette and subsequently measuring its response by

means of optical beam deflection. The optical beam

deflection-based experimental setup for measurement of

droplet volume measurement consists of three components,

namely, a source of laser light, external actuator to vibrate

micropipette and an optical detector (Fig. 6).

A B

C D

I/V Converter/

Amplifier

Data Acquisition

and Processing

Focusing Optics

Laser Source

Syringe

Piezo Actuator

Silver Coated

Micropipette

Droplet

Quadrant

Photo

Detector

Fig 6. Schematic diagram of droplet volume measurement subsystem.

Light from a laser diode (wavelength 633 nm, power 3

mW) was focused using a converging lens on to the surface

of the micropipette. The micropipette attached with syringe

was arranged in such a way that the reflecting surface formed

an angle with the optical axis of the laser beam. The reflected

light from the surface of micropipette was collected on a

quadrant photo detector (QP100-6, First Sensor GmbH,

active diameter DD = 11 mm, responsivity 0.4 /A W )

kept at a distance of around 0.5mm from the reflecting

surface. It was ensured that the reflected spot was within the

active area of the detector. The photo-currents from the

quadrants of the detector were fed to the signal conditioning

circuitry, composed of I-V converters of gain 106 .The

voltages from each quadrant VA,VB, VC and VD were fed to

real-time controller (DS1103, dSPACE GmbH) operated at

20 kHz update rate. Within the real-time controller, the value

(VA+VB)-(VC+VD) was computed. The resulting signal is

proportional to the amplitude of angular oscillation of the

micropipette at the site of measurement. In order to excite the

micropipette, a piezo-buzzer was employed. In order to

obtain the resonant frequency of the micropipette, the

piezobuzzer was vibrated by providing a sinusoidal input

and the frequency of excitation was automatically swept

between two specified limits. Fig. 7(a) shows the photograph

of the measurement system. The normalized frequency

responses are plotted in Fig. 7(b) and clearly depict the

reduction in resonant frequency caused by generation of a

droplet.

Laser Source

Microscope

Piezo Actuator

Quadrant

Photo

Detector

Syringe With

Silver Coated

Micropipette

I/V

Converter/

Amplifier

1500 1750 2000 2250 2500 2750 3000-0.5

0

0.5

1

Am

pli

tud

e

Frequency (Hz)

Without Droplet

With 260pL Droplet

With 1696pL Droplet

(a)

(b)

Fig 7. (a) Photograph showing the measurement system (b) normalized

frequency responses of the micro-pipette carrying droplets of two different

volumes.

B. Droplet deposition system

After the formation of liquid droplet at the tip of the

syringe needle, the droplet is dispensed by applying a

voltage between the syringe needle and a target aluminium

plate. Upon application of the potential difference, the drop

becomes polarised and gets attracted to the plate owing to

coulomb force. If the force of attraction is larger than the

force due to surface tension of the liquid, it separates from

the pipette and gets deposited on the plate. This phenomenon

is known as electrohydrodynamic pulling. The shooting of

charged droplets depends on viscosity, the surface tension,

the density, the electrical permittivity and the electrical

conductivity of the fluid. In the experiments a 330 V, 100

mA dc power supply was constructed so that the distance

Project #ISTC 0317

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between the micro-pipette and the target could be maintained

around 100-150μm. The circuit diagram of the power supply

is shown in Fig 8(a). It employed a 230/230V transformer

followed by a bridge rectifier and a filter. The power supply

also comprised a short circuit protection circuit designed

using MOSFET (BUZ326). Fig 8(b) shows the photograph

of the power supply.

(a)

(b)

Fig. 8: (a) Circuit diagram of the power supply (b) photograph of the

fabricated circuit.

IV EXPERIMENTAL RESULTS

Syringe

Power Supply

Target

(Aluminium Plate)

Droplet

Silver Coated

Micropipette

Actuator

(Screw Driven

Stage)

(a)

(b)

Fig 14. Photograph of droplet generation system

The silver-coated micro-pipette was attached at the end of

a 1ml insulin syringe. In order to produce a droplet, a

screw-driven stage was attached with the piston of the

syringe. The schematic diagram of the setup is shown in Fig

9(a) and the photograph of the system is shown in Fig 9(b). The pitch of the screw used was 0.5 mm. The radius of the

syringe cavity was 0.36 cm. One full rotation of the bolt

displaced a liquid volume of around 20.3μL. In this setup,

with careful manual actuation, it was possible to produce

liquid droplets of volume around 260 picolitres consistently.

The corresponding radius of the droplet was around 40 um.

The micrographs showing the formation and shooting of a

liquid droplet of volume about 260 pL on application of a

high voltage is shown in Fig. 15.

500um

Fig. 15. Electro hydrodynamic pulling of glue of volume about 260pL

IV CONCLUSION

This paper reported the design and development of a small

volume droplet dispenser based on Electro Hydrodynamic

pulling. The dispenser is capable of real time measurement

of the mass of liquid dispensed. The developed liquid

dispenser consists of thee subsystems, namely, the droplet

generation subsystem, droplet mass measurement subsystem

and the non-contact type liquid droplet deposition

subsystem. The droplet deposition subsystem comprised a

silver coated micropipette attached to a syringe along with an

actuation system. The micropipette of desired characteristics

and dimensions was made using a pipette puller developed in

house. The silver coating on the micropipette was done by

Brashear’s process. The droplet mass measurement

subsystem works on the principle of shift in resonant

frequency of a micropipette due to formation of a liquid

droplet. The shift in resonant frequency was obtained by

employing an optical beam deflection measurement system.

The principle of the third subsystem, namely the non-contact

type liquid droplet deposition system is electro

hydrodynamic shooting, wherein an electric field is applied

between the droplet and the target to result in deposition of

the droplet. A 330 V power supply was designed and

developed to realize this subsystem. All of these developed

subsystems were experimentally evaluated. The droplet

generation subsystem was able to generate liquid droplets of

260pL consistently. The shift in resonant frequency of the

micropipette due to the formation of 260 pL and 1696 pL

liquid droplets were measured in real time with the liquid

droplet mass measurement subsystem. Finally, the droplets

Project #ISTC 0317

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of different volumes were successfully deposited on a target

by means of electrohydrodynamic pulling.

REFERENCES

1. A. S.Yang, C. H. Cheng and F. S. Hsu, “PZT actuator applied to a

femto-liter droplet ejector”, Journal of Mechanical Science and

Technology 21, pp. 621, (2007) .

2. B.Heij, C. Steinert, H. Sandmaier, R.Zengerle, “A tuneable and

highly-parallel picolitre-dispenser based on direct liquid displacement”,

Sensors and Actuators A 103 (2003).

3. P. Ferraro, S. Coppola, S. Grilli, M. Paturzo and V. Vespini,

“Dispensing nano–pico droplets and liquid patterning by

pyroelectrodynamic shooting”, Nature Nanotechnology 5(2010).

4. K.Thurow, T.Kruger , N. Stoll, “An optical approach for determination

of droplet volumes in nano dispensing”, Journal of automated methods

and management in Chemistry 2009(2009).

5. Ren, R.B Fair, M.G. Pollack, “Automated onchip droplet dispensing

with volume control by electro wetting actuation and capacitance

metering”, Sensors and Actuators B 98(2004).

6. Ernst, W. Streule, R. Zengerle, P Koltay, “Quantitative Volume

Determination of Dispensed Nanoliter droplets on the fly”, IEEE-

Transducers 2009.

7. C. Haber, M. Boillat, B.V. D Schoot, “Flow sensing driven nano

dispensing: The path to more reliable liquid handling operations”,

Application Note: American Laboratory October 2004.

8. R. Tamizhanban, K. R. Sreejith, and G. R. Jayanth, “An automated

pipette puller for fabrication of glass micropipettes”, 85, 055105 (5pp),

2014.

Synthesis Optical and Electrical Characterization of II-VI colloidal quantum dots for IR applications

Atul Prakash Abhalea , Abhijit Chatterjeeb, Naresh Babub, Arup Banerjeeb and K.S.R. Koteswara Raoa,*

a Department of Physics, Indian Institute of Science, Bangalore- 560012 b SAC (ISRO), Sensors Development Area, Ahmedabad -15

Abstract: colloidal quantum dots (CQDs) can provide tuneable bandgap, cost effective and relatively low processing temperature materials for photo-detection. In this paper we have discussed the synthesis of HgCdTe and PbS colloidal quantum dots and their application as IR photodetectors. I. Introduction

Colloidal quantum dots are potential

candidates for the light detection applications,1 due to its exceptional characteristics such as tuneable bandgap, comparatively easy and cost effective synthesis and tuneable properties by post synthesis processes.1,2 In the CQD materials bandgaps are tuneable with the values greater than that of the bulk ones and hence only low band gap compound semiconductors can match the spectral range suitable for Infrared photodetection applications. Among several low band gap compound semi-conductors HgCdTe (MCT), PbS are suitable materials with higher exciton Bohr radius (~18 nm), which allows bandgap tuneablity and low binding energy due to its high dielectric constant (~17.2), which helps relatively easy exciton dissociation and charge extraction. Recent work on the PbS-CQD based schottky barrier photodiodes highlights the importance of the CQD based photodetection.3,4

II. Experimental

Monodispersed colloidal quantum dots

of MCT and lead sulphide are synthesized with the method reported by the MC Weidman et. al.,5 with excitonic absorption peaks from ~1300-1600 nm. This method provides narrow size distribution of the quantum dots, which are essential for the better carrier transport in the

*Email Address: [email protected]

solid CQD films, which increases the hopping probability 6.

Fig. 1 The absorption spectrum of the PbS CQD solid films.

Fig. 1 shows a typical absorption

spectrum of solid CQD films, where quantum dots are grown at 120 ºC for different time periods varying from 5 – 60 minutes. The presence of clear first and second excitonic peaks in all the spectra confirms the narrow size distribution, which is also visible from the TEM image in Fig. 2.

Fig. 2. Transmission electron micrograph of the PbS quantum dots.

In the device point of view, schottky barrier devices are fabricated with Aluminium and Indium Tin Oxide (ITO) contacts, where ITO works as Ohmic contact. Similarly PbS-


CQD/Si <100> othe schois utilisearray odependawhich geometrare depo~50 nm patterneWhen pcontactsthe croshave efcompletnumber

Fig. 3 C

in the dshown iunder thin the degradatschottkyof the Ato avoidCQD/p-observedcharacteshown inSi and Pfrom thewith the~1100nm

heterojuncoriented singlottky devicesed (Fig. 3) tf size 300

ant photoresis analogou

ry, patternedosited on ththick CQD d Al is deppotential is s only one Css junction offective cone 2N pixelsof contacts.

Crossbar cont

I-V characte

dark and undin Fig. 4. Ihe light photreverse biastion is obse

y diodes, proAl, indicates a

d degradatioSi heterojud under theristics of n Fig. 5, whePbS-CQD. Te CQD film

e Si filter whm were passe

tions are fle crystal silis particular dthis can be × 300 μm

sponse can us to the d transparenhe glass subsfilm is spin

posited as shapplied to

CQD area, whof the two linntribution. I can be ha

tact CQD pix

eristics of theder 40 W tuIt is clear frtocurrent dens. Over a perved in thobably due ta need for a on.7 In the unctions, phe light illu

these heterere a contribTo observe t

m, devices where only waved through.

fabricated onicon wafers. device geomused as a p

m and positbe collec

ROIC. In nt ITO contstrate on whcoated and t

hown in Figany two cr

hich appearsnear arrays In this wayandled with

xel array.

e schottky diungsten lampfrom the fignsity dominperiod of t

he Al/PbS-Cto the oxidaprotective lacase of P

photocurrentumination. rojunctions bution from bthe contribu

were illuminavelengths ab

n a For etry

pixel tion

cted, this acts hich then g. 3. ross s on will y a 2N

iode p is ure, ates time

CQD tion ayer PbS-

is I-V are

both tion ated

bove

Figsch

FigPb

III

wiCuwhthiarefileRCshaspadevsigsigdet

g. 4 I-V chahottky diode

g. 5 Dark abS-CQD/Si h

I. LBIC PbS-C

ith the remourrent (RC-Lhich are presis technique ea on the deed is presentC-LBIC imaaped deviceatial distancvice and co

gnal. In Figgnal is plottetails will be

aracteristics .

and light I-Vheterojunction

C Imaging

QD / p-Si ote contact LLBIC) technsented else-wgives informevice, wheret. Fig. 6(a) shage of the Pe. Here, theces parallel lour represe

g. 6(b) the ed, which is bdiscussed du

of the PbS-

V Charactern under reve

devices areLight Beamnique, the dwhere. Imag

mation about e the built inhows a 3-dim

PbS-CQD/p-Se axes repreto the plannts the magcross-sectio

bipolar in nauring the pres

-CQD/Al

ristics of erse bias.

e imaged m Induced details of ging with an active

n electric mensional Si square esent the ne of the gnitude of on of the ature. The sentation.

Fig. 6 a). 3D plot of RC-LBIC image. b) a cross-section of 3D signal for PbS/Si heterostructure.

IV. Conclusion We have synthesized monodispersed colloidal MCT and PbS quantum dots with chemical bath deposition technique with variable excitonic absorption peaks. Schottky, as well as heterojunction devices are fabricate with solid film of CQD on Al and Silicon by spin coating technique. In order to study the photodetection capabilities of the devices I-V characteristics under the dark and light conditions are studied, where a good photoresponse is observed for both schottky as well as heterojunction devices.

References

1 G. Konstantatos and E.H. Sargent, Infrared Phys. Technol. 54, 278 (2011). 2 E. Sargent, Nat. Nanotechnol. 7, 349 (2012). 3 J.P. Clifford, K.W. Johnston, L. Levina, and E.H. Sargent, Appl. Phys. Lett. 91, 253117 (2007). 4 E. Heves, C. Ozturk, V. Ozguz, and Y. Gurbuz, Electron Device Lett. 34, 662 (2013).

5 M. Weidman, M. Beck, and R. Hoffman, ACS Nano (2014). 6 P. Guyot-Sionnest, J. Phys. Chem. Lett. 3, 1169 (2012). 7 H. Fu and S.-W. Tsang, Nanoscale 4, 2187 (2012).

Movement strategies for commensalism: coexistence of meso-carnivores in human-dominated landscapes

Maria Thaker1 and AbiTamimVanak2

1Centre for Ecological Sciences, Indian Institute of Science

1Ashoka Trust for Research in Ecology and the Environment Abstract - In the increasingly human-dominated landscapes of India, the survival and long-term persistence of mammalian carnivores is a key conservation challenge. This is especially the case for species that survive in the semi-arid savanna biomes of central India. The overall aim of this study is to determine the ecological parameters and behavioural strategies that enable existence of the golden jackal,Indian fox, and jungle cat in human-dominated systems. Here, we present the first part of the study, which was to generate detailed maps of the study areasin Maharastrausing supervised and rule-based classification ofLISS IV imagery. Comparison of these methods indicates that object-based methods have higher accuracy and is more applicable th 1 an supervised classification methods for delineating habitat patches in grassland areas. On-ground surveys of these areas confirm the presence of focal species (Golden Jackal, Indian fox and Jungle cat). During the next phase of the project, we will obtain fine-scaled movement data of the focal species using advanced GPS/UHF telemetry. These data will be superimposed onto the high-resolution maps generated thus far to determine patterns of resource use and movement ofmeso-carnivores across the landscape mosaic. I. INTRODUCTION

Tropical landscapes worldwide are 1Project code: IST/BES/MT/329

being rapidly transformed into a complex matrix of human-dominated multiple use land cover types with differing intensities of use (Gibbs et al. 2010). With the second largest human population in only 2.4% of the world’s land area, natural habitats in India have undergone some of the greatest rates of transformation (FSI 2011).The consequence for organisms that once inhabited large, unbroken tracts of native habitats has been either population decline to local extinction on the one hand, or tenuous survival on the other (Karanth et al. 2010). The majority of research has focused on species in decline, with particular attention given to the effects of habitat fragmentation and general loss of biodiversity, yet there are a range of species that seem to be effective commensals in human-dominated landscapes.

An understanding of how willing species are to traverse or even utilize potentially inhospitable and risky habitats is critical to understanding coexistence (Russell et al. 2003). Adaptations of animals to such risk in the landscape can be measured in terms of timing (whether at particular times of day or night), residence (how long they stay in landscape elements) and speed of movement (Graham et al. 2009). Thus, understanding how animals make decisions regarding movement and incorporating behavioral decisions into population models is an important step for predicting the consequences of fragmentation for population


persistence (Russell et al. 2003; Fahrig 2007; Vanak et al. 2010). Resource selection by animals is a hierarchical process of behavioral responses to particular environmental characteristics (Horne et al. 2008). Different environmental factors and scales may influence the movement of individuals within a landscape, and therefore animal-landscape relationships should be examined across a range of scales (Anderson et al. 2005; Boyce 2006). This is particularly important when examining resource selection dynamics of species inhabiting fragmented or human-dominated landscapes (Anderson et al. 2005).

In India much wildlife exists in human-dominated landscapes across a variety of biomes. Yet, there are very few detailed studies on the status, ecology and behavioural strategies of species living in these landscapes (Akhtar et al. 2004; Athreya et al. 2007) or even of the semi-arid tropics in particular (Vanak and Gompper 2010). The first step and a key challenge in understanding how animals utilize human-dominated areas isin distinguishing human-modified from natural habitats using remotely sensed data. Here, we report the results of our analyses of high-resolution imageries using supervised and rule-based classification to effectively delineate habitat patches at a fine resolution.We focus on the dry savanna grassland habitat, because the spectral heterogeneity of these landscapes are particularly difficult to classify and because small carnivores that utilize these areas are poorly studied. These detailed maps of native and human-modified habitat patches have been surveyed on ground, and will later be superimposed with the movement information of Indian foxes (Vulpesbengalensis), golden jackals (Canisaureus) and jungle cats

(Felischaus). In doing so, we will have fine-scaled data on the habitat requirements and levels of tolerances for human modification for each of these species. II- METHODS A. Study area

The dry savannah grasslands of peninsular India are unique habitats that support a vast proportion of India’s agro-pastoralist community. These ecosystems have also been subject to land-use change (e.g. irrigation or conversion to agriculture or tree-plantations) resulting in major decrease in extent. We focus our study on the areas near the villages of Takali-Dhokeshwar and Kedagaon-Kurkumbhof Maharashtra. The landscape there is typical of much of the central Indian plateau, with a matrix of savannah grasslands, agriculture land, and settlements. B. Classification of remote sensing data

Landcover data were obtained from high-resolution satellite imageries (IRS LISS IV, dated 18 January 2013) were acquired for the two study areas. After atmospheric correction, we conducted a supervised classification using the Maximum Likelihood Classifier in Erdas Imagine, using ground truthed data from field survey as training data for the classifier. We mapped the following and cover classes: grasslands, agriculture, fallow, water and builtup/bare (Fig. 1a).

As a comparison, we also conducted an object-based classification of the same LISS IV images in eCognition developer 8.7, based on a rule-set (Fig. 1b). All images were subjected to multiresolution segmentation in order to cluster spectrally homogenous entities into one object (Hofmann and

Puzicha, 1998). We classified built-up areas and bare rocky areas using brightness and green band values to avoid spectral mixing with barren grasslands. To identify agricultural fields with and without standing crops, we incorporated two vegetation indices, Normalized Differential Vegetation Index (NDVI) and Normalized Differential Vegetation Structural Index (NDVSI) within eCognition as object features. NDVI relates directly to vegetation health, while NDVSI shows vegetation structure (Yang, Willis and Mueller, 2008)

One of the biggest hindrances in grassland mapping is the difficulty in distinguishing between grasslands and fallow agricultural fields. If a field is left uncultivated for period of time, we find little to no spectral difference between these fallow fields and natural grasslands. We thus utilized a combination of rules to separate the fallow land from grasslands; for example, the mean value of the IR band in summer images for grassland “objects” was higher compared to fallow fields “objects”. We set the threshold for object classification accordingly.

We further classified grasslands

into different classes using the GLCM (Grey Level Co-occurrence Matrix) texture feature within eCognition(similar to Guo et al. 2004). The GLCM is produced by putting each pixel and its neighbour within an object into a 256x 256 matrix, and the number of pair occurrences is final value of the GLCM feature. Since the study area included a wide range of spectral classes, GLCM dissimilarity and homogeneity were used to extract grasslands. Finally, patches that were left unclassified because of the heterogeneity of the landscape were classified using relations to

neighbouring objects properties. For example, smaller patches of land between agricultural fields were assigned as agriculture and not grassland. And grasslands with undulating topography were included in the grassland class if surrounded by other grassland 'objects'.

Figure 1.(a) Pixel- and (b) Object-based classification of the Takali-Dhokeshwar (Maharastra) area.

C. Ground-truthing and animal presence surveys The land cover maps that we generated were exported as shapefiles and largest contiguous 'core' areas within our study area were extracted using Patch Analyst extension in ArcGIS. The core areas were divided into 13km2 grids and the grids above 60% grassland cover were selected for survey (Fig. 2). Extensive field-work, which included transects and random sampling, was conducted to survey presence of grassland specific species, especially golden jackals, Indian foxes, and

Legend

Grassland

Agriculture

Fallow land

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Builtup area/Bare(b) Object based classification

(a) Pixel based classification

Jungle cats. We searched grassland and the surrounding areas along existing trails and tracks for species-specific signs, such as carnivore scat, track impressions, herbivore pellets, and opportunistic sightings. Local information was used to confirm presence of endangered and grassland specialist species.

Figure 2. Map of the TakaliDhokeshwarstudy area, depicting the grids (white) and survey tracksfor animal presence (green).Image courtesy of Google earth.

III- CONCLUSIONS AND FUTURE WORK Thus far, we have generated detailed maps of our study area using both maximum likelihood supervised classification and object based classification methods. As expected, supervised classification of complex heterogeneous landscapes, such as grassland habitats, is limited. We find that this classification method produces a ‘salt and pepper effect’ which greatly contributes to the inaccuracy of the classification (see also de Jong et al. 2001; Gao and Mas 2008; Van de Voorde et al. 2004). This problem arises because supervised

classification assigns classes to pixels ignoring their related spatial and contextual information (Weih, Jr and Riggan, Jr), which results in incorrectly classifying a map as overly fragmented. Instead, the object based classification created a grassland map, which incorporated the heterogeneity of the class. For example, hilly grasslands, ridges, scrub forest, and grass covered plains were all classified as a single class: grassland. This method therefore the land cover class as a habitat rather than a heterogeneous milieu. Not surprisingly, comparison between these methods shows that object based classification had a higher overall accuracy (81% accuracy) than supervised classification (64%) for detailed mapping of semi-arid grassland areas.

On ground surveys of both study areas confirm the presence of the target study species. During the next phase of the project, we will capture and collar the target animals (n = 10 per species) using GPS/UHF telemetry devices. The detailed movement information we then obtain from these tracked animals will inform us of the behavioural strategies that permit use of the habitat matrix. ACKNOWLEDGEMENTS We thank AmeyaGode for help in the analysis of remote sensing data, and AbhijeetKulkarniand HarshalBhosalefor help in the field surveys. REFERENCES [1] Akhtar, N., H. Singh Bargali, & N.

Chauhan. (2004). Sloth bear habitat use in disturbed and unprotected areas of Madhya Pradesh, India. Ursus, 15, 203-211.

[2] Anderson, P., M. G. Turner, J. D. Forester, J. Zhu, M. S. Boyce, H. Beyer, & L. Stowell. (2005). Scale-

dependent summer resource selection by reintroduced elk in Wisconsin, USA. Journal of Wildlife Management, 69, 298-310.

[3] Athreya, V. R., S. S. Thakur, S. Chaudhuri, & A. V. Belsare. (2007). Leopards in human-dominated areas: a spillover from sustained translocations into nearby forests? Journal of the Bombay Natural History Society, 104, 45-50.

[4] Boyce, M. S. (2006). Scale for resource selection functions. Diversity and Distributions, 12, 269-276.

[5] de Jong, S. M., Hornstra, T., & Maas, H.-G. (2001). An integrated spatial and spectral approach to the classification of Mediterranean land cover types: the SSC method. International Journal of Applied Earth Observation and Geoinformation, 3(2), 176-183.

[6] Fahrig, L. (2003). Effects of habitat fragmentation on biodiversity. Annual review of ecology, evolution, and systematics, 487-515.

[7] Gao, Y., & Mas, J. F. (2008). A Comparison of the Performance of Pixel Based and Object Based Classifications over Images with Various Spatial Resolutions. Online journal of earth sciences, 2(1), 27-35.

[8] Gibbs, H., A. Ruesch, F. Achard, M. Clayton, P. Holmgren, N. Ramankutty, & J. Foley. (2010). Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proceedings of the National Academy of Sciences, 107, 16732-16737.

[9] Graham, M. D., I. DouglasHamilton, W. M. Adams, & P. C. Lee. (2009). The movement of African elephants in a humandominated landuse mosaic. Animal Conservation, 12, 445-455.

[10] Guo, X., et al. (2004). Measuring spatial and vertical heterogeneity of grasslands using remote sensing techniques. Journal of Environmental Informatics, 3(1), 24-32.

[11] Hofmann, T., & Puzicha, J. (1998). Statistical models for co-occurrence data.Technical report, Artifical Intelligence Laboratory Memo 1625, M.I.T., 1998.

[12] Horne, J. S., E. O. Garton, & J. L. Rachlow. (2008). A synoptic model of animal space use: Simultaneous estimation of home range, habitat selection, and inter/intra-specific relationships. Ecological Modelling, 214, 338-348.

[13] Karanth, K. K., J. D. Nichols, K. U. Karanth, J. E. Hines, & N. L. Christensen. (2010). The shrinking ark: patterns of large mammal extinctions in India.Proceedings of the Royal Society London-B. 277: 1971-1979.

[14] Russell, R. E., R. K. Swihart, & Z. Feng. (2003). Population consequences of movement decisions in a patchy landscape. Oikos, 103, 142-152.

[15] Van de Voorde, T., De Genst, W., Canters, F., Stephenne, N., Wolff, E., & Binard, M. (2004). Extraction of land use/land cover related information from very high resolution data in urban and suburban areas. Remote Sensing in Transition, 237-244.

[16] Vanak, A. T., & M. E. Gompper. (2010). Multi-scale resource selection and spatial ecology of the Indian fox in a human-dominated dry grassland ecosystem. Journal of Zoology, 281, 140-148.

[17] Weih Jr, R. C., & Riggan Jr, N. D. Object-Based Classification vs. Pixel-Based Classification: Comparative Importance of Multi-Resolution Imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38, 4.

[18] Yang, Z., et al. (2008). Impact of band-ratio enhanced AWIFS image to crop classification accuracy. Proc. Pecora 17 http://www. asprs.org/a/publications/proceedings/pecora17/0041. pdf.

ISTC0321

Development and Performance Evaluation of a PCM coupled Heat PipeG. M. Karthik, Girish Kini, A. Ambirajan and P. Kumar

Abstract - Designing a suitable thermal control system forpresent day electronics is becoming more and morecomplex with the invention of low mass(compact) highdissipation electronic components. Heat pipe is aninnovative device used for removal of heat from a remotesource to far-away heat sink. Thermal control design of aspacecraft requires transportation of heat from theelectronic packages which are housed inside, to a thermalradiator which is located outside. Heat from thermalradiator is eventually dissipated into the space by radiation.Even if the spacecraft heat loads are cyclic in nature,thermal radiators are being designed to meet peak loadrequirements. To avoid oversizing of a thermal radiator, aheat storage unit may be employed to store the peak loadand release it over a longer time. The present projectexplores the possibility of integrating a Phase ChangeMaterial (PCM) module to a conventional heat pipe to takecare of periodic heat load transience. The PCM acts as athermal capacitor storing heat during transient peaks in heatloads and releasing it during the off peak load conditions tothe radiator.

NOMENCALTURE

A area (m2)Dh hydraulic diameter (m)hfg latent heat of vaporization (J/kg)L length (m)ṁ mass flow rate (kg/s)N number of groovesp pressure (Pa)Q heat rate (W)rc capillary radius (m)u velocity (m/s)w groove width (m)x coordinate along the heat pipe (m)

Greek Symbolssurface tension coefficient (N/m)density (kg/m3)

μ viscosity (kg/m/s)

Subscriptsa adiabatic sectionc condensere evaporatorin inputl liquid

v vapor

I. INTRODUCTION

Axially grooved Aluminium Ammonia Heat pipes are usedin spacecraft applications to transport heat from electronicpackages (heat source) to heat radiator (heat sink). Heatpipes are generally embedded in the honeycomb panels onwhich electronic packages are mounted on the inside andheat is radiated to space from the outside. A typical heatpipe consists of a hermetically sealed envelope containingwick structure filled with saturated working fluid in liquidphase and remaining space (core) filled with vapour. Heattransferred to the heat pipe (at evaporator) is removed byevaporation of the working fluid in the wick, therebyincreasing the vapor pressure in the core causing flow ofvapor towards the cooler end, where it condenses (atcondenser) and heat is released. The condensate returns tothe evaporator due to pressure gradient along the length ofthe wick caused by surface tension forces (capillarypumping).

In thermal management of electronics, Phase ChangeMaterials (PCMs) are extensively used to maintaintemperatures during transient heating. PCMs are highlyeffective heat storage materials that undergo a phasechange at a particular temperature or a range oftemperatures. These materials generally have a high latentheat of fusion allowing a small amount of PCM to meet theheat storage needs of a majority of applications.

A key challenge in the design of a thermal control systemof a spacecraft, while ensuring its reliability andeffectiveness, lies in accommodating high heat dissipationsand varying orbital heat loads. When the thermal load is notsteady, as in the case of remote sensing satellites, thethermal system adopted demands similar heat removalcapacity as that at constant thermal load. The reason forthis is that the number of heat pipes and radiator arearequirement are based on the peak thermal load and not theorbital average thermal load. One method of optimallymanaging the issue of varying heat load withoutoverdesigning the thermal system is by the use of a suitablePhase Change Material in conjunction with heat pipe forefficient and stable heat transfer. This concept would notonly optimize the thermal design, it would also result inlesser number of heat pipes and reduced radiator area. This


in turn would result in a significant reduction of overallmass of the spacecraft thermal control system.

II. MATHEMATICAL MODEL OF HEAT PIPE

The heat transport capability (measured in Watt-meter) isone of the key factors in the thermal performance of heatpipes. The mathematical model is developed to estimateheat transport capability of a heat pipe based on capillarylimitation. Heat pipe with Aluminium alloy as envelopematerial, extruded axial grooves as wick and ultrapureanhydrous ammonia as working fluid is considered for thisstudy. The variation of the radius of curvature of themeniscus at the liquid-vapor interface is used to calculatethe maximum heat transport capability of the heat pipe.

The radius of the meniscus which is formed at the liquid-vapor interface is related to the pressure difference betweenthe liquid and vapor by the Young-Laplace equation [4],which in differential form can be written as

− = − ( ) ( ) (1)Both liquid and vapor flow are modeled as laminar pipeflow. The liquid pressure gradient in the grooves and vaporpressure gradient in the core can be expressed as, [5],

= −32, , = −32 , (2)The axial conduction of heat in the envelope is neglected.Hence by conservation of energy, the mass flow rate can beexpressed as

= − = − ( )ℎ (3)The liquid and vapor velocities are expressed as follows

= ( )ℎ , = ( )ℎ (4)The heat load in the axial direction, [3], is

Combining equations (1) and (2) we obtain−32 , + −32, = − ( ) ( ) (6)

Further substituting for the liquid and vapor velocities fromequations (5) and (6)32 ( ), ℎ + 32 ( ), ℎ = − ( ) ( ) (7)The boundary condition having radius of meniscus at thecondenser end equal to the radius of the vapor groove [1] isconsidered. The equation (9) is solved by the fourth-orderRunge-Kutta method and variation of curvature over thelength of the groove is obtained. The maximum heat thatcan be transported by the heat pipe is obtained by equatingthe radius of meniscus at the evaporator end to half ofgroove width [2].

= 2 (8)Maximum heat transport capability is obtained bymultiplying the maximum heat transported with effectivelength of the heat pipe. For a given heat pipe geometry,heat transport capability is a function of temperature. Thisis due to the fact that working fluid properties like density,viscosity, surface tension, latent heat, etc. varies withtemperature. Variation of computed maximum heattransport capability over different operating temperatures isplotted in the following figure.

Fig. 1. Variation of maximum Heat Transport Capability withoperating temperature

It can be seen from the plot that, maximum heat transportcapability reaches to its peak at a given temperature andfalls to minimum on either side.

III. HEAT PIPE EXPERIMENTAL SETUP

An experimental setup is built to measure maximum heattransport capability of the given heat pipe. Theexperimental setup consists of a horizontal workingplatform, height adjustable fixture, instrumented heat pipe,DC power supply, constant temperature bath and a dataacquisition unit, as shown in the following figure.

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Fig. 2. Schematic showing experimental setup

The heat pipe is divided into three zones, namely,evaporator (where heat is added), adiabatic (where there isno heat transfer across the heat pipe, to externalenvironment) and condenser (where heat is removed). Thegiven heat pipe is instrumented with resistance foil heaterson the evaporator end and micro channel heat exchangers atthe condenser end. The zone between evaporator andcondenser (adiabatic) is covered with insulation material.T-type thermocouples are mounted on external surface atvarious locations along the length of the heat pipe. Theentire setup is connected to a data acquisition and controlunit (cRIO-9082 system). A Virtual Interface (VI) iscreated using Lab-View program through which theexperiments are controlled.

Fig. 3. Complete Experimental Setup

The maximum heat transport capability values arecalculated for highest value of heat input before dry outlimit is reached, for different operating temperatures.Figure 4 gives experimentally obtained values at -20oC,0oC, 20oC and 40oC along with theoretical estimate.

It shall be noted that, at -20 oC experimental value is lowerthan theoretical value and at higher temperatures,experimental results are higher than theoretical estimates.This is due to the fact that, mass of the working fluid isestimated considering liquid volume equal to groovesvolume and vapour volume equal to core volume at 0oC. Attemperatures below 0oC, specific volume of liquidammonia decreases causing shortage of liquid in thegrooves. While at higher temperatures, specific volume ofliquid ammonia increases causing availability of moreliquid in the grooves to absorb more heat in the evaporator.This liquid puddle effect at higher temperatures will beconsidered in the theoretical computation as a part of futurework.

Fig. 4. Experimental data of maximum Heat Transport Capabilitycompared with theoretical estimation

IV. DESIGN OF PCM MODULE

The Phase Change Material (PCM) module is designed soas to store approximately 30 kJ of heat. Consideringparaffin wax or Eicosane as the candidate phase changematerial, the size of the module was worked out to be100mm x 100mm x 20mm based on the latent heat of thechosen PCM material. Flanges are provided on all foursides at the top to have Copper is chosen as the material ofthe module. Two pressure relief valves are provided on thetop plate of the module to provide pressure relief in casesof pressure build up inside the PCM module during itsoperation. A silicone gasket of 1mm thickness is usedbetween the two parts of the PCM module to ensureadequate sealing of the phase change material duringmelting. The module also has a slot, at its base, throughwhich the heat pipe can be coupled thermally to the PCMwith minimum resistance.

Fig. 5. PCM module assembled with a top plate










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The thermo-physical properties of Paraffin Wax andEicosane are given in the following tables 1 and 2.

Material Paraffin Wax(MERCK make)Melting Point (°C) 58-60

Density (kg/m3) 790 (liquid, 65 °C)916 (solid, 24 °C)

Heat of Fusion (kJ/kg) 173.6Thermal Conductivity(W/m-K)

0.167 (liquid, 63.5 °C)0.346 (solid, 33.6 °C)

Table 1: Properties of Paraffin Wax

Material Eicosane (Sigma-Aldrich)Melting Point(°C) 35-37

Density(kg/m3) 770 (liquid)810 (solid)

Heat of Fusion(kJ/kg) 241.0Thermal Conductivity(W/m-K)

0.157 (liquid)0.390 (solid)

Table 2: Properties of Eicosane

V. PCM MODULE CHARACTERIZATION STUDIES

PCM module is filled with 150g of Paraffin wax and isinstrumented with a resistance foil heater at its base.Several thermocouples are mounted at various locations onthe external surfaces of PCM module. In addition, onethermocouple is left floating inside the PCM module tomeasure the temperature of the candidate PCM. Thethermocouples are connected to a NI 9213 DAQ cardwhich is connected to NI cRIO system. The heater ispowered through a programmable DC power supply unit.The measured data are continuously stored on a dedicatedcomputer through Lab-View interface.

Few experiments are conducted to study phase changebehaviour of PCM inside the module, to verify heat storagecapacity and to measure temperature distribution within themodule. The experiments were repeated for variousconfigurations of the module, different heat inputs andoperating conditions.

It is found that for an input of 20 W of heater power for4630 sec, 1.0 W was consumed by PCM module as sensibleheat, 3.5 W was expended as sensible heat within the PCM,9.5 W of heat is lost through convection and remaining 6.0W is utilised in melting the PCM. Approximately 28 kJ ofheat can be stored as latent heat using Paraffin wax as PCMin this module. Since Copper is chosen as material forbuilding PCM module, temperature difference under stablecondition was found to be less than 5oC within the module.Module temperatures and PCM melting characteristics areshown in the following plot.

Fig. 6. Experimental data showing temperature distribution on thePCM module

VI. HEAT PIPE TRANSIENT EXPERIMENTS

Experiments were carried out to characterize the transientbehavior of the heat pipe. These experimental results serveas a baseline reference to understand the characteristics of aheat pipe when coupled with PCM module. To simulate 30kJ of heat input, the heat pipe evaporator is powered to 50W for 10 minutes in the two experiments described below.

1. In the first experiment, 50 W of heat applied for 10minutes at the evaporator is removed from thecondenser, exposed to the surroundings, through naturalconvection.

2. In the second experiment, heat exchanger is attached tothe heat pipe condenser which in turn is connected toconstant temperature bath. 50 W of heat is removed at aconstant temperature of 30oC through the continuousflow of fluid inside the heat exchanger.

Heat pipe transient temperatures in the above experimentsare plotted in Figure 7.

Fig. 7. Experimental data showing evaporator temperature duringheat pipe transient experiments

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It may be noticed from Figure 7 that, in the absence of heatstorage unit, 25°C temperature raise is observed in the heatpipe within 10 minutes of heat input. To maintain the heatpipe at constant temperature during heat addition,continuous removal of heat is required. This can beachieved either through the use of complex active devicelike constant temperature bath / heat exchanger or a simplePCM module coupling.

VII. HEAT PIPE COUPLED WITH PCM MODULE

The PCM module is coupled to the heat pipe at theevaporator with the help of thermal grease to ensure goodthermal contact. The other surface of the heat pipe isinstrumented with foil heater, as shown in the followingfigure.

Fig. 8. Schematic showing Heat Pipe Coupled with PCM Module

The PCM module is filled with Paraffin wax (% fill). Twoexperiments are carried out again. In the first experiment,50 W of heat for 10 minutes is removed from the heat pipecondenser through natural convection. Following plotshows heat pipe transient temperatures without PCMmodule and by coupling the PCM module.

Fig. 9. Heat Pipe evaporator temperature when coupled with PCMModule (Paraffin Wax) having condenser section exposed toambient.

It may be noticed that, by introducing PCM module on theheat pipe evaporator, temperature raise falls by 7 to 8oC.However, since heat source is coupled to the heat pipe, onlya small amount of heat flows towards PCM module.Experiments by coupling the heat source directly to thePCM module will be carried out as a part of future work.

In the second experiment, condenser end of the heat pipe isconnected to the heat exchanger, which in turn to theconstant temperature bath. 50 W of heat for 10 minutes is

removed at a constant temperature of 60oC through thefluid circulation in the heat exchanger. Transienttemperatures of the heat pipe without PCM module and bycoupling PCM module are shown in the following plot.

Fig. 10. Heat Pipe evaporator temperature for Heat pipe coupledwith PCM Module having condenser section connected to a heatexchanger

An interesting phenomenon was observed during theexperiment on PCM coupled heat pipe. Though thecondenser was maintained at 60oC, the evaporatortemperature was less than 60oC initially due to the presenceof PCM module. Heat pipe was operating with flow ofammonia vapour from condenser end to evaporator end andflow of liquid ammonia in the grooves from evaporator endto condenser end. When a heat load of 50 W was appliedon the evaporator, its temperature increased to condensertemperature and heat pipe operation ceased. This resultedin sudden rise of heat pipe temperature and PCM modulefor next 90 seconds. When evaporator temperaturesufficiently rose above condenser temperature, heat pipestarted operating by reversing the direction of working fluidin the vapour core and grooves. This flow reversalphenomenon may be observed in the following plot whichshows evaporator and condenser temperatures of the heatpipe.

Fig. 11. Temperature vs Time plot showing the flow reversalprocess

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Heat of superheat equivalent to about 5oC temperature risefor 90 seconds was required to cause the reversal of fluidflow in the heat pipe. Such phenomenon is not reported inany known literature of open source.

One more experiment is conducted by filling the PCMmodule with n-Eicosane and coupling to heat pipeevaporator. 50 W of heat for 10 minutes is removed fromthe condenser through natural convection to thesurroundings. Following plot gives heat pipe transienttemperatures without PCM module and by coupling PCMmodule.

Fig. 12. Heat Pipe evaporator temperature when coupled withPCM Module (Paraffin Wax) having condenser section exposed toambient.

Since n-Eicosane is a single compound pure substancewhich melts at a temperature below 40oC and has samemelting point and solidification point temperature, it is apotential candidate for use in space applications.

VIII. CONCLUSIONS AND FUTURE WORK

Efforts have been put to develop 1-D mathematical modelto simulate steady state operation of an axially groovedheat pipe and estimate maximum heat transport capability.This model will be improved further to include puddleeffect of the working fluid at higher temperatures.Operation of heat pipe under transient heat loads will bestudied by developing a transient mathematical model as apart of future work.

Several experiments were conducted to characterise a givenheat pipe and calculate its maximum heat transportcapability at different temperatures. A PCM module isdesigned and thermal characterisation tests were conducted.This PCM module was thermally coupled to the evaporatorof the given heat pipe. Series of experiments wereconducted to study thermal behaviour of heat pipe in thepresence of PCM module. An interesting phenomenon offlow reversal was noticed inside the heat pipe.

Following activities are planned as a part of future work:

Coupling of PCM module in adiabatic zone andcondenser zone separately and analyse thermalcharacteristics.

Shift the heat source from heat pipe side to PCMmodule side and repeat all the above experiments.

Use thermal conductivity enhancers like finned topplate, metal foam or honeycomb structure inside thePCM module and repeat the experiments.

All efforts will be made to develop a state-of-the-art PCMcoupled heat pipe for spacecraft thermal controlapplications.

IX. REFERENCES

[1]Y. Chen, W. Zhu, C. Zhang and M. Shi, 'ThermalCharacteristics of Heat Pipe with Axially Swallow-tailedMicrogrooves', Chinese Journal of Chemical Engineering,vol. 18, no. 2, pp. 185-193, 2010.

[2]K. Do, S. Kim and S. Garimella, 'A mathematical modelfor analyzing the thermal characteristics of a flat micro heatpipe with a grooved wick', International Journal of Heatand Mass Transfer, vol. 51, no. 19-20, pp. 4637-4650,2008.

[3] Jeong-Se Suh and Young Sik Park, ‘Analysis ofThermal Performance in a Micro Flat Heat Pipe withAxially Trapezoidal Groove’, Tamkang Journal ofScience and Engineering, Vol. 6, No. 4, pp. 201-206 (2003)

[4]A. Faghri, Heat pipe science and technology.Washington, DC: Taylor & Francis, 1995.

[5]D. Reay, P. Kew and P. Dunn, Heat pipes. Oxford:Butterworth-Heinemann, 2000

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ISTC/CCE/NKS/335

Theoretical and Computational Study of Frictional Effects in Viscoelastic and

Generalized Elastic Contacts

D. Satish Kumar, Narayan K. Sundaram

I. INTRODUCTION

The problem of contacts between nominally clamped

engineering components under the influence of

external cyclic loads is important because of their as

initiators of failure. Micro-slip, high edge-of-contact

stresses and stress gradients combine to produce the

phenomenon of fretting failure. The highly local

nature of fretting contact failure necessitates high-

fidelity contact stress modeling in such applications

as bearings, riveted joints, etc.

In this context, metal-metals contacts have

been studied extensively using elastic contact

mechanics. However, metal-polymer (elastic-visco

elastic) contacts have been studied only sparingly,

with far fewer solutions to problems of fundamental

interest. The list of such solutions dwindles to none

when frictional effects and geometrically conforming

bodies are considered. The present work is a progress

report on a computational and theoretical study to

remedy these deficiencies and better understand the

role of friction in viscoelastic contacts better. A

secondary objective is to explore more general

geometries for elastic frictional contacts.

II. PRELIMINARY COMPUTATIONAL STUDIES

A geometrically conforming pin-plate configuration

was chosen for the problem. As a first approximation,

the elastic pin was modeled as rigid, and the material

of the plate modeled as a delayed viscoelastic

element with two springs and one time-constant. A

material time constant τ=5 s was assumed for

PMMA. The ABAQUS STANDARD implicit Finite

Element analysis (FEA) solver was used to model the

contact. A custom structured contact mesh-maker,

FEMESH2D, developed by one of the authors1 was

Narayan K Sundaram is an Assistant Professor in the

Department of Civil Engineering, IISc

used to generate the mesh for the plate , as shown in

Fig. (1). A total of 324 contact elements were used to

mesh the hole, of which roughly 100 elements were

in contact at peak load. The indenter was modeled

using discrete rigid R2D2 elements. The ABAQUS

four-noded, plane strain CPE4R element with

reduced integration was chosen. At least 100 time-

step increments were used per load step in the

analyses in order to capture the slip history and slip

reversals accurately.

The contact parameters are as follows: Hole

radius R=1 inch, pin-radius Rd=0.995 inch, E

(PMMA) =4.35x105 psi, Poisson’s ratio ν = 0.40. The

viscoelastic network parameters are τ=5 s, G1 = G2

= 2.14x105 psi. In the interest of simplicity, the

viscoelastic material’s Poisson’s ratio was treated as

a constant. The coefficient of friction µ=0.35.

Multiple loading scenarios were tested: 1)

Monotonically increasing, uni-directional pin-load 2)

Monotonically increasing, oblique pin-load 3) Cyclic

pin-load. A peak load of P=10,000 lb/in was chosen,

giving an effective conforming contact parameter

Lp = 2G(R-Rd)/(P(κ+1)) of 0.21. The lower the value

of this parameter, the more geometrically conforming

the contact, and the less valid the half-plane approx-

imation used in Hertz-type contact analyses.

In typical elastic contact analyses, a repeated

(fretting-type) load consists of the following: A

vertical load P applied in the first step with no

horizontal loading. In subsequent steps, P is held

constant, while horizontal load Q is cycled between

+Q0 and –Q0. In an elastic contact, it is well known

that the pressure and shear tractions reach a state

called shakedown, in which the tractions achieved at

D. Satish Kumar is an MSc student in the Department

of Civil Engineering, IISc


ISTC/CCE/NKS/335

he extremities of the load cycle do not change from

cycle to cycle.

In viscoelastic contacts, the expected lag in response

of the material makes the ratio of the time-period of

cycling to the time constant τ a critical parameter,

which is absent in purely elastic fretting contacts. We

selected a Q amplitude of 0.5 µ P, and the cycling

time period Tp of the horizontal load as 0.5 τ. The

analysis was run for total time of T=10 τ to capture a

sufficient number of cycles of response.

The pressure and shear tractions in the two cases are

shown in Figures (2) and (3) respectively. The purely

elastic tractions are also shown for comparison. It is

seen that the viscoelastic tractions also shakedown,

just as in the elastic case. The steady-state pressure

tractions differ by very little between the elastic and

viscoelastic cases; however, the peak shear tractions

in the two cases differ by as much as 9 percent. In

future studies, it is planned to do a complete

convergence study of the tractions. Subsequently,

subsurface stresses will be examined. The latter

analyses requires ultra-fine meshes, and replacement

of the contact boundary conditions by traction

boundary conditions using T, N obtained from the

contact analysis, with quadratic elements used

subsurface.

Figure 2: Normalized pressure tractions N(θ) R / P during horizontal load cycling. The elastic tractions (cyan and magenta lines) are shown

for reference. The time steps for these plots are t=2 τ (only P, black

lines). The blue and red lines are pressure tractions after 7 and

7.5 cycles of horizontal loading

Figure 3: Normalized shear tractions T(θ) R / P during

horizontal load cycling. The elastic tractions (cyan and

magenta lines) are shown for reference. The time steps

for these plots are t=2 τ (only P, black lines). The blue and

red lines are shear tractions after 7 and 7.5 cycles of

horizontal loading.

Figure 1 Close up view of the finite element mesh

REFERENCES

Golden and Graham 1988; Boundary Value

Problems in Linear Viscoelasticity, Springer

Sundaram N., Farris T.N., 2010; J. Mech. Phys.

Solids, 58(11) 1819-1833

ISTC/MAE/CO/322

Combustion studies of composite propellants

containing nano- burn rate catalysts

Charlie Oommen, R Arun Chandru

Department of Aerospace Engineering, Indian Institute of Science, Bangalore

and

R Rajeev

Vikram Sarabhai Space Center, Thiruvananthapuram

This paper presents our research initiative to

comprehensively study the combustion

properties of composite propellants in the

presence of nano-burn rate catalysts, and their

relation to the thermal decomposition properties

of AP, and the properties of the nano-catalysts.

Our preliminary results indicate that a simple

and direct co-relation between the thermal

decomposition properties of AP and the burning

rate of composite propellant, as suggested in

recent literature, does not necessarily hold true

in the case of nano-catalysts. On-going studies

will provide further insights into the factors and

mechanisms affecting the ballistic properties of

composite propellants in the presence of nano-

burning rate catalysts.

1. Introduction:

Ammonium Perchlorate (AP)-based solid rocket

propellants are extensively used in strategic

military vehicles and in launch vehicles for

space applications. The internal ballistic

properties of these composite propellants are

mainly governed by the thermal decomposition

characteristics of their major ingredient, AP (60–

90% w/w). The decomposition properties of AP

are known to be particularly sensitive to the

presence of certain additives, even in small

amounts. These additives are used as ballistic

modifiers to tailor the propellant’s ballistic

properties. Transition metal oxides are effective

catalysts for the thermal sensitization of AP, and

are consequently incorporated as burning rate

enhancers in AP-based propellant formulations

(0.1–3% w/w). Some proven transition metal

oxide catalysts used for burning rate

enhancement include Fe2O3, CuO, Cu–Cr–O and

MnO2 [1,2].

Of late, numerous nano-catalytic systems are

being evaluated for the thermal sensitization of

AP. It is reported that nano-metal oxides exhibit

superior catalytic activity than their micron-

sized counterparts, by way of lowering the

decomposition temperature, increasing the

reaction rate and enhancing the specific heat

release of AP decomposition. The enhanced

activity of these nano-metal oxide systems

towards AP decomposition has generally been

attributed to the higher surface area in the case

of nanoparticles, the enhanced defects in nano-

structures, and the synergistic effects of the

catalyst and support in nano-composites [3-5].

Although there are several reports on the

enhanced effect of nano-catalytic systems on AP

decomposition, reports regarding the ballistic

property modification of the composite

propellants with nano-catalysts are sketchy and

mostly contradictory. There are hardly any

studies that try to relate the properties of nano-

catalysts to the decomposition properties of AP,

and subsequently to the combustion properties

(burning rate, pressure exponent etc.) of the

processed AP-based composite propellant.

Our research group has previously worked on

the syntheses, characterization and evaluation of

various nano-catalytic systems for AP

decomposition. Some of the nano-catalysts that

we have synthesized and evaluated include

nano-structured Cu-Cr-O, Fe2O3, mesoporous

MnO2, ZnO etc. (see Fig. 1). Particularly, the

mesoporous MnO2 catalyst prepared by our

group shows the best catalytic activity towards

AP decomposition reported till date in open

literature [5].


This paper presents our research initiative to

comprehensively study the combustion

properties of AP-based composite propellants

containing various nano-burn rate catalysts, and

some preliminary results regarding the same.

Efforts to relate the combustion properties of the

propellant to the decomposition properties of

nano-catalyzed AP, and in turn to the nano-

structural properties of the catalysts are being

pursued. Various propellant processing aspects

involving the nano-catalysts like dispersion,

stability etc. are also being studied. During the

course of this research program, our objectives

mainly revolve around (a) creating a

comprehensive database of decomposition and

combustion characteristics of AP-based

composite propellants containing nano- burn rate

catalysts, (b) establishing co-relations between

the nano-catalyst properties, AP thermal

decomposition characteristics, and AP-based

composite propellant combustion characteristics,

and (c) investigating into the mechanism of

activity of nano- burn rate modifiers in AP-

based composite propellants.

Figure 1: Representative SEM images of some nano-

catalysts prepared by our research group:

(a) nano-structured Cu-Cr-O, (b) nano-structured

Fe2O3, and (c) mesoporous MnO2

2. Experimental

2.1 Materials: Various transition metal salts and

other chemicals used in the preparation of

(nano)-catalysts were all of analytical grade and

used as received without further purification. AP

used in our experiments was kindly provided by

IIT, Madras. Hydroxyl-terminated polybutadiene

(HTPB), Dioctyl adipate (DOA), and Isophorone

diisocyanate (IPDI) used for making the

composite propellants were provided by VSSC,

Thiruvananthapuram.

2.2 Syntheses and characterization of nano-

catalytic systems: Methods previously reported

in literature like homogeneous co-precipitation,

mesoporous templating, electrospinning etc.

were used to synthesize transition metal nano-

catalysts. Also, synthesis methods developed in-

house by our research group like impinging

streams and submerged spray precipitation were

used. Some of the nano-catalysts that we have

synthesized and evaluated include nano-

structured Cu-Cr-O, Fe2O3, mesoporous MnO2,

ZnO etc. The nano-catalysts were characterized

via techniques such as UV-Vis-IR Spectroscopy,

XRD, SEM, TEM, EDS and ICP-AES.

2.3 Evaluation of nano-catalytic systems: AP-

HTPB (80:20% w/w) composite propellant

formulations containing nano-burn rate catalysts

were prepared by standard methodology.

Custom-designed micro-V-blender and planetary

micro-mixer with hot-water circulation jacket

were used to mix the propellant compositions.

Multiple blade configurations were designed and

tested experimentally. The mixing homogeneity

achieved using our custom-designed micro-

mixers were comparable to that reported in

literature, as demonstrated using optical and

SEM imaging. Both micron-sized and nano-

structured catalysts could be processed

efficiently, under slightly different conditions.

Cylindrical strands of 50mm length were cast

under vacuum using custom-made casting

apparatus, and cured in a PID controlled hot-air

oven. The mixing homogeneity in the presence

of nano-catalysts was verified by optical and

SEM images. The propellant strands were

ignited using a heated nichrome wire, and the

burning rate was acquired via fuse wires

connected to a data acquisition system. Burning

consistency was monitored via a long-range

high-speed optical camera. Experiments at high

pressures upto 120 bars are planned in our high

pressure propellant strand burner being

fabricated. Thermal analyses techniques like

DTA-TG, DSC and EGA with FTIR & GC-MS

are also being planned to investigate into the

mechanism of activity of nano- burn rate

modifiers in AP-based composite propellants.

Table 1: Summary of catalyst surface area, decomposition temperature of catalyzed AP and burning rate

of AP-based composite propellant

3. Results and Discussion:

Fig. 1 shows representative SEM images of (a)

nano-structured Cu-Cr-O, (b) nano-structured

Fe2O3, and (c) mesoporous MnO2, prepared by

our research group. As shown in Fig. 2, the

addition of 2% w/w mesoporous MnO2 to AP

exhibits exceptional catalytic activity, as

compared to its micron-sized counterpart;

manifested as unprecedentedly low

decomposition temperatures, fast reaction rates

and enhanced heat releases. Structural features

of the nano-catalyst like its high surface area and

the presence of a 3D mesoporous network,

which facilitate the adsorption and desorption of

reactive molecules during the secondary gas-

phase processes, account for its observed

excellent catalytic activity.

Figure 2: Non-Isothermal TG and DTA curves of (a)

pure AP, (b) AP + 2% w/w micron-sized β-MnO2 and

(c) AP + 2% w/w mesoporous β-MnO2

One of the main arguments reported in literature

is that nano-catalysts, which are effective for

thermal sensitization of AP, when incorporated

in AP-based composite propellants, lead to

significantly higher burning rates [3-5].

However, as seen from Fig.2 and 3, and Table 1,

this is not necessarily true. Though mesoporous

MnO2 exhibits the best catalytic activity towards

AP decomposition reported in literature till date,

its effect on the burning rate of AP-HTPB

composite propellants is modest compared to

Fe2O3. These preliminary results with

atmospheric pressure burning rates reveal that

there is no direct co-relation between the AP

thermal decomposition characteristics, and AP-

based composite propellant combustion

characteristics in the presence of nano-catalysts.

Properties of the nano-catalysts such as its

phase, crystallanity, surface area and porosity

may be playing underlying roles in such

observation. Such a mechanism would be clear

after the on-going studies involving Fe2O3 nano-

catalysts with varied morphologies and surface

areas. These Fe2O3 catalysts are being prepared

by our in-house developed continuous

submerged spray precipitation technique,

wherein the nano-catalyst properties can be

varied in a facile manner by varying the reactant

conc. and/ or the inlet air pressure. Further, the

combustion studies in a high-pressure strand

burner are being intended, and are expected to

provide insights on the burning rates of nano-

catalyzed composite propellants at various

pressures and their corresponding pressure

exponents.

Figure 3: Representative images of combustion of

AP-HTPB composite propellant strands (a) without

catalyst, (b) with 2% w/w mesoporous MnO2 and (c)

with 2% w/w nano-structured Fe2O3,

SampleSurface area of

catalyst

Decomposition

temperature

Burning rate

(atm)

m 2 /g o C mm/sec

Pure AP - 426 2.1

AP+2% w/w micron-sized Fe2O3 14.5 361 3.0

AP+2% w/w nano-structured Fe2O3 66.9 310 3.8

AP+2% w/w mesoporous MnO2 86.7 273 3.2

Conclusion

This paper briefs about our research program

and progress regarding the combustion studies

of AP-based composite solid rocket propellants

containing nano- burn rate catalysts. Various

experimental set-ups have been/ are being

designed, fabricated and validated. Ongoing

studies indicate that the effectiveness of a nano-

catalyst in the thermal decomposition of AP

does not always translate into higher burning

rates of the composite propellant.

References

1. P. W. M. Jacobs and H. M. Whitehead,

Decomposition and combustion of

ammonium perchlorate; Chemical Reviews,

1969, 69 (4), 551–590.

2. David L. Reid, Antonio E. Russo, Rodolphe

V. Carro, Matthew A. Stephens, Alexander

R. LePage, Thomas C. Spalding, Eric L.

Petersen, and Sudipta Seal, Nanoscale

Additives Tailor Energetic Materials; Nano

Letters, 2007, 7 (7), 2157–2161.

3. Wei Li and Hua Cheng, Cu–Cr–O

nanocomposites: Synthesis and

characterization as catalysts for solid state

propellants; Solid State Sciences, 2007,

9(8), 750–755.

4. Satyawati S. Joshi, , Prajakta R. Patil and V.

N. Krishnamurthy, Thermal Decomposition

of ammmonium perchlorate in the presence

of nanosized ferric oxide; Defence Science

Journal, 2008, 58(6), 721-727.

5. R. Arun Chandru, Snehangshu Patra, Charlie

Oommen, N. Munichandraiah and B. N.

Raghunandan, Exceptional activity of

mesoporous β-MnO2 in the catalytic thermal

sensitization of ammonium perchlorate;

Journal of Materials Chemistry, 2012, 22,

6536-6538.

Debadrita Paria is a PhD student in the Centre for Nano Science and Engineering at the Indian Institute of Science, Bangalore, 560012, INDIA. [email protected] Ambarish Ghosh is an Assistant Professor at the Centre for Nano Science and Engineering, and an associate faculty in the Departments of Physics and Electrical Communication Engineering, Indian Institute of Science, Bangalore, 560012, INDIA. [email protected]

Project Ref No.: STC/P-337 dtd 25.3.2014

Electromagnetic field enhancement in sub-nm gaps

Debadrita Paria and Ambarish Ghosh

Abstract- We describe computational results on the

calculation of electromagnetic field enhancement at the

junction of plasmonic nanoparticles. In particular, we

assume a pair of Silver nanoparticles at variable

distances from each other and calculate the wavelength

and gap size dependence of the electromagnetic field

enhancement.

I. INTRODUCTION

The large concentration of electromagnetic fields near

metal nanoparticles upon illumination of light is one of the

defining concepts of light matter interactions at the

nanoscale [1,2]. The strength of this so called “near field”

can be very large at the junction of two metal nanoparticles,

which has facilitated promising technologies, such as the

detection of single molecules [3,4] through a large

enhancement of the Raman scattering signal. The degree of

enhancement of the near field increases rapidly as the

particles are brought closer, although at separations below a

few Angstorms, quantum tunneling of plasmons can limit

the enhancement. The purpose of this paper is to calculate

the degree of enhancement as a function of gap distance

down to few Angstorms, and investigate the wavelength

dependence of the field enhancement.

II. PLASMONIC COUPLING

In electrostatics, the electric field between two oppositely

charged plates varies inversely as the distance between

them, which arises due to Coulomb interactions between

the charges residing on the two plates. Similarly, the

dependence of the EM field enhancement9 on the size of the

gap (d) originates in the electromagnetic interactions

between the localized plasmon modes of two neighboring

nanoparticles (radius a). In the simplest approximation,

each particle can be approximated as an electric dipole,

whose strength is proportional to the net electric field

( = ) at its location, where is the polarizabiltiy of

the particle at the wavelength of illumination. The

contributions to the net electric field for particle 1 come

from the incident field ( ), as well as the electric field due

to the dipole () of particle2 at the location of particle 1,

where the dominant term of ∝ , thus implying a

strong distance dependence of the dipolar coupling. This

approximation breaks down for < , beyond which

higher order multipolar contributions needs to be taken into

account, where numerical calculations can provide an

accurate picture. Also important is the direction of

propagation and state of polarization of the incident beam,

and their relation to the interparticle axis.

III. RESULTS AND DISCUSSIONS

Figure 1: Light incident along the axis of a nanodimer. See text for details.

As an example, depicted schematically in Fig. 1, we have

considered linearly polarized monochromatic light incident

along the axis of the interparticle separation for two silver

nanospheres of radius a = 70 nm separated by a distance d.

The calculated value (using Comsol) of the squared

enhancement (|| ||⁄ )of the electric field, shows

concentration of the electric field at the junction of the

particles, which increases manifold as the distance d is

reduced. The maximum value of the enhancement within


the junction, given by (|| ||⁄ ) ,depended strongly on

the illumination wavelength and the interparticle separation,

as can be seen in Fig. 2a. The smallest d considered in

these simulations corresponded to the thickness of an atom,

which resulted in maximum value of(|| ||⁄ ) ≥ 10 in

the wavelength regime 405 nm to 530 nm (approx),

implying a system of plasmonic dimers with sub-nm

separation can achieve the strongest EM field

enhancements. The distance dependence of

(|| ||⁄ ) for three illumination wavelengths is shown

in Fig. 2b.

Figure 2:Light of certain wavelength, linearly polarized along the x-axis, incident along the interparticle axis (z-axis) of two silver nanospheres separated by a distance d. a, Spectral dependence of the maximum value of the enhanced field(|| ||⁄ )in the dimer junction for various d (labeled in nm). b, Dependence of the maximum enhancement (|| ||⁄ )

at the dimer junction on the size of the gap d = 0.34 nm, for three illumination wavelengths. The particular choices of the wavelengths (red – 620 nm, green – 530 nm, blue – 470 nm) are arbitrary, covering the visible range of the electromagnetic spectrum.

Interestingly, at small gaps (d< 0.68 nm) the dipolar

plasmonic modes, which are typically in the UV, were seen

to shift in the visible regime. This is interesting, as one of

our major aims in this project is to develop radiation

detectors in the NIR and IR region of the electromagnetic

spectrum.

IV. SUMMARY AND FUTURE DIRECTIONS

Our efforts in last few months have been to develop a

computational model to calculate the electromagnetic field

enhancement at the junction of a nanosized plasmonic

dimer. These calculations although trivial at large values of

d, tend to become time consuming and difficult when d

becomes less than a nm. As expected, over six orders of

intensity enhancement could be observed. It is important to

note that the present calculation does not allow for quantum

tunneling of the plasmons [5], which can become a limiting

mechanism below a dimer gap of 0.3 nm. Accordingly, the

enhancements obtained here could be the largest possible,

as reducing the gap further will only result in a reduced

field magnitude.

Going forward, we wish to try experimental validation of

these calculations. Of particular interest would be to

investigate if the plasmonic enhancement can be moved

further to NIR and IR regimes of the EM spectrum, such as

to couple the system with a photodetector material.

REFERENCES

[1] Maier, S. A. Plasmonics: Fundamentals and Applications: Fundamentals and Applications. (Springer, 2007). [2] Novotny, L. & Hecht, B. Principles of nano-optics. (Cambridge university press, 2012). [3] Kneipp, K. et al. Single molecule detection using surface-enhanced Raman scattering (SERS). Physical review letters 78, 1667 (1997). [4] Nie, S. & Emory, S. R. Probing single molecules and single nanoparticles by surface-enhanced Raman scattering. science 275, 1102-1106 (1997). [5] Savage, K. J. et al. Revealing the quantum regime in tunnelling plasmonics. Nature491, 574-577 (2012).

Modelling primary atomization of liquid jets

Santosh Hemchandra∗†

1. Introduction

The objective of this project is to develop a modelling technique using a fully compressibleapproach for modelling the atomization of liquid jets. The approach comprises of three mainelements -

1. Level-set method solver to track the location of the gas-liquid interface

2. Compressible flow solvers for the liquid and gaseous phases

3. A Ghost-fluid method module that captures jump conditions arising from mass, momen-tum and energy balance across the gas-liquid interface.

During the period 2013-2014, we analyzed several options for reinitialization scheme for item1 above. These efforts were partially successful at the time of the previous interim reportsubmitted in April, 2014 due to the presence of bugs in the reinitialization scheme. We havesince fixed these bugs and are currently working on phase 2. All of these solvers are beingimplemented within the custom MultiSolv framework developed within our group to realizecomplex multi-physics flow simulations.The activities over the past six months have been dividedinto phases as follows

1. Debug and fix the CR2 [1] level-set reinitialization scheme (current status: done).

2. Incorporate the level-set solver within the MultiSolv framework - (current status: done).

3. Implement a coupling framework within the MultiSolv kernel in and demonstrate couplingbetween flow and level-set solver in order to learn how this can be achieved - (currentstatus: done).

4. Convert existing 2D flow solver to 3D - (current status: done)

5. Parallelize the 3D solver, using MPI, to use structured multi block meshes and supportinput from commercial grid generation software - (current status: done).

The rest of this report is organized as follows. Section 2 provides details of the solver couplingframework implemented within the kernel and presents results from test simulations with a 2Dcompressible flow solver and the level-set method solver. Section 3 provides details about thestatus of the 3D compressible solver development effort and presents some initial results fromongoing simulations. Finally, this report closes with an outlook on next steps for the third year.

∗Assistant Professor, IISc, Bangalore†E-mail: [email protected], STC project # ISTC/MAE/SH/319

1


Figure 1: Schematic of level-set and flow solver overset grids

2. Solver coupling framework

The solver coupling framework and the mechanism of communication between the flow andlevel-set equation solvers is described in this section. The level-set solver advects gas-liquidinterface as per the velocity imposed by the flow field on either side. The objective of couplingthe level-set solver and flow solvers through an interface within the MultiSolv Kernel is toensure that the former can get the velocity information from flow solver and move the level-set/interface accordingly and the latter can get level-set field values from the former and applymatching conditions via the Ghost Fluid Method (GFM).

The level-set solver uses uniformly spaced structured grids and uses the local level-set methodto solve the level-set equation in order to reduce computational cost. This allows for refine-ment of the the level-set grid in order to resolve small interface features without increasingcomputational cost. The flow solvers on the other hand, use multi block structured grids withnon-uniform spacing that is necessary to correctly resolve flow features. Thus, coupling the twosolvers together requires mapping of the level-set solver grid on to flow solver grid and vice versaso that level-set function values and flow velocities can be interpolated between the two grids asneeded. The level set grid is overlaid on the flow grid as shown in fig. 1. It is created such thatit encloses whole flow domain. The grid size chosen for the level-set grid is ∆xLS = α ∆xF,min,where, ∆xF,min is the smallest distance between any two given points on the flow grid and α isa parameter. Bilinear interpolation is used to interpolate velocities from neighboring flow gridpoints to level-set grid points eg. see fig. 1 - the velocities at points marked ‘a’ and ‘b’ on thelevel-set grid are determined from the flow grid points ‘m’-‘p’ using bilinear interpolation.

Figure 1 shows that the grid points on the two grids do not exactly overlap. Thereforeto interpolate the data (velocity) from flow solver, each level-set grid point needs to haveinformation about its neighboring flow grid points. This is achieved by mapping level-set gridon the flow grid. Within the MultiSolv framework, each solver requests the other to map itspoints onto the latter’s grid and return an index handle per point during computation setup.Subsequent requests for function values at the the mapped points are obtained using thesehandles to address points. Thus, neither solver needs to be aware of the other’s internal datastructures allowing for great flexibility in choosing the numerical method etc. for either solver.We next show results from tracking the evolution of a closed material line in a two-dimensionaljet.

2

(a) (b)

Figure 2: (a) Schematic of flow configuration and boundary conditions (b) Initial level-setlocation

2.1 Test flow problem

Figure 2a schematically shows a two dimensional planar jet flow that is chosen as the baselineflow test case. Inflow forcing is applied such that the preferred varicose mode of the inflowprofile is excited in order to reduce the potential core breakdown length. The inflow velocityprofile is specified using the tanh function such that it varies smoothly from a value of 174.8m/s along the jet centerline to a value of 88.0 m/s in the co-flow region (see fig. xx). Theslot width is chosen as 1.25 mm for this test case, yielding Re = 6795. The solution domainis 50h long and is discretized with a uniform grid with ∆x = 0.09h and ∆y = 0.063h. Note,this flow configuration was chosen in order to leverage a pre-existing computation available.Both the shear layers are forced with and amplitude of 1% of the centerline velocity, in order toexcite the varicose mode. A time step of 0.001∆x is used and simulations are run for 0.5 flowthrough times (approximately 2000 time steps). All boundary conditions are applied using theNavier-Stokes characteristic boundary conditions (NSCBC) technique [2, 3].

Figure 2b shows the initial condition for the closed material curve as a red circle of diameter1h located along the centerline at a distance of 15h from the jet inflow plane. This position cor-responds to the start of the jet breakup location and would hence result in maximum distortionof the material surface within the given simulation time. Figure 3a shows several snapshots ofthe evolution of the the material curve at various times. The evolution of the curve is trackedusing the level-set solver which in turn, gets the velocity field from the flow solver. Note alsothat the curve is deformed into very thin filaments over time. These filaments then break upinto smaller pockets as the curve evolves. This can be explained as follows. The thickness ofthe filament reduces until such a point where it can no longer be locally resolved on the level-set mesh and hence breakup occurs. Physically, this would occur when the filament thicknesswas of the order of molecular length scales - a length scale that cannot be resolved within theframework of a continuum assumption based CFD solution. However, since the level-set meshcan be refined in our method independently of the flow mesh, filament breakup can be caused tooccur arbitrarily close to the physical value without significant increase in computational cost.This fact can be seen qualitatively in fig. 3 which shows the shape of the curve at t∗ = 0.172for various values of mesh refinement ratio, α. Notice that finer mesh, i.e. progressively smallervalues of α results in retention of longer unbroken filaments.

3

Figure 3: Evolution of the material curve shown at various instants in time

Figure 4: Influence of level-set mesh refinement ratio, α = ∆xLS/∆xFlow, on the materialline geometry (t∗ = 0.172). The background field shows vorticity magnitude

This concludes the summary of the level-set - flow solver coupling framework implementedwithin the overall framework of MultiSolv. We next summarize progress made on the devel-opment of a, 3D fully compressible, massively parallel, structured multi-block DNS/LES solversince April 2014.

3. Three dimensional flow solver

The 2D flow solver used for the above simulations has been extended to be able to computethree dimensional flows. Together with the explicit filtering technique, this solver can in generalbe used for LES of complex turbulent flows. The solver has also been parallelized using MPIand has been implemented within the framework of MultiSolv. As such, the solver couplingmethodology described in the previous section extends to the present three dimensional caseas well. Our implementation solves the Navier-Stokes equations in strong conservation form ingeneralized co-ordinates. As such, the present code can be used to compute flows in complex,industrially relevant geometries as well. As such, an input interface for grids specified in the

4

(a)

0 20 40 60 80 100 120 1400

20

40

60

80

100

120

140

Number of processes

Sp

ee

du

p [

t(1

)/t(

k)]

Speedup (1213)

Speedup(Linear)

(b)

Figure 5: (a) instantaneous slice plot of pressure (p-po) showing evolution of gaussianpressure pulse in a rectangular domain (b) a typical speedup plot up to 128 processors for1213 grid points

CGNS format has been implemented in the solver. CGNS is a fairly widely used industrystandard format that is supported by many standard mesh generation packages including inour case, ANSYS ICEM-CFD. Thus, the present 3D code has a significantly greater range ofproblems, i.e. from academic geometries to industrially relevant geometries, to which it can bepotentially applied.

The two dimensional NSCBC procedure [2, 3] has been reformulated for NS equations in gen-eralized co-ordinates. Currently, subsonic inlet, constant temperature no-slip wall and subsonicnon-reflecting outlet boundary conditions have been implemented as these will be necessary torealize the spray simulations that are the final goal of this project. Currently, validation anddebugging of each of these boundary condition implementation is ongoing and is expected to befinished by January 2014. We next present two test cases computed with this code and resultsfrom scaling tests on our in-house compute cluster.

3.1 Spherical pressure pulse

This case computes the evolution of a spherical pressure pulse in a cube of dimension 0.1 m. Thedomain is discretized using a uniform mesh with 121× 121× 121 points. No slip wall boundaryconditions are applied on all faces of the cube. The solution is initialized with a stationary flowat a temperature of 300 K. The wall temperature is assumed to be the same at all times. Theinitial pressure distribution is given by

p

po= 1 + 0.0001 × exp

(− (r−ro)2

2×c2)

(1)

where, po = 1.01325 × 105 and r is the radius vector referenced to the center of the cube.Figure 5a shows a typical snapshot of the the solution at a time t = 3× 10−5 s after the start ofthe simulation. This test case was used to test scaling results. A result from preliminary scalingtests with the inviscid version of the code, i.e. before transport terms were implemented, areshown in fig. 5b. This shows a reasonable level of scaling upto 128 processors. We have notbeen able to perform scaling tests with the current code at the moment as we are focusing ondebugging and implementing boundary conditions. We will perform scaling tests on the fullcode during the next year.

5

(a)

x/D

y/D

0 0.5 1 1.5 2 2.5−0.5

0

0.5

2.3

2.4

2.5

2.6

2.7

x 10−6

p

(b)

Figure 6: (a) schematic for the flow in a square duct showing boundary conditions andinlet velocity profile. (b) pressure and velocity fields for the square duct case

3.2 Flow in a square duct

Figure 5a schematically shows the flow setup for a flow in a square duct. The profile of thex-component of the velocity at the inflow is specified a top hat profile going from a centerlinevelocity, UCL, of 4 m/s to 0 m/s using smooth profiles generated using hyperbolic tangentfunctions. Likewize, the velocity profile at the inlet is extended into the domain along thestreamwise direction . The other velocity components are set to zero. The temperature at theinlet is specified as 300K. The reference pressure at the outlet for the NSCBC procedure isspecified as 1.01325 × 105 Nm−2. Figure 6b shows the solution at time t = .04 s correspondingto 0.32 flow through times (L/UCL). The field shows the instantaneous pressure distribution.Also shown are typical velocity vectors at selected locations close to the inlet. Note this solutionhad not reached steady state as of the time of writing this report.

4. Conclusion and next steps

The simple flows described in the previous section are being used at this time to debug andvalidate the 3D solver. We will next generalize the thermodynamics model in the solver to beable to use arbitrary equations of state, thereby, allowing the same code to be used for theliquid phase as well. Finally, we envisage coupling the this solver with the level-set solver andperforming first multiphase flow simulations begining April 2015.

References

[1] D. Hartmann, M. Meinke, and W. Schroder. Differential equation based constrained reini-tialization for level set methods. Journal of Computational Physics, 227(14):6821–6845,2008.

[2] T. J. Poinsot and S. K. Lele. Boundary conditions for direct simulations of compressibleviscous flows. Journal of computational physics, 101(1):104–129, 1992.

[3] K. W. Thompson. Time-dependent boundary conditions for hyperbolic systems, ii. Journalof Computational Physics, 89(2):439–461, 1990.

6

Development of an optimization based image processing software system for Indian forest resource assessment using Radar Imaging Satellite (RISAT-1)

Images

S N Omkar and G. Narayana Naik Principal Investigator, Dept of Aerospace Engineering, Indian Institute of Science, Bangalore

Ashoka Vanjare andSourabh Rao A Project Assistant, Dept of Aerospace Engineering, Indian Institute of Science, Bangalore

P G Diwakar Co- Investigator, NRSC, Hyderabad, India

Prashant BK Project Intern, Dept of Aerospace Engineering, Indian Institute of Science, Bangalore

Abstract- This paper examines the results of optimization based Image processing software system developed at Research and Development Center, Computational Intelligence Lab, Indian Institute of Science, Bangalore. Currently we are developing data processing system for processing forest land cover classification problem by using RISAT-1 satellite images. In this paper, we are exploring use of optimization techniques for improving the results and advantage in image analysis.Developed imaging system provides image enhancements, speckle reduction, and image classification; substantially increase the functionality of the software along with application of remote sensing data in different fields. This leads to optimal usage of remote sensing data.

I. INTRODUCTION Basic essential of life is land, water and air. Land cover refers to the physical and biological cover at the surface of the earth, including water, vegetation, bare ground, man-made structures, etc. Land cover information [1] acts as important piece of information for assessment of land utilization and resources optimally. Due to its advancement in digital technology, robustness and cost-effectiveness, remote sensing has been increasingly used to extract land cover information through either manual interpretation or automated classification. Further researchers are using optimization techniques [2] in order to improve usage of available resource. Pattern recognition techniques [3]are commonly conceived to have the capability of improving automated classification accuracy due to their distributed structure and strong capability of handling complex data. Many researchers are using neural networks techniques for land cover classification and comparing with the traditional statistical methods. This paper is divided into six sections, radar dataset, study area, data processing methods, results and conclusion.

II. RADAR IMAGE DATASET

In this section, different space Agencies and their SAR imaging products are given. 1) Indian Space Research Organization (ISRO) and National Remote Sensing Centre (NRSC) Example:RISAT-1 and 2 2)National Aeronautics and Space Administration (NASA) Example: radar Sensor SIR-A, SIR-B and SIR-C. 3) Japan Aerospace Exploration Agency (JAXA) Example: Advanced Land Observing Satellite ALOS 1 and 2 4) European Space Agency (ESA) Example: ENVISAT ASAR Different space agencies have launched different earth observation missions in order to collect earth land surface features. Initially data is acquired, calibration and validation, which involve sensor characterizations, image quality measurements, and accuracy improvement. Processed data is dissimilated to public use as standard products like L0, L1 and L2 products for remote sensing applications. ISRO recently launched a satellite mission with SAR sensor as payload called RISAT-1 satellite. RISAT-1 radar satellite images have information on land surface features. RISAT-1 dataset works in ScanSAR, strip and spot modes to provide images with coarse, fine and high spatial resolutions respectively. This is very useful in different remote sensing applications and one such application is forest resource assessment.

III. STUDY AREA In this section, study is discussed. We have chosen Bangalore region for studying urban vegetation features and Mandya and Mysore region for studying forest vegetation. Forest region consists of agricultural region, water region and hilly regions. We have initially used unsupervised classified techniques for forest region classification before collecting ground truth data.


IV. DATA PROCESSING METHODS In this section, we are discussing different data processing methods. They are: 1) Preprocessing (Geometric correction and Radiometric calibration), 2) Speckle reduction and3) Image Classification.

1) Preprocessing: (Geometric correction and Radiometric calibration)

Initially Polarimetric SAR satellite image are pre-processed. Radio metrically [4] calibrate – Calculate Sigma nought and beta nought of the given image and after this step geometric correction is performed. Antenna pattern correction is done to calculate the variation and remove the variations caused in transmission and receiving of signals.

Fig: 1 a) RISAT-1 Polarimetric SAR (Combination hh-hv-hh) is overlaid on the Landsat 8 multispectral optical satellite image and b) RISAT-1 image is compared with Landsat 8 image for urban vegetation classification and c) MysoreForest region is considered as region of interest for forest region classification. Given polarimetric SAR satellite image (HH and HV) is geometrically registered [4-6] to optical satellite image in order to geocoding the data. The geocoded image is shown in figure -1.

2) Speckle reduction techniques: As polarimetric SAR image is affected by noise [4] so filtering techniques are applied in order to remove the noise. a) Lee Filter: Lee filter is used to smoothen the image and remove noise from data. Lee filtering is a standard deviation based filter that filters data based on statistics computed. Lee filters preserve image edges, sharpness and spatial information compared to low-pass smoothing filter. The image pixel value is replaced by a value calculated using the surrounding connected pixels. b) Enhanced Lee: Enhanced Lee filter is used to reduce speckle in radar imagery which preserving texture and edge information. The Enhanced Lee filter is an advanced version of the Lee filter. Enhanced Lee filter uses local statistics-coefficient of variation for window information. Neighboring image pixel value is put into one of three classes namely a) Homogeneous: the neighboring image pixel value is replaced by the average of the filter window, b) Heterogeneous: The neighboring image pixel value is replaced by a weighted average and c) Point target: The neighboring image pixel value is not changed. c) Frost filter: Frost filter is used to reduce speckle while preserving edges and retaining texture information in radar images. It is an exponentially damped circularly symmetric filter that uses local statistics. The pixel being filtered is replaced with a value calculated based on the distance from the filter center, the damping factor, and the local variance. d) Gamma filter:Gamma filter is used to reduce speckle while preserving edges in radar images. Gamma filter assumes that the data is gamma distributed and filtered image pixels are replaced with a neighbor image pixel value calculated based on the local statistics. e) Local Sigma filter:Local sigma filter is used to preserve fine detail in low, medium and fine resolution image and low contrast areas.It is to reduce speckle significantly by using the local standard deviation computed for the filter box to determine valid image pixels within the filter window. f) Bit Error filter:Bit error filter is used to remove bit-error noise, which is usually the result of speckles in the data caused by isolated pixels that have extreme values unrelated to the image scene. Bit-error removal method uses an adaptive based technique to replace speckle or spike pixels with the average of neighboring pixels. Local statistics like- mean and standard deviation are computed within the filter box and it is used to set a threshold for filtering the desired image quality. g) Gray Level Co-occurrence Matrix filter: In statistical texture analysis, texture features are computed from the

statistical distribution of observed combinations of intensities at specified positions relative to each other in the image. According to the number of intensity points (pixels) in each combination, statistics are classified into first-order, second-order and higher-order statistics. Gray Level Co-occurrence Matrix (GLCM) method [4-7] is a way of extracting second order statistical texture features. GLCM is a matrix where the number of rows and columns is equal to the number of gray levels, in the image. Suppose an image to be analyzed is rectangular and has Nx resolution cells in the horizontal direction and Ny resolution cells in the vertical direction.Let the gray tone appearing in each resolution cellsis Ng quantized levels. Let Lx = 1, 2… Nx be the horizontal spatial domain, Ly = 1, 2... Ny be the vertical spatial domain and G = 1, 2...Ng be the set of Ng quantized gray tones. The set LyxLx is the set of resolution cells of the image ordered by their row- column designations. The image I can be represented as a function which assigns some gray tone in G to each resolution cell or pair of coordinates in LyXLx, such that I: LyX Lx --> G. GLCM is a matrix of relative frequencies Pij with which two neighboring resolution cells separated by distance d occur on the image, one with gray tone i and the other with gray tone j. This matrix is a function of the angular relationship between the neighboring resolution cells as well as a function of the distance between them. Texture measurement requires the choice of window size, quantization levels, displacement value, and orientation factors for each texture measure. In this work, each texture measure/feature have been calculated in all 4 orientation angles (0°, 45°, 90°, 135°) and feature measures of the matrices of all the four orientations are then averaged, to get a directionally invariant matrix.

3) Image Classification methods: In this section, we are discussing different image classification methods [3-4] used in this paper. Unsupervised method: In this section, two unsupervised classification methods are discussed. K-means and ISO clustering method are applied to SAR image in order to identify the vegetation regions. a)K-means clustering: K-Means is an unsupervised classification technique which calculates initial class means in evenly distributed in the data space then iteratively clusters the pixels into the nearest class using a minimum distance technique.Each iteration recalculates class means and reclassifies pixels with respect to the new means. All pixels are classified to the nearest class. By setting a standard deviation value or distance threshold value unclassified image pixels are grouped till they meet threshold criteria. This process is continued till thegive number of pixels in each class are assigned till selected pixel change threshold value or the maximum number of iterations is reached.K means parameters

Mean 1752.132369, Standard deviation 1360.323747, Classes-8 and threshold-5 are used for all the filtered images. b)ISO-Data Clustering: ISO-DATA is an unsupervised data clustering technique which calculates class means considering as evenly distributed in the data space then iteratively clusters the remaining pixels using minimum distance techniques.In each iteration,mean is recalculated and the same image reclassified with respect to the new computed means. This process is repeated until the number of pixels in each class changes till selected threshold value is reached else the maximum number of iterations is attained. Supervised classification method: In this section, the three ArtificialNeural Network methods like Multi-Layer Perceptron (NN-MLP),Radial Basis Function - Neural Network(NN-RBF) andCircular Complex valued Extreme Learning Machine(CC-ELM) are discussed. These methods are applied for classification of Multispectral and Polarimetric SAR (Synthetic Aperture Radar) data. Artificial neural network methods c) Neural Networks-Multi Layer Perceptron: Neural Network is a layered feed-forward neural network classification technique. NN-MLP technique uses standard back propagation as supervised learning technique. It can select the number of hidden layers.It uses a logistic or hyperbolic activation function for classification process. NNMLP learns by adjusting the weights in the node to minimize the difference between the output node activation and the output. InNNMLP method, error is back propagated through the network and weight adjustment is made using a recursive process. NNMLP classification technique is used to perform non-linear classification and this method is used as optimized for pattern classification. d) Neural networks-Radial Basis Function:Neural Network - radial basis function (NN-RBF) also uses a feed forward network. It is structurally similar to MLP-NN, but the activation function in the hidden layer nodes is called radial basis activation function and it also uses back propagation as supervised technique. The output of the activation function depends on the location of the center of the function and the spread of the function. The output of a radial basis function can be defined as:

ɸ(x) = exp(-|| x – c ||2 / 2*σ2) Eq 1 Where, c is the center of the RBF unit,x is the input and σ is the spread of the RBF unit. The inputs are first normalized using suitable normalization. This activation function in the hidden layer produces a non-zero response when the input falls within kernel function. Each hidden unit has its own receptive fields in input space. The weights connecting the inputs to the hidden layer decide the spread of the activation function and the weights

connecting the hidden layer to the outputs is used as a scalar multiplier to the hidden layer outputs. The network output is the sum of weighted hidden layer outputs. The training of the network involves the adjustment of the two sets of weights and updating of centers of the hidden nodes. The mean squared error is determined between the network output and the target output values. The centers and weights are initialized randomly. The weights and centers are optimized using Gradient Descent Back Propagation algorithm, by minimizing the instantaneous mean squared error. The training algorithm aims at minimizing the error and the optimization of the weights and the center location. The training is carried out till the target performance is reached. The network is then tested for performance and generalization using the testing dataset. e) Circular Complex Extreme Learning Machine (CC-ELM): Circular Complex-valued Extreme Learning Machine (CC-ELM) [23] is designed for handling real-valued classification problems. CC-ELM is a single hidden layer network with non-linear input and hidden layers and a linear output layer. A circular transformation with a translational or rotational bias term that performs a one-to-one transformation of real-valued features to the complex plane is used as an activation function for the input neurons. The neurons in the hidden layer employ a fully complex-valued Gaussian based activation function. The input parameters of CC-ELM are chosen randomly and the output weights are computed analytically. The input and output configurations of CC-ELM are similar to that of MLP and RBF-NN. In CC-ELM the input layer activation function is ‘Circular Transformation’, which maps the real valued data to the complex domain. The transformation function is give in equation 2.

zt = sin ( axt + ibxt + α ) Eq 2 Where:xt is the input vector for the tth sample and observation,such that each of xti is normalized in [0,1]; 0 < a, b ≤ 1, and 0 <α< 2π. All the three NN-MLP, NN-RBF and NN-ELM methods are used for urban vegetation because of similar techniques for SAR image classification.

V. RESULTS In this section, two applications urban vegetation classification and forest vegetation classification is discussed.For urban vegetation classification, we have used Landsat 8 multispectral image and RISAT-1 SAR image. Initially the SAR image are preprocessed using frost and GLCM filters and enhanced images are classified using three different neural networks-NNMLP, NNRBF and CC-ELM methods. Here we have classified RISAT image and overlaid on multispectral image. Class Number of Pixels Urban 12405 Vegetation 3942 Water 5957

Training Set Size 300 Pixels per class Testing Set Size 750 Pixels per class Table 1: Comparison of different classification techniques In table-1, three different image pixels-urban, vegetation and water is given along with training and testing set size.

Fig 2: a) original Landsat image, b) MLP-NN classified SAR image, c) NN-RBF classified SAR image and d) CC-ELM classified SAR image. Figure 2 gives details of classified images by using three different methods-NN-MLP, NN-RBF and CC-ELM.Results of classified image can be compared with original image. Properties NN-MLP NN-RBF CC-ELM

Hidden neurons

10-30 (best 28) 100 max (accuracy

goal 0.007)

50

Train accuracy

97.30% 98.85% 98.45%

Testing accuracy

97.25% 98.63% 98.40%

Table 2: Comparison of different classification techniques From Table-2 we can see the classification accuracy. These three algorithms are optimized so we are getting good results. By quantitive analysis and value, we can understand by using optimization techniques we can improve classification results. For forest vegetation classification we have used RISAT-1 (HH and HV) images. For forest region classification application we applied six filters lee, enhanced lee, frost, gamma, local sigma, bit error.

Figure 3: Results of lee filtered SAR classified image

Figure 4: Results of enhanced lee filtered SAR classified image

Figure 5: Results of frost filtered SAR classified image

Figure 6: Results of gamma filtered SAR classified image

Figure 7: Results of local sigma filtered SAR classified image

Figure 8: Results of Bit error filtered SAR classified image Figure 3-8 gives the different filtering techniques applied and image quality is enhanced. Six filtered SAR images are classified using K Means and ISO Data clustering techniques into 8 different classes. As we don’t have ground truth data,hence we have usedK-means unsupervisedtechnique for forest region classification.Enhanced lee and frost filtering technique has improved image quality in comparison with other filtering techniques. By visual inspection, we have to determine the classified quality with the given image.

VI. CONCLUSION Speckle reduction technique plays very important role in radar image processing due to which pattern classification results

improves.Initially, we used filtering techniques for preprocessing and enhanced images were classified. For urban vegetation region as we use optimized neural network techniques, results are good and in case of NN-RBF it has classified the given image very well in comparisons to other techniques. To classify forest regions, we initially used unsupervised method to determine different unknown classes. K-means clustering method performs well in comparisons with ISO Data method. In urban vegetation applications, we observed that optimization techniques are very useful in order to improve classification results while in forest region classification application K-means gives initial class information.

FUTURE WORKS Develop different optimization techniques and compare for different forest land classification problem and these techniques are designed into asatellite image processing application. Further these techniques have to be improved by keeping time complexity.

ACKNOWLEDGMENT

The authors thank the reviewers for their critical comments which helped to improve the quality of the paper significantly. This work is supported by IISc-STC cell and carried out by research grant (Project no ISTC0340). Thanks to Aditi Kanjolia, Student, Indian Institute of Technology, Indore, Email: [email protected] and Sandesh C, Student, Indian Institute of Technology, Kharagpur, Email: [email protected]. We are grateful to Forest department of India, Forest survey of India, survey of India, NASA, JAXA and ESA for providing information and support.

REFERENCES

[1] Ulaby, F.T.; Kouyate, F.; Brisco, B.; Williams, T.H.L., "Textural Information in SAR Images," Geoscience and Remote Sensing, IEEE Transactions on , vol.GE-24, no.2, pp.235,245, March 1986 doi: 10.1109/TGRS.1986.289643

[2] Barber, David G., et al. "A comparison of second-order classifiers for SAR sea ice discrimination." Photogrammetric engineering and remote sensing 59.9 (1993): 1397-1408.

[3] Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification, 2E" wiley

[4] Lee, J.S., Pottier, E., “Polarimetric Radar Imaging - From basics to applications” , CRC Press (2009))

[5] D. G. Barber, M. E. Shokr, R. A. Fernandes, E. D. Soulis, D. G. Flett, and E. F. LeDrew. A comparison of second-order classifiers for SAR seat ice discrimination. Photogrammetric Engineering &Remote Sensing, 59:1397-1408, 1993.

[6] R. T. Frankot and R. Chellappa. Lognormal randomfield models and their applications to radar image synthesis. IEEE Trans. Geosc. Remote Sensing, 25:195-206, 1987.

[7] Murni, N. Darwis, M. Mastur, and D. Hardianto. A texture classification experiment for SAR radar images. In Proceedings of Pattern Recognition in Practice IV, pages 213-224, Vlieland, Netherlands, June 1994.

[8] A. H. Schistad and A. K. Jain. Texture analysis in the presence of speckle noise. In Proceedings of the International Geoscience and Remote

Sensing Symposium (IGARSS), pages 884-886, Houston, Texas, May 1992.

[9] Berberoglu, Suha, et al. "Texture classification of Mediterranean land cover." International Journal of Applied Earth Observation and Geoinformation 9.3 (2007): 322-334.

[10] M. Tuceryan and A. K. Jain. Texture analysis. In C. H. Chen, L. F. Pau, and P. S. P. Wang, editors, Handbook of Pattern Recognition and Computer Vision, pages 235-276. World Scientific Publishing Company, 1993

[11] Holmes, Quentin A; Nuesch, Daniel R.; Shuchman, R.A, "Textural Analysis And Real-Time Classification of Sea-Ice Types Using Digital SAR Data," Geoscience and Remote Sensing, IEEE Transactions on , vol.GE-22, no.2, pp.113,120, March 1984 doi: 10.1109/TGRS.1984.350602

[12] Solberg, Anne H. Schistad, and Anil K. Jain. "Texture fusion and feature selection applied to SAR imagery." IEEE Transactions on Geoscience and Remote Sensing 35.2 (1997): 475-479.

[13] Schistad, AH.; Jain, AK., "Texture Analysis in the Presence of Speckle Noise," Geoscience and Remote Sensing Symposium, 1992. IGARSS '92. International, vol.2, no., pp.884,886, 26-29 May 1992 doi: 10.1109/IGARSS.1992.578286

[14] Jong-Sen Lee, "Digital Image Enhancement and Noise Filtering by Use of Local Statistics," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.PAMI-2, no.2, pp.165,168, March 1980 doi: 10.1109/TPAMI.1980.4766994

[15] Kuan, Darwin T.; Sawchuk, AA; Strand, Timothy C.; Chavel, P., "Adaptive Noise Smoothing Filter for Images with Signal Dependent Noise," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.PAMI-7, no.2, pp.165,177, March 1985doi: 10.1109/TPAMI.1985.4767641

[16] Frost, Victor S.; Stiles, Josephine Abbott; Shanmugan, K.S.; Holtzman, J., "A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.PAMI-4, no.2, pp.157,166, March 1982 doi: 10.1109/TPAMI.1982.4767223

[17] Lopes, A; Touzi, R.; Nezry, E., "Adaptive speckle filters and scene heterogeneity," Geoscience and Remote Sensing, IEEE Transactions on , vol.28, no.6, pp.992,1000, Nov 1990 doi: 10.1109/36.62623

[18] Zhenghao Shi; Fung, K. B., "A comparison of digital speckle filters," Geoscience and Remote Sensing Symposium, 1994. IGARSS '94. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation., International , vol.4, no., pp.2129,2133 vol.4, 8-12 Aug 1994 doi: 10.1109/IGARSS.1994.399671

[19]Haralick, R.M.; Shanmugam, K.; Dinstein, Its'Hak, "Textural Features for Image Classification," Systems, Man and Cybernetics, IEEE Transactions on , vol.SMC-3, no.6, pp.610,621, Nov. 1973 doi: 10.1109/TSMC.1973.4309314

[23] Fast learning Circular Complex-valued Extreme Learning Machine (CC-ELM) for real-valued classification problems R. Savitha , S. Suresh , N. Sundararajan

[24] Kim, T., Adali, T.: Fully complex multi-layer perceptron network for non-linear signal processing. Journal of VLSI signal processing 32(1/2), 29-43 (2002)

[25] Tohru Nitta: Orthogonality of decision boundaries in complex valued neural networks. Neural Computing. 2004 Jan; 16 (1): 73-97.

[26] Ortega, J.M.: Matrix Theory. Pienum Press, New York (1986) [27] A. H. Schistad Solberg, A. K. Jain, and T. Taxt, “Multisource

classification of remotely sensed data: Fusion of Landsat TM and SAR images,” IEEETrans. Geosci. Remote Sens., vol. 32, no. 4, pp. 768–778, Jul. 1994.

[28]Pacifici, Fabio, et al. "Urban mapping using coarse SAR and optical data: Outcome of the 2007 GRSS data fusion contest." Geoscience and Remote Sensing Letters, IEEE 5.3 (2008): 331-335.

[29] Ashoka Vanjare, S.N. Omkar, J.Senthilnath,"Satellite Image Processing for Land Use and Land Cover Mapping", IJIGSP, vol.6, no.10, pp.18-28, 2014.DOI: 10.5815/ijigsp.2014.10.03

[30]Ashoka Vanjare,S.N. Omkar,Akhilesh Koul,Devesh,"Aerial Video Processing for Land Use and Land Cover Mapping", IJIGSP, vol.5, no.8, pp.45-54, 2013.DOI: 10.5815/ijigsp.2013.08.06

[31] C.S. Arvind, Ashoka Vanjare, S.N. Omkar, J. Senthilnath, V. Mani and P.G. Diwakar, “Multi-temporal Satellite Image Analysis Using Unsupervised Techniques,” Advances in Computing and Information

Technology (Eds. Natarajan Meghanathan, DhinaharanNagamalai, NabenduChaki), Advances in Intelligent Systems and Computing, 2013, vol. 177, pp. 757-765, Springer Verlag, Berlin, Germany, ISBN 978-3-642-31551-0.

[32] J. Senthilnath, S. N. Omkar, V. Mani, Ashoka Vanjare, P. G. Diwakar"Multi-Temporal Satellite Image Analysis Using Gene Expression Programming"Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012Advances in Intelligent Systems and Computing Volume 236, 2014, pp 1039-1045.

[33] M. E. Shokr. Texture measures for sea-ice classification from radar images. In Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), pages 763-768, Vancouver, Canada, 1989

[34] RISAT-1 Handbook, Indian Space Research Organization, India [35]ALOS Data Users Handbook, JAXA, Japan [36] Sentinel-1 User Handbook, European space agency [37] SAR user marine manual [38] CEO Users Guide book [39]J.N Bruning, Radar Imagery: Radarsat-1 [40] PolSAR Tutorials for researchers, European space agency

Numerical Simulations of Darcy-Brinkman-Forccheimer Equations

for Flow in Porous Media∗

Karthick M.†and Gaurav Tomar‡

Abstract – Implementation of Darcy-Brinkman-Forccheimer equations in the open source codeGerris. The adaptive mesh refinement andvariable time stepping in Gerris can be em-ployed to perform high resolution simulationsof two phase flow in porous media.

I Introduction

Multiphase flow in porous media has gained impor-tance in the last decades due to demand for increasedefficiency in extracting oil and other fuel sources suchas shale gas. Porous wicks are employed in the evapo-rators of modern heat pipes such as capillary pumpedloop and loop heat pipes. Liquid refrigerant floodsthe porous wick due to capillary suction and evap-orates due to the heat flux from a heat load. Thevapour so formed is pushed out of the vents into thevapour line and condenses away from the heat gener-ation point. This provides great flexibility in design-ing heat exchangers for electronic cooling in satelliteswhere equipment located in the core of the satelliteneed to dissipate heat in the outer space through ra-diation. Since the experimental investigation are lim-ited by the lack of access to the flow regions in suchsystems, numerical simulations can provide insightsinto the complex phase change heat transfer and fluidflow physics.

∗Project: ISTC/0327†Project Assistant in the Department of Mechanical Engi-

neering, IISc Bangalore‡Assistant Professor in the Department of Mechanical En-

gineerin at Indian Institute of Science Bangalore - 560012.Email: [email protected]

The flow through a porous medium is inherentlymultiscale involving small length scale local resis-tance to flow due to tortuous paths and flow overmultiple order larger domains. Therefore mathemat-ical models for flow in a porous media are derivedby generating volume averaged representations of theflow at smaller micro length scales [1, 2, 3]. Flow inporous media is generally governed by Darcy’s lawwhere the velocity, u, is directly proportional to thepressure gradient:

u = −Kµ

(∇p− ρg) (1)

The permeability, K, is a function of the porousstructure essentially pore diameter, porosity and tor-tuosity. However, the above equation is valid only formoderate flow rates and at higher flow rates convec-tive terms become important and should be included.

In the present study, we develop algorithms forsolving Darcy-Brinkman-Forccheimer equations forflow in porous channels. An operator split algo-rithm has been employed in the open source codeGerris which allows use of adaptive mesh refinementand time stepping. We validate the above numeri-cal implementation using an asymptotic Darcy flowin a channel and Poisseuille flow through a high per-meability channel. The developed algorithm can bereadily extended to simulate three dimensional flowswith complex geometries.

II Governing Equations

The dimensionless form of the governing equations fora non-homogeneous porous medium are given below.


Figure 1: Linear variation in Darcy flow velocity withpermeability. The theoretical stream wise velocity isUt and the numerical results are denoted by Un.

Continuity equation is given by:

∇ · u = 0 (2)

The x−direction momentum equation is given by:

1

ε

∂u

∂t+

1

ε2(u · ∇)u =

1

ρ

[−1

ε∇ (εp)

+µ

ε∇2u− µ

Ku− CF ρ√

k|u|u

](3)

and the y−direction momentum equation is given by,

1

ε

∂v

∂t+

1

ε2(u · ∇) v =

1

ρ

[−1

ε∇ (εp)

+µ

ε∇2v − µ

kv − CF ρ√

k|u| v

](4)

The second term in the above equations are due toBrinkman correction to porous media flow to includethe effect of viscous forces, third term is the lineardrag due to solid matrix resistance to the flow, andthe last term is the Forccheimer correction to includethe non-linear drag when inertial forces become im-portant.

III Numerical Formulation

An operator split algorithm has been employed forthe implicit implementation of the Darcy and For-cchiemer terms [4]. For constant permittivity flows,

Figure 2: Parabolic velocity profile for permeabil-ity K = 0.004 and µ = 0.05 in a channel withno-slip boundary conditions at the top and bottomwalls. The corresponding pressure decreases acrossthe channel linearly.

we solve for u/ε instead of u. This scaling with poros-ity, results in modified momentum equations that areidentical to Navier-Stokes, thus allowing us to employthe standard Godunov schemes for advection termsand Crank -Nicolson algorithm for viscous Brinkmanterms. The Darcy terms are modelled using an oper-ator split that results in:

∂u∗

∂t= − µ

Ku∗ (5)

which essentially results in point updates for individ-ual grid cells. A similar scheme has been used forForccheimer terms.

IV Results and Discussions

We validate the above discussed operator split al-gorithm by solving the Darcy velocity by choosingCF = 0 and not solving for the advection and theBrinkman terms. Figure 1 shows the linear varia-tion in the velocity for a specified pressure with per-meability. The theoretical and numerical curves su-perimpose thus validating the implementation of theDarcy flow. Figure 2 shows the parabolic profile withno slip boundary conditions imposed at the top andbottom walls. The initial uniform velocity profile de-velops into the theoretical fully developed parabolicprofile.

V Conclusions

The basic test cases validate the implementationof the Darcy-Brinkman-Forcchiemer equations. Thepresent code needs to be validated against strictertest cases such as natural convection in a porous me-dia [5]. Subsequently, a two phase flow would be im-plemented by modelling the saturation equation asan advection-diffusion equation [6, 7]. Further, heattransfer and phase change models would enable usto simulate evaporators of capillary pumped loops tounderstand the conditions under which depriming ofthe wick may occur.

References

[1] Bastian P., Numerical computation of multiphaseflows in porous media. Habilitation thesis, 1999.

[2] Bear J., Dynamics of Fluids in Porous Media.Dover Publications, 1972.

[3] Bear A. and D. Nield, Convection in Porous Me-dia. Springer, 2006.

[4] Versteeg H.K. and Malasekera W., An Introduc-tion to Computational Fluid Dynamics. LongmanScientific & Technical, 1995.

[5] Nithiarasu P. and Seetharamu K.N. and Sundara-jan T. Natural convective heat transfer in a fluidsaturated variable porosity medium Int. J. HeatMass Transfer, 40, 1997.

[6] Chavent G and J. Jaffre., Mathematical Mod-els and Finite Elements for Reservoir Simulations.North Holland, 1978.

[7] Tomar G., Numerical Simulations of Two PhaseFlow in a Porous Medium using Volume of FluidMethod STC Symposium, 2011.

Double diffusive convection in the Earth’s core

Venkatesh Gopinath and Binod Sreenivasan

Abstract—The present-day Earth’s dynamo is powered by therelease of light elements due to inner core freezing. Compositionalbuoyancy dominates thermal buoyancy in the Earth’s core, butbefore inner core nucleation, the geodynamo may have beendriven by purely thermal convection. A study is in progress tounderstand the combined effects of thermal and compositionalconvection in a rapidly rotating spherical system permeated bya magnetic field. The onset of convection will be investigated asan eigenvalue problem so that parameter regimes inaccessibleto direct numerical simulations can be reached. We shall theninvestigate the full nonlinear problem with Earth-like boundaryconditions for heat and composition. This study constitutes animportant step in building Earth-like dynamo models.

I. INTRODUCTION

Evidence for the existence of a geomagnetic field goes backto at least 3.5 Gyr. Estimates of the age of the inner core areon the order of 1 Gyr. The geodynamo has therefore operatedin its early stages on purely thermal convection, and later on amixture of thermal and compositional convection. Recent largeestimates of the thermal conductivity of the Earth’s core havereinforced the belief that present-day Earth’s core is largelycompositionally driven.

Double-diffusive phenomena have been studied in vari-ous contexts. However, in the context of the Earth’s core,numerical studies of the dynamo process have used equalthermal and compositional diffusivities. In this so-called ‘co-density’ formulation [1], only one scalar transport equationis solved for. A few recent studies [2], [3] deal with con-vective instabilities in thermal convection within a rapidlyrotating spherical shell. Where double diffusion is present, thebuoyancy profiles for heat and composition convection usedin these studies are not Earth-like. Moreover, no magneticfield effects have been considered. It is accepted that theEarth’s outer core convects more strongly at the bottom thanat the top. Whereas early studies have proposed that the top10 − 20% of the core may be thermally stratified, recentlarge estimates of thermal conductivity have pushed the stablestratification to about 40%. In simple terms, the ratio of thermalto compositional diffusivities in the core may vary significantlyacross different regions of the Earth’s core. Although directnumerical simulations of double diffusive convection appliedto planet Mercury are available [4], Earth-like regimes have notbeen investigated. Three-dimensional dynamo simulations withdifferent thermal and compositional diffusivities and shownthat finger-like narrow plumes penetrate into the thermallystable layer, causing an increase in magnetic field strength inthe stable layer [5] compared to dynamo models that used

Venkatesh Gopinath is a Senior Research Fellow at Centre of EarthSciences, IISc Bangalore, India. [email protected]

Dr. Binod Sreenivasan is an Associate Professor at Centre for EarthSciences, IISc, Bangalore, India. [email protected]

Project number: ISTC/EES/BS/331

the codensity formulation. These studies provide the impetusfor a systematic investigation of dynamo action with double-diffusive convection.

Linear stability analysis offer significant advantages overDirect Numerical Simulation (DNS) in Earth-like magneto-hydrodynamic (MHD) systems. One can obtain meaningfulresults at very low Ekman number E (ratio of viscous toCoriolis forces, ∼ 10−15 for the Earth’s core), which ispresently very difficult to obtain in fully non-linear simulationsbecause of computing constraints. In this study we perform alinear stability analysis of combined thermal and compositionalconvection in an applied magnetic field to obtain the criticalparameters at onset and the structure of convection.

II. GOVERNING EQUATIONS

The governing equations for the velocity U , the magneticfield B, the temperature T and the concentration C are solvedin the Boussinesq approximation. These equations are madedimensionless [5] and written as follows:

E

(∂U

∂t+ U · ∇U

)+ 2z × u +∇Π = E∇2U

+RaTPrTr

roT +RaCPrC

r

roC +

1

Pm(∇×B)×B (1)

∂B

∂t= ∇× (U ×B) +

1

Pm∇2B (2)

∂T

∂t+ (U · ∇)T =

1

PrT∇2T (3)

∂C

∂t+ (U · ∇)C =

1

PrC∇2C (4)

∇ ·U = 0 (5)∇ ·B = 0, (6)

where E is the Ekman number, RaT is the thermal Rayleighnumber, RaC is the compositional Rayleigh number, PrT isthe thermal Prandtl number, PrC is the compositional Prandtlnumber and Pm is the magnetic Prandtl number. These areour dimensionless control parameters. In order to study thegrowth of instability in the system, the initial state of thesystem is perturbed, and the perturbation variables u, b, Θ,T ′ and C ′ are solved for at onset of convection. The velocityand magnetic fields can be expressed as a sum of toroidal andpoloidal vectors:

u = ∇×Ψr + ∇×∇× Φr (7)b = ∇× Γr + ∇×∇× Ξr (8)

and with the application of the operators (∇×) and (∇×∇×)to the Navier–Stokes equation and (∇×) to the inductionequation and taking r-components of the equations, the lin-earized equations governing the perturbations are obtained.


The resulting perturbations quantities are expanded in normalmodes:

X(r, θ, φ, t) = X(r)Pml (cos θ) exp(imφ+ iωt), (9)

where X = (Ψ,Φ,Γ,Ξ, T ′, C ′) and Pml (cos θ) is the associ-

ated Legendre function of degree l and order m.

III. METHOD OF SOLUTION

In our eigenvalue problem, the eigenvalue is the frequencyω. With temporal derivatives, onset of stationary convectionarises for Re(ω) = 0 and oscillatory convection arises forRe(ω) > 0. For a range of given Rayleigh numbers Ra, a bi-section algorithm is employed to find the eigenvalue ω. The Racorresponding to the eigenvalue obtained is the critical thermalRayleigh number RacT keeping the compositional Rayleighnumber fixed (or vice versa). The pseudospectral collocationmethod is being employed in the radial (r) direction. Theresulting generalized complex eigenvalue problem is given by

AX = λBX (10)

where λ = ω and X = (Ψ,Φ,Γ,Ξ, T ′, C ′).

IV. WORK IN PROGRESS

The eigensolver is presently being benchmarked againstknown thermal convection results. A systematic study will beundertaken at low Ekman numbers where all dimensionlesscontrol parameters are kept constant except for the ratio ofthermal to compositional diffusivities. The effect of this ratioon the structure of the flow at onset is not well understood.The role of a spatially varying azimuthal magnetic field withequatorial antisymmetry (that mimics an axial dipole) will alsobe examined. Finally, the onset will be studied at small radiusratios that represent early-Earth regimes.

Apart from the linear stability calculations, a three-dimensional nonlinear simulation study is also in progress, forwhich a dynamo code is already available. Even as the linearstudy gives insights into the low Ekman number regimes whichare otherwise difficult to simulate, the problem is not steppedforward in time and the back reaction of the magnetic fieldon the flow (via the magnetic Lorentz force) is not treatedadequately. Furthermore, the magnetic field is an applied onerather than a self-generated one, which makes DNS morerealistic than linear onset analyses. The Lewis number (ratioof thermal to concentration diffusivity) used in previous DNS[4] was 10, which is much lower than the value expected inplanetary cores. In our study, we use Lewis numbers ∼ 100-1000, and thermal buoyancy profiles that mimic stratificationin the upper regions of the core. As the dynamics of the innercore is thought to affect outer core mixing [6], determiningthe thermal and solutal conditions at the inner core boundaryposes an additional challenge in our study. It is hoped that ouranalysis would eventually provide valuable data on the pastand present-day evolution of the Earth’s magnetic field.

REFERENCES

[1] Braginsky, S.I., Roberts, P.H., Equations governing convection in Earth’score and the geodynamo, Geophys. Astrophys. Fluid Dyn. 79, 197(1995).

[2] Net, M., Garcia, F., Sanchez, J., On the onset of low-Prandtl-numberconvection in rotating spherical shells: no-slip boundary conditions, J.Fluid. Mech. 601, 317-337 (2008).

[3] Net, M., Garcia, F., Sanchez, J., Numerical study of the onset ofthermosolutal convection, Phys. Fluids. 24, 064101 (2012).

[4] Manglik, A., Wicht, J., Christensen, U.R., A dynamo model with doublediffusive convection for Mercurys core, Earth Planet. Sci. Lett. 289,619628 (2010).

[5] Christensen, U.R., Wicht, J., Models of magnetic field generation inpartly stable planetary cores: applications to Mercury and Saturn, Icarus196, 1634 (2008).

[6] Alboussiere, T., Deugen, R., Melzani, M., Melting-induced stratificationabove the Earths inner core due to convective translation Nature 466,744-747 (2010).

1

Utilizing Ionic Liquid and Mixed Solvent Electrolytes to Synthesize Polymer

Electrolytes for Lithium-based Batteries

Sudeshna Sen,1 Sneha Malunavar,2 C. Gouri3 and Aninda J. Bhattacharyya4

Abstract: We have developed a polymer electrolyte prepared from room temperature ionic liquid electrolytes. The polymer electrolyte possesses ambient temperature ionic conductivity ≥ 10-3 Ω-1cm-

1, mechanical strength with elastic modulus 1 ≈ MPa and electrochemical window ≈ 5 V. Electrolyte films of (20-30)µm thickness were assembled in laboratory cells with various nanostructured cathode and anode materials for application in lithium-ion and lithium-air batteries.

I INTRODUCTION

Over the last few decades, polymer electrolytes have been demonstrated as an important alternative to molecular liquid electrolyte. Polymer electrolytes exhibit compliable mechanical property compared to crystalline compounds and may also exhibit high ambient temperature ionic conductivity. All these combined together make polymer electrolytes very promising for prospective electrochemical devices. In polymer electrolyte, the polymer plays the role of a solid solvent. Polymers with different chemical compositions, architectures are used to completely or partially dissolve salts containing ions such as Li+ /Na+ / H+ salts. However, optimization and theoretical comprehension of material's ion transport mechanism is a non trivial issue. This is primarily related to the extensive spatial and temporal heterogeneity of the polymer host which significantly influences ion transport and in general the electrolyte’s mechanical and electrochemical properties. The focus of the present work is to synthesize cross-linked

polymer electrolytes from room temperature ionic liquids (Scheme 1). The motivation here is to employ them in lithium-ion batteries as an electrolyte and separator. We also wish to theoretically investigate the extent of influence of the host complexities on the ion transport in cross-linked polymer electrolytes. This will be achieved via modeling of experimental data using concepts based on statistical mechanics. The motivation here is to obtain various relaxation processes were analyzed using time-temperature scaling principles. The experimental investigations will include infrared spectroscopy, Brillouin scattering, and impedance spectroscopy. We summarize here the work done under this project.

Scheme 1: Schematic representation of the proposed gel polymer electrolyte

1Sudeshna Sen is a JRF at SSCU, IISc, Bangalore, India, [email protected] 2Sneha Malunavar, PA at SSCU, IISc, India, [email protected] 3C. Gouri is Scientist SG & Head, Lithium-ion Electrodes Section, Lithium-ion and Fuel Cell Division (LFCD) VSSC, Thiruvanthapuram 695022, India, [email protected] 4Prof. Aninda J. Bhattacharyya is an Associate Professor at SSCU, IISc. Bangalore 560012, India, [email protected]


2

II PROPOSED WORK AND GOALS A. Cross linked gel polymer electrolyte: Gel polymer electrolytes are attractive alternatives to liquid electrolytes in several electrochemical devices including lithium-based batteries. Gels display high ambient temperature ionic conductivity and highly compliable mechanical property. The main motivation for the usage of gels is mainly targeted towards the enhancement in energy density of batteries. Conventional gel polymers (polymer electrolyte + liquid solvent) do not perform well as their performance deteorates rapidly due to ageing factors leading to very poor battery performance. Thus, there is considerable need to design gel polymers with superior electrochemical and mechanical properties using newer methods and materials. We demonstrate here newer forms of multifunctional gel polymer electrolytes using low melting point solids such as room temperature ionic liquids. Room temperature ionic liquids (RTILs) have been extensively demonstrated as a viable alternative electrolyte in lithium-ion battery applications due to their high thermal stability, ionic conductivity and wide electrochemical voltage window. RTILs have also been used as a medium for synthesis of diverse compounds. In our studies, RTIL was used as a medium to synthesize gel polymer electrolytes. Earlier acrylonitrile was polymerized inside ionic liquid medium to obtain crosslinked gel system [ref 3]. Although, the resulting polymer electrolyte displayed high ionic conductivity, it’s the mechanical properties were poor. In this work we explore the possibility of improving the mechanical property of the gel polymer electrolyte by inclusion of more than one monomer to synthesize cross linked blend polymer-like electrolyte. Free radical polymerization of acrylonitrile and methacrylate (MPTMS) monomers was carried out in pyrolidinium ionic liquid to synthesize

novel crosslinked gel polymer electrolytes. Thermal stability, mechanical property and ionic conductivity of the synthesized cross linked blend-like polymer gel system were studied. From the thermogravimmetry analysis (TGA) data it was observed that this new system was thermally stabile up to 350 °C [figure 3]. At temperatures higher than 350 °C, significant weight loss ≈ 80 % (350-475 oC) was observed. However, the weight retention (> 475 oC) of the blend-like polymer was higher than the pure ionic liquid suggesting higher thermal stability.

Fig 1: Thermogravimetric analysis (above) and differential scanning caloriemetry (bottom; only relevant temperature regime shown) of cross-linked gel samples vis a vis the pure ionic liquid The ionic conductivity is measured by ac-impedance spectroscopy in the frequency range from 1 Hz to 1 MHz (figure 2). The

3

conductivity of this system was found to be of the order of 10-3 Ω-1cm-1 in the temperature range -20 to 60 °C, which is highly beneficial for operation in battery applications. The acrylate monomer was incorporated to improve mechanical property of PAN/IL system. The mechanical property of the blend-like gel polymer electrolytes were studied using static and dynamic rheology measurements (figure 3).

Fig 2: Temperature dependent ionic conductivity of crosslinked polymer gel electrolyte The polymer network structure in the gel polymer electrolyte was characterized by static rheology measurement. In static rheology measurement, the viscosity of the system was monitored as a function of shear rate. The decrease in viscosity with increasing shear rate indicated presence of a connected network in the gel. Along with the network formation, the viscoelastic behavior was also studied by dynamic rheology measurement. Cyclic voltammetry were also carried out to estimate the electrochemical voltage window of the synthesized materials. It was observed that the material is electrochemically stable in the potential range from -0.5 to 5 V. This makes it highly suitable for battery applications with high voltage electrode materials such as polyanions phosphates and silicates.

Fig 3: Photographs of the polymer elecrolytes at various monomer ratios (above). Dynamic rheology measurement of cross-linked blend-like gel polymer electrolyte (bottom) Additionally, the new cross-linked systems were galvanostatically cycled with LiFePO4 as a cathode material. Though the as-synthesized polymer electrolyte showed electrochemical performances, the applicability of the cross-linked systems in battery application is presently restricted by the inability to cast thin films (≈ 30-100 µm). It is felt that further improvement in the mechanical property of the gel polymer is expected to improve overall physical properties and device performance.

Fig 4: Electrochemical stability window of the gel polymer electrolytes

4

B: Mixed solvent based gel polymer electrolyte: Plastic crystals possess rotational disorder in their crystalline lattice structure. The molecule transformed to this rotator phase via solid-solid phase transition below their melting point. Plastic phase is achieved via solid-solid phase transition(s) below their melting point. Depending on the plastic crystalline compound, the role of rotators vary in their influence on the ionic conductivity. In insulating polar organic plastic crystalline compounds such as succinonitrile, the presence ofrotors significantly influence the effective conductivity of the electrolyte (SN + ionic salt, NaX, LiX). Succinonitrile consists of three geometrical isomers, two gauche and one trans above the normal to plastic transition temperature (Tnp). These two isomers exist in equilibrium in plastic phase and transform from one to another form via 120o rotation around C-C bond. The trans phase has been proposed to act as impurity phase which reduces activation energy barrier for ion transport. Such fenomenon of solvent dynamics, playing a important role in ion transport has also been obsereved in various ionic liquid, an important subclass of ionic plastic crystalline materials. Among several ionic liquid systems based on imidazolium, piperidinium, alkyl ammonium based ionic liquids, N-methyl-N-butyl pyrrolidinium bis(trifluoromethane sulfonyl) imide (PY14TFSI) has been well studied ionic liquid for battery applications it’s owing to several attractive physical properties. However the intrinsic ionic conductivity is lower compared to other ionic liquid.

We discuss here an electrolytic system where a mixture of solvents comprising of a plastic crystalline compound and RTIL has been employed to synthesize the electrolyte. Incorporation of small amount of ionic liquid into succinonitrile matrix resulted in a highly conducting electrolyte compared to both IL-

LiX or Succinonitrile-LiX systems. Similar system has been earlier reported in dye sensitized solar cell lead to an increase in efficiency of dye DSSC. The ionic liquid decreases melting point of the SN while keeping normal-plastic transition temperature unchanged. The room temperature ionic conductivity and cyclic voltammetry have been performed in order to investigate applicability of the mixed solvent in lithium battery application and a new type of gel polymer electrolyte has also been synthesized using this solvent matrix in this study.

Scheme 2: Schematic representation of the synthesis of gel polymer electrolyte using SN and IL as solvents Prior to prepare the above mentioned mixed plastic crystalline solvent, succinonitrile was sublimed twice to reduce impurity. In order to prepare mixed solvent based electrolytic system, initially SN-LiTFSI system was synthesized by dissolving required amount of lithium salt, lithium bis(trifluoromethane sulfonyl) imide (LiTFSI) in succinonitrile and stirred at 60 0C. The salt concentration was kept constant (0.5 M) for all the studies. The ionic liquid, 1-Butyl-1-Methyl bis(trifluoromethane sulfonyl) imide (PY14TFSI) was incorporated in the SN-LiTFSI system in three different molar ratios (with respect to SN) and stirred at room temperature to get homogeneous liquid electrolytic system. Three different molar ratios of SN to IL studied are 20:1, 10:1 and

5

5:1 keeping LiTFSI concentration same (0.5M). For the lower ionic liquid

Fig 5: Ionic conductivity of the gel polymer electrolytes using SN+IL as solvents concentration i.e. for the composition [SN:IL 20:1]-0.5M LiTFSI, an waxy appearance, characteristic to SN was observed at room temperature. But as we increase ionic liquid concentration, i.e, for the compositions [SN:IL 10:1]-0.5M LiTFSI and [SN:IL 5:1]-0.5M LiTFSI, a liquid appearance was observed at room temperature. This shows a clear indication of reduction of melting point of succinonitrile upon addition of small amount of ionic liquid.

Fig 6: Electrochemical stability window of the gel polymer electrolytes using SN+IL as solvents

The room temperature conductivities of mixed solvents with various IL concentrations, keeping salt concentration same, is presented in the table 1. Table 1: Room temperature conductivities of SN-IL systems of different solvent ratio

Solvent composition Conductivity (Ω-1cm-1)

SN-0.5M LiTFSI 2.4 x10-4

[SN:IL 20:1]-0.5M LiTFSI

2.3x10-3

[SN:IL 10:1]-0.5M LiTFSI

5.9 x10-3

[SN:IL 5:1]-0.5M LiTFSI 4.5 x10-3

IL 0.5M LiTFSI 2.5 x10-3 The above room temperature ionic conductivity data clearly indicates that room temeprature conductivity of the liquid electrolyte strongly depends on molar ratio of succinonitrile and ionic liquid. With increasing ionic liquid concentration, conductivity increases up to a maximum value (6.9 x10-3 Ω-

1cm-1 for the composition SN:IL 10:1). On further increasing ionic liquid concentration, i.e for the molar ratio 5:1, conductivity decreases. As the lithium salt concentration was kept constant, the effect of salt on the ionic conductivity has not been taken into account. Instead of the large variation in conductivities, all of the compositions of the plastic crystal mixed solvent (including the composition [SN:IL 5:1]-0.5M LiTFSI) have higher ionic conductivities compared to the pristine SN-LiTFSI and IL-LiTFSI systems. The composition, [SN:IL 10:1]-0.5M LiTFSI exhibits highest conductivity (5.9 x10-3 Ω-1cm-

1), almost comparable to widely used liquid EC/DMC based electrolyte. In order to investigate the reason behind this significant improvement in ionic conductivity using the mixed solvent compared to both pristine IL an succinonitrile, several factors like viscosity, solvent dynamics and ion association effect

6

should be taken into account carefully. Reduction of viscosity and ion association in SN-IL-LiTFSI systems compared to pristine IL-LiTFSI and SN-LiTFSI systems may be reason behind the improvement in ionic conductivity in the proposed mixed solvent. Besides, the decrease in ionic conductivity at higher ionic liquid concentration (for the composition [SN:IL 5:1]-0.5M LiTFSI) compared to other compositions, indicates possibility of increasing ion association in that concentration compared to lower concentration of ionic liquid. To explain the variation in ionic conductivities with ionic liquid concentration, the effect of ionic liquid incorporation on succinonitrile dynamics and nature of ion pairing in this solvent should be studied in more details The cyclic voltammetry was carried out for the highest conducting composition to monitor electrochemical window of the mixed plastic crystal solvent and represented in figure 6. The plot clearly indicates a wide electrochemical voltage window for the solvent from 0.5V to 4.7V. In this plot a strong cathodic peak at around 0.2 V and anodic peak at -0.5 V were observed for characteristic lithium striping and lithium plating processes. No further electrochemical degradation of electrolyte is observed up to the voltage 4.5V. The stability of the solvent at upper voltage region is close to ionic liquid PY14TFSI-0.5M LiTFSI system (5V) and thus can be applied for high voltage cathode materials.

III CONCLUSIONS We have demonstrated here a novel class of gel polymer electrolytes for possible applications in lithium-based batteries. The ion transport mechanism (not discussed here, c/f proceeding paper 2013 and progress report 2014) is affected by both the dynamics of the polymer and ion solvation. As other types of salts viz. magnesium, sodium can be easily incorporated in the synthesis, the gel polymer

electrolytes discussed here will also be promising for other battery chemistries.

REFERENCES [1] Das, S; Bhattacharyya, A. J. Phys. Chem. Lett. 2012, 3, 3550−3554 [2] Patel, M; Bhattacharyya, A. J. Phys. Chem. B 2010, 114, 5233–5240 [3] Patel, M; Gnanavel, M; Bhattacharyya, A. J. J. Mater. Chem. 2011, 43, 17419-17424 [4] S. Sen, Bhattacharyya, A. J. 2014, unpublished

SPARSITY-BASED CROSS-TERMS-SUPPRESSED TIME-FREQUENCY DISTRIBUTION OFMULTI-COMPONENT LINEAR FREQUENCY MODULATED SIGNALS

Shreyas Hampali, Subhankar Ghose, and Chandra Sekhar Seelamantula

ABSTRACTWe address the problem of estimating the parameters of multiplelinear frequency-modulated (LFM) chirp signals. We consider sig-nals corrupted by additive white noise and develop a new techniquebased on the properties of the instantaneous autocorrelation (IA)sequence of multiple LFM chirp signals. The IA matrix, whoserows are the IA sequences obtained at different time instances ofthe signal is introduced. We show that, by obtaining a sparse repre-sentation of the rows of IA matrix with a dictionary of sinusoids, theinherent cross terms are eliminated. Further, by obtaining a sparserepresentation of the columns of the IA matrix, the instantaneousfrequencies of the component chirps are estimated to obtain a highresolution cross-term suppressed sparse WVD (CTSS-WVD).Thecomponent chirps that manifest as straight lines in CTSS-WVDare estimated using an iterative line parameter estimation method.We show simulation results for four-component chirp signal withvarying SNR levels. We also validate the technique by suppressingcross-terms in a bat echolation signal.

Index Terms—Instantaneous autocorrelation sequence, Am-biguity function, sparsity, LASSO, Linear frequency modulatedchirps

I. INTRODUCTION

L INEAR frequency modulated (LFM) signals are a class ofnon-stationary signals widely used in applications such as

radar, sonar and wireless communication [1]. In all these casesthe transmitted signal undergoes a time-varying phase shift dueto relative motion between transmitter and the receiver. Further,due to presence of multiple targets in the radar case and multiplepaths in the communication case, the received signal is a sum ofLFM signals and analysis of such multicomponent LFM signalsreveal properties of targets or communication paths. The multi-component LFM signals also arise in applications such as electroniccounter measure systems for pulse-Doppler radars, where LFMsignals from various transmitters need to be analyzed. Due to itswide applications the study of multiple LFM signals in noise hasreceived considerable interest during the past two to three decades[2]–[5].

I-A. Contributions of this paperIn this paper, we propose a technique for obtaining cross-

term suppressed WVD (CTS-WVD) by sparsifying the discreteambiguity function for multi-component LFM chirp signals in whitenoise. Using CTS-WVD, we introduce high-resolution, noise andcross-term suppressed sparse WVD (CTSS-WVD) that is suitablefor estimating multi-component chirp parameters in low SNRs (0

This work was supported by the Indian Space Research Organization- Indian Institute of Science Space Technology Cell (Project:ISTC/EEE/CSS/0293).Authors email : [email protected], [email protected],[email protected] : +91 80 2293 2695

dB). The estimation of the chirp parameters in the CTSS-WVD iscomputationally far less complex than Hough transform. The CTS-WVD is shown to have lower variance, better resolution in the time-frequency (TF) distribution and comparable cross-term suppressionability than other cross-term suppression methods.

II. AMBIGUITY FUNCTION OF MULTICOMPONENTLFM CHIRP SIGNAL

The instantaneous autocorrelation (IA) sequence of a signal s[n]is defined as,

IA(n, k) = s∗[n− k]s[n+ k]. (1)

The ambiguity function (AF) of a signal is given in terms of IAsequence as

M(τ, θ) =∑n∈Z

IA(n, τ/2)ejθn (2)

For a mono-component LFM chirp signal, the AF is given by,

M(τ, θ) = b20eja0τ

∑n

eja1τnejθn (3)

Thus, for a given τ , the AF of an LFM chirp is the Fouriertransform of a sinusoid of frequency a1τ . In case of multiplechirps, equation (2) can be divided into two terms as cross-termcomponent, Mc(τ, θ) and auto-term component, Ma(τ, θ). For atwo component LFM chirp signal the AF is given by,

M(τ, θ) =

2∑i=1

b2i eja0iτ

∑n

eja1iτnejθn

︸︷︷︸Ma(τ,θ)

+∑n

ej(C01(τ)+C11(τ)n+C21n2)ejθn

+∑n

ej(C02(τ)+C12(τ)n+C22n2)ejθn

︸︷︷︸Mc(τ,θ)

, (4)

where,

C0i(τ) = a01τ

2+ a02

τ

2+ (−1)i

1

8(a11 − a12)τ2,

C1i(τ) = a11τ

2+ a12

τ

2+ (−1)i(a01 − a02),

C2i = (−1)i1

2(a11 − a12)

It can be verified that in the scenario a11 6= a12, the cross-terms are Fourier transforms of LFM chirps. The auto-terms arealways sinusoids with frequency a1iτ . When the chirp rates a1i

of the component chirps are equal, the cross terms are sinusoidsof frequency a1iτ ± (a01 − a02). In the presence of white noise,the signal to noise cross-terms manifest as random signal. Thus,the problem of cross-term suppression is that of extraction of lowfrequency sinusoids in the ambiguity function domain.


III. CROSS-TERM SUPPRESSION USING A SPARSEREPRESENTATION FOR AMBIGUITY FUNCTION

We introduce the instantaneous autocorrelation (IA) matrix ofthe signal s[n] and denote it by A. The size of A is N

2×N and

the (i, jth) element is given by,

Ai,j = s∗[j − i] · s[j + i]. (5)

The AF can be expressed in terms of IA matrix as,

M(2τ, θ) = F B; B = AT τ, θ ≥ 0 (6)

where F is the discrete Fourier transform matrix.The rows of the IA matrix contain signal auto-terms, signal-to-

signal cross-terms and signal-to-noise cross-terms. The signal-to-signal cross-terms are LFM chirps when chirp rates are unequaland the signal-to-noise cross-terms are random signals. The signalauto-terms being the only sinusoids in this scenario, the cross-termsare suppressed by identifying the sinusoids in the IA sequence.The sinusoids can be identified by estimating its frequencies usingvarious parameter estimation techniques such as estimation ofsignal parameters via rotational invariance techniques (ESPRIT)[11] and multiple signal classification (MUSIC). However, suchtechniques require the model parameter which is the number ofsinusoids present in the signal and is unknown a priori. Further, thepresence of signal dependent cross-terms deviates the IA sequencefrom the signal model defined by sinusoidal parameter estimationmethods.

In our approach, we use sinusoidal dictionary based least abso-lute shrinkage and selection operator (LASSO) formulation definedin [12].

III-A. Cross-term suppression problem formulation usingLASSO in ambiguity function domain

The cross-terms in the rows of IA matrix in (5) for unequal ratechirp signals are separated by identifying the sinusoids, which arethe auto-terms in the IA sequence. A set of sinusoids in an IAsequence along with their magnitudes are estimated using LASSOformulation with dictionaries of sinusoids each of size P denotedby X(i) and X(r), respectively. The (i, j)th element of the Mn ×P, P ≥Mn dictionary matrices is given by,

X(i)i,j = sin

(2πi(j − P/2)

P

),

X(r)i,j = cos

(2πi(j − P/2)

P

),

j = 0, 1, 2, · · · , P − 1;

i = 0, 1, 2, · · · ,Mn − 1. (7)

The nth column vector of matrix B in (6) has Mn = N − 2nnon-zero elements in it as can be verified from the construction ofIA matrix in (5). This Mn element vector is denoted by Bn. AsBn is complex the auto-term identification problem is formulatedin the LASSO framework in two stages as,

minwn

‖ X(i)wn − imag(BnT) ‖22 +λ ‖ wn ‖1,

minwn

‖ X(r)wn − real(BnT) ‖22 +λ ‖ wn ‖1 . (8)

The two stages correspond to the imaginary and real parts of Bn.The optimization problem in (8) identifies the auto-terms in the nth

column of the matrix B. The performance of this two stage processis improved by retaining only those frequencies for the dictionaryin stage 2 whose amplitudes are non-zero in stage one. As the

coefficient vector w∗n denotes the auto-terms in the IA matrix, thecross-term suppressed sparse AF (CTSS-AF) is obtained as,

CTSS-AF = [w∗1,w∗2, · · · ,w∗N

2]

When the formulation in (8) is used for the case of a signalconstituted of equal rate LFM chirps, the cross-terms fail to getsuppressed and appear along with the auto-terms. This is because,the cross-terms too appear as sinusoids when rates of componentchirps are equal.

The cross-terms in case of equal rate LFM chirps have frequen-cies higher than that of auto-terms. As explained in the SectionII when a11 ≈ a12 in (4), cross-terms appear along coordinates(τ, a11τ ± (a01 − a02)

)in the discrete AF domain, whereas the

auto-terms are present at coordinates (τ, a11τ). Thus, for small τ ,the cross-terms appear on either side of the frequency origin asseen from Figure 2.

The cost function (8) imposes equal penalty (λ) on all thecoefficients in vector wi. Thus, we reformulate the auto-termidentification problem in (8) to encompass both the scenarios ofequal and unequal chirp rates and is given by,

minwn ‖ X(r)wn − real(ATn ) ‖22 +λ ‖ Dwn ‖1, (9)

where D ∈ Rp×p,

Di,j =

0 if i 6= j,

e(i−p/2)2

γ2 if i = j.

where γ is the penalty controlling factor. The diagonal penaltymatrix D satisfies the condition of imposing higher penalty forfrequency components that are away from zero. The penalty matrixD is motivated by the Choi-Williams kernel, Φ(τ, θ) used for cross-term suppression and is given by,

Φ(τ, θ) = e−α(τθ)2 .

It should be noted that, in case of wide-band (large a11) equalrate chirp signals, for large τ , (a11τ ± (a01 − a02))) is positiveif a11 > 0 and negative if a11 < 0. Hence, in this scenario thecross-terms that appear on the same side of the frequency originare not suppressed by the cost function (9).

Figure 1 shows a normalized slice of the CTSS-AF obtained us-ing the general formulation in (9). We observe that the cross-termswhich appear as sinusoids are suppressed by the new formulation.Figure 3 shows the AF of a three chirp signal obtained using (6) andFigure 4 shows the corresponding cross-term suppressed discretesparse AF.

−3 −2 −1 0 1 2 30

0.2

0.4

0.6

0.8

1

FREQUENCY

Fig. 1: A slice of the sparse-AF for two equal rate chirpsignals each at SNR = 0 dB.The sparse-AF is obtained us-ing cost function (9). Thecross-terms are suppressed.

τ

θ

50 100 150 200 250−2

−1

0

1

2

0

0.2

0.4

0.6

0.8

1

Cross−terms

Auto−term

Fig. 2: Ambiguity function of twoequal rate chirp signals with param-eters a01 = 0.2513, a02 = 0.6283,a11 = a12 = 7.33× 10−4.

τ

θ

20 40 60 80

−50

−25

0

25

50

0.2

0.4

0.6

0.8

1

Fig. 3: Ambiguity function ofa three component LFM chirpsignal each at high SNR

τ

θ

20 40 60

−50

−25

0

25

50 0

0.2

0.4

0.6

0.8

1

Fig. 4: CTSS-AF of a three com-ponent LFM chirp signal each athigh SNR

The length of the AF sequence in nth row of the IA matrix isN −2n. The ability of the cost function (9) to provide an accuratesparse representation of the signal is a function of the signal lengthas shown in [12]. Thus, for large τ , when the IA sequence lengthis small, the signal auto-terms are not consistently identified asverified in Figure 4.

Denoting the minimizer of (9) by w∗n, the sparse approximationof Bi denoted by Bi is given by,

Bn = (X(r) + jX(i))w∗n (10)

The cross-term suppressed IA (CTS-IA) matrix denoted by A isobtained by arranging the vectors Bi row-wise as,

A =[B1, B2, · · · , BN

2

]T(11)

Similar to AF, the Wigner-Ville distribution (WVD) of a signalis directly obtained from its IA matrix A as,

WVD(n,ω

2

)=∑k

IA(n, k)ejωk = 2 · real(F A

)(12)

By utilizing the cross-term suppressed IA matrix A, the cross-termsuppressed WVD is obtained as 2 · real

(F A

).

Figure 5 shows cross-term suppressed WVD obtained fromdifferent techniques in no noise scenario. The Choi-Williamsmethod which uses a low pass filter in the AF domain is knownto compromise on the TF resolution of the signal. The signaldecomposition based technique [7] which uses Gabor atoms isfree of cross-terms, however the TF resolution of the signal iscompromised as verified from Figure 5b. The proposed methodon the other hand has better TF resolution and also achieves crossterm suppression as observed in Figure 13, which shows a sliceof the TF distributions in Figure 5 along the dotted line. Theparameters considered in the simulation are a01 = 0.2513, a02 =0.7504, a11 = a12 = 7.36× 10−4, p = 1024, N = 512.

Figure 6 compares the performance of the proposed methodwhen a signal is constituted of four LFM chirp signal in a noisyscenario. The proposed method is seen to have better TF resolutionthan Gabor atoms based signal decomposition method [7] and a bet-ter cross-term suppression ability than Choi-Williams method. Theparameters considered are a01 = 0.2513, a02 = 0.5027, a03 =0.9425, a04 = 1.1310, a11 = 7.36 × 10−4, a12 = 18× 10−4,a13 = −6.14 × 10−4, a14 = 7.36 × 10−4, p = 1024, N = 512and SNR1 = SNR2 = SNR3 = SNR4 = 0 dB

IV. SPARSE WVD AND MULTI-COMPONENT CHIRPSIGNAL PARAMETERS ESTIMATION

In this section, we introduce cross-term suppressed sparse WVD(CTSS-WVD) using A. The CTSS-WVD is then used to esti-mate chirp parameters using an iterative line parameter estimation

TIME

FR

EQ

UE

NC

Y

100 200 300 400 500

50

100

150

0.2

0.4

0.6

0.8

1

(a) Wigner-Ville distribution

TIME

FR

EQ

UE

NC

Y

100 200 300 400 500

50

100

150 0

0.2

0.4

0.6

0.8

1

(b) TF distribution using match-ing pursuit method [7]

TIME

FR

EQ

UE

NC

Y

100 200 300 400 500

50

100

150

0.2

0.4

0.6

0.8

1

(c) TF distribution using Choi-Williams kernel method

TIME

FR

EQ

UE

NC

Y

100 200 300 400 500

50

100

150

0.2

0.4

0.6

0.8

1

(d) TF distribution using pro-posed method

Fig. 5: Comparison of the cross-term suppression methods for twoequal rate chirp signal in no noise scenario.

TIME

FR

EQ

UE

NC

Y

100 200 300 400 500

50

100

150

0.2

0.4

0.6

0.8

1


TIMEF

RE

QU

EN

CY

100 200 300 400 500

50

100

150 0

0.2

0.4

0.6

0.8

1

(b) TF distribution using match-ing pursuit method [7]

TIME

FR

EQ

UE

NC

Y

100 200 300 400 500

20

40

60

80

100

120

140

0.2

0.4

0.6

0.8

1

(c) TF distribution using Choi-Williams kernel method

TIME

FR

EQ

UE

NC

Y

100 200 300 400 500

50

100

150

(d) TF distribution using pro-posed method

Fig. 6: Comparison of the proposed cross-term suppression methodfor a four component chirp signal each with SNR = 0 dB

method. The CTSS-WVD is obtained by sparsifying CTS-WVDintroduced in Section III and has improved frequency resolutionaiding in accurate estimation of parameters of component chirpsignals.

The nth column of the CTS-IA matrix which we call as the IAvector is denoted by zn. The auto-term component in the IA vector

is a sum of complex sinusoids given by,

zn[k] =∑i

b2i ej2(a0,i+a1,in)k. (13)

We observe that the frequency of each sinusoid is twice theinstantaneous frequency of the corresponding chirp at time instantn. Thus, a sparse representation of the IA vector zn similar to(8), with a dictionary of sinusoids as defined in (7), providesinformation of the sinusoidal frequency content in the vector whichin turn is an estimate of the IFs of the component chirps. Thetwo cost functions for obtaining the sparse representation of zn isprovided below for clarity,

minwn

‖ X(i)wn − imag(zn) ‖22 +

p∑i=1

‖wn‖1, (14a)

minwn

‖ X(r)wn − real(zn) ‖22 +

p∑i=1

‖wn‖1, (14b)

where X(i) and X(r) are the sine and cosine dictionary matrix.As discussed in Section III, (14b) is used for suppressing spuriousfrequencies detected by optimizing (14a).

The sparse representation is obtained for each column of CTS-IA matrix and the minimizer of (14) is denoted by w∗n for columnn. The CTSS-WVD matrix is obtained from w∗n as,

CTSS-WVD = [w∗1,w∗2, · · · ,w∗N

2] (15)

Figure 7 shows the WVD of a four chirp signal. The SNR of eachof the component chirps is 4 dB. The CTSS-WVD obtained usingthe above described procedure is shown in Figure 8. A slice of theTF distributions along the dotted line in Figure 7 and 8 is shown inFigure 9. The resolution of CTSS-WVD is better than WVD due tothe the sparsity constraint used in the formulation. The cross-termsare also suppressed thus providing better estimate of the IF of thecomponent chirps.

IV-A. Detection and estimation of line parameters in sparseWVD

‘ We develop a new technique termed as iterative line parameterestimation method (ILPEM) that iteratively estimates the slope andintercept parameters of intersecting lines in a sparse distribution.ILPEM is an alternative to the conventional Hough transform andhas lower computational complexity.

Let the set of frequencies present at any time instant n, obtainedfrom CTSS-WVD be denoted by Γn. A single chirp scenario is firstconsidered and is later extended to multiple chirps at the end ofthis section. The problem of line parameter estimation in sparse TFplane with least squares cost function is written as,

mina,b,

η1∈Γ1,η2∈Γ2,··· ,ηN∈ΓN

N∑n=1

(ηn − (a · n+ b))2 , (16)

where, at each time instant n, an optimal frequency is chosen formthe set Γn such that the frequencies at all time instants are co-linearand is given by the line parameters (a, b). We propose a two stepsuboptimal iterative solution to the (N + 2)-dimensional searchproblem by assuming that the initial value of the line parametersis close to the minimizer.

η(i)n = arg min

ηn∈Γn

(ηn − (a(i−1) · n+ b(i−1))

)2

; (17a)

n = 0, 1, 2, · · · , N − 1

a(i), b(i) = arg mina,b

N∑n=1

(η(i)n − (a · n+ b)

)2

, (17b)

where a(i), b(i), η(i)1 , η

(i)2 · · · , η

(i)N represent the optimization pa-

rameters at ith iteration.The initial value of the line parameters in (17a) denoted by

(a(0), b(0)) is obtained using the frequency marginal which isshown in Figure 8. The frequency corresponding to the maximumvalue of the marginal is used as b(0). In our simulation, the valueof a(0) is set to 0 as no information about the chirp rate is availableinitially. For the case of multiple chirp lines, the frequencies η(i)

n

of the set Γi corresponding to a line as obtained from (17a) areremoved from their corresponding sets and the two-step iterativeprocedure is repeated.

We now evaluate the performance of the sparse WVD alongwith ILPEM (sparse-WVD-ILPEM) by estimating the parametersof a mono-component chirp signal.To facilitate comparison, thechirp parameters are also estimated by applying Hough transformto sparse-WVD (sparse-WVD-Hough). The case of a single chirpsignal in noise enables performance evaluation of sparse-WVD andILPEM in separation as the cross-term suppression described insection III is not required.

For simulations we consider signal length N = 300 and thenumber of sinusoids in the dictionary P = 750. Figure 10a and10b shows the variance of the chirp parameter estimates usingvarious techniques. The resolution of the transformed variablesin the Hough transform (θ, ρ) is set to 0.1. The performanceof the ILPEM is comparable with the Hough transform basedtechnique (sparse-WVD-Hough) for SNR > −2 dB and is betterfor SNR < −2 dB. A comparison with de-chirping based methoddiscussed by Peleg and Porat in [10] with 4096-point discreteFourier transform (DFT) shows that the threshold SNR of thesparse-WVD-ILPEM technique is about 4 dB lesser than the de-

TIME

FR

EQ

UE

NC

Y

100 200 300

50

100

150

200

250

300

350

0.2

0.4

0.6

0.8

1

Fig. 7: WVD of a four compo-nent LFM chirp signal each atSNR = 4 dB.

TIME

FR

EQ

UE

NC

Y

100 200 300

50

100

150

200

250

300

3500

0.2

0.4

0.6

0.8

1

Fig. 8: CTSS-WVD for a fourcomponent LFM chirp signaleach at SNR = 4dB along withfrequency marginal used for ini-tializing the line parameters. Thepeak of the frequency marginal isused to initialize the iterative lineparameter estimation method.

0 100 200 3000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Frequency

(a) WVD

0 100 200 3000

0.2

0.4

0.6

0.8

Frequency

Auto−terms

(b) CTSS-WVD

Fig. 9: A slice of the WVD and CTSS-WVD along the dotted lineshown in Figure 7 and 8

chirping method. Further, the proposed method is consistent withCRLB for SNR > −2 dB.

The advantages of the ILPEM over Hough transform are threefold. 1)The estimates obtained using the ILPEM have lower vari-ance than Hough transform based estimates. 2)For an N×N sparsematrix, the computational complexity of ILPEM is O(N2) (linearsearch in (17a) has a complexity of O(N)), whereas the complexityof Hough transform is O(N3), if the transformed space also has thedimension N ×N . 3) ILPEM has the line-fitting property and candetect lines in noisy scenarios. The Hough transform formulationyields poor results when the observations are not co-linear.

The iterative line parameter estimation method converges to localminimas of (16) when the distribution is noisy and less sparse. Atlow SNRs, the sparse-WVD is noisy and the estimates of chirpparameters are inaccurate as observed in Figures 10a and 10b.Simulation results show that the iterative line parameter estimationmethod converges to minima in less than 15 iterations for signallength N = 300, dictionary size P = 375 and SNR >= −2 dB.

V. PERFORMANCE EVALUATIONWe evaluate the robustness of the proposed algorithm by consid-

ering a four component chirp signal with varying SNR levels. Theperformance is compared with two other methods namely Wigner-Hough transform discussed by Barbarossa in [9] and producthigher order ambiguity function (PHAF) based method proposed byBarbarossa et al. in [13]. We obtain CTSS-WVD of a bat echolationsignal and prove that the technique efficiently suppresses cross-terms in real scenarios.

The multi-component signal considered is same as in Figure6 whose parameters are given in Section III. The size P of thesinusoidal dictionary defined in (7) is 750. For the PHAF method,four different lags are considered as discussed in [13] with 2048-point DFT. For Wigner-Hough based method, the size of thetransformed domain is 256 × 256. At low SNR, the probabilityof detecting a chirp line in a sparse-TF distribution or in theWigner-Hough domain decreases. However, such false detectionscan be identified by comparing the sum of squared errors asobtained in cost function (17b) with a threshold. Deriving anoptimal detector for detecting such false alarms is beyond the scopeof this work and hence, in this paper we compare the estimatedparameters with the ground truth and declare as detected if thedeviation is within 10% of its original value (oracle). We consider200 Monte-Carlo simulations to obtain variance of the estimatedchirp parameters. Figures 11 and 12 show the performance of theproposed method for each of the detected component chirp signalsand compare it with other techniques for different SNR levels. Thechirp parameter estimates obtained using CTSS-WVD is observedto have lower mean-squared-error (MSE) than Wigner-Hough orPHAF estimates for three component chirps (chirps 1,2 and 4). Theerror in estimation of chirp rate propagates in the PHAF methodand affects the carrier frequency estimates as discussed in [13]. Thecarrier frequency estimates using CTSS-WVD does not suffer fromerror propagation and hence has lower MSE than PHAF techniqueas seen from Figure 12. The average probability of detection (PD)of all the component chirps is shown in Figure 14. The Wigner-Hough method is benefited from the coherent integration and hasPD = 1 for large SNRs. The PD of the proposed method is morethan 0.95 for all SNRs and decreases with SNR as the iterative lineparameter estimation method converges to local minimas.

Figure 15 shows the TF distributions of a bat echolation signal1.The bat echolation signal used here has deviations from the linear

1The authors wish to thank Curtis Condon, Ken White, and Al Feng ofthe Beckman Institute of the University of Illinois for the bat data and forpermission to use it in this paper.

0 5 10

−60

−40

−20

0

20

SNR (dB)

VA

RIA

NC

E (

dB)

CRLBSPARSE−WVD−ILPEMDECHIRPINGMETHOD [8]SPARSE−WVD−HOUGH

(a) Comparison of carrier fre-quency estimates.

−4 −2 0 2 4 6 8 10

−100

−80

−60

−40

−20

SNR (dB)

VA

RIA

NC

E (

dB)

CRLBDECHIRPINGMETHOD [8]SPARSE−WVD−ILPEMSPARSE−WVD−HOUGH

(b) Comparison of chirp rate es-timates.

Fig. 10: Chirp parameter estimates of a mono-component LFMchirp in noise. The sparse WVD-Hough estimates are obtained byapplying Hough transform to the sparse WVD obtained using (14).

0 2 4 6 8−110

−105

−100

−95

−90

−85

−80

SNR (dB)M

SE

(dB

)

CTSS−WVDPHAFWIGNER−HOUGH

(a) Chirp 1

0 2 4 6 8−110

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−90

−85

−80

SNR (dB)

MS

E (

dB)


(b) Chirp 2

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SNR (dB)

MS

E (

dB)


(c) Chirp 3

0 2 4 6 8−110

−105

−100

−95

−90

−85

−80

SNR (dB)

MS

E (

dB)


(d) Chirp 4

Fig. 11: Performance comparison of chirp rate estimator for a fourcomponent LFM chirp signal with varying SNRs

chirp and the chirps do not occupy the full signal length. However,we observe from Figure 15c that the cross-terms are suppressedin CTSS-WVD without loss of TF frequency resolution. The lowamplitude second harmonic chirp at higher frequencies is notcompletely captured by CTSS-WVD.

VI. CONCLUSIONWe have proposed a novel technique for estimating the param-

eters of the multiple LFM chirp signals in noisy scenario. Thealgorithm is based on the sparse representation of the instantaneousautocorrelation sequence of the signal and is a three step process.The cross-terms present in the IA sequence are suppressed in thefirst step, followed by instantaneous frequency estimation in thesecond step and an line parameter estimation method in the laststep. The sparse representation is obtained using LASSO with adictionary of sinusoids. A high-resolution cross-term suppressedtime frequency representation of the multiple chirp signals is alsoobtained. Simulation results show the estimator performs efficientlyup to an SNR of -2dB for single chirp signals. We also evaluate theperformance using a four-component chirp signal in noise and showthat the resulting estimates are reliable. Finally, the effectivenessof the technique is proved by applying it to a bat echolation signalthat has minor deviations from the signal model considered.

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VII. REFERENCES

[1] M. S. Wang, A. K. Chan, and C. K. Chui, “Linearfrequency-modulated signal detection using Radon-ambiguitytansform,” IEEE Trans. Signal Process., vol. 46(3), pp. 571–586, Mar 1998.

[2] Y. Sun and P. Willett, “Hough transform for long chirpdetection” IEEE Trans. Aerosp. Electron. Syst., vol. 38(2),pp. 553–569, Apr. 2002

[3] X-G Xia, “Discrete chirp-Fourier transform and its applicationto chirp rate estimation,” IEEE Trans. Signal Process., vol48(11), pp. 3122–3133, Nov. 2000

[4] L. Qi, R. Tao, S. Y. Zhou and Y. Wang, “Detection andparameter estimation of multicomponent LFM signal based onfractional Fourier transform,” Sci. China (Ser F.),, vol 47(2),pp. 184-198, 2004

[5] J. C. Wood and D. T. Barry, “Radon transformation oftime-frequency distributions for analysis of multicomponentsignals,” IEEE Trans. Signal Process., vol. 43, pp. 3166–3177,Nov. 1994.

[6] H. I. Choi and W. J. Williams “Improved time-frequencyrepresentation of multicomponent signals using exponential

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Fig. 13: A slice of differentTF distributions along the dot-ted line shown in Figure 5. Theproposed method has the bestresolution.

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Fig. 15: TF distribution of bat echolation signal using differenttechniques. The fundamental and the first harmonic are sharplyresolved in CTSS-WVD along with suppressing the cross-terms.

kernels,” Proc. IEEE Int. Conf. Acoust., Speech, Signal Pro-cess., vol. 37, pp. 862–871, Jun 1989.

[7] S. Mallat and Z. Zhang,“Matching pursuit with time-frequency dictionaries,” IEEE Trans. on Signal Process.,vol. 41, pp. 3397–3415, Dec. 1993.

[8] R. A. Altes, “Detection, estimation and classification withspectrograms,” J. Acoust. Soc. Am., vol. 67, pp. 1232–1246,Apr. 1980

[9] S. Barbarossa, “Analysis of multicomponent LFM signals bya combined Wigner-Hough transform,” IEEE Trans. SignalProcess., vol. 43. pp. 1511–1515, Jun. 1995.

[10] S. Peleg, B. Porat, “Linear FM signal parameter estimationfrom discrete-time observations,” IEEE Trans. Aerosp. Elec-tron. Syst., vol. 27, pp. 607-615, Jul. 1991.

[11] R. Roy and T. Kailath, “ESPRIT – Estimation of signalparameters via rotational invariance techniques,” IEEE Trans.Acoust., Speech, Signal Process.,vol. 37, pp. 984 – 995, Jul.1989

[12] Tibshirani, “Regression shrinkage and selection via the lasso,”J. Roy. Statist. Soc., Series Bvol. 58, pp. 267–288, 1996.

[13] S. Barbarossa and A. Scaglione, “Product high-order ambi-guity function for multicomponent polynomial-phase signalmodeling,” IEEE Trans. on Signal Process., vol. 46, pp. 691–708, Mar. 1998.

Investigating the Origin of the Indian Ocean Geoid Low

Attreyee Ghosh

December 24, 2014

Abstract

This is an ongoing effort to interpret the geoid low in the Indian Ocean just south of the Indian penin-

sula. Several theories exist regarding the origin of the geoid low, which happens to be the lowest gravity

anomaly on Earth. However, none of them are wholly successful in explaining it. We wish to test differ-

ent tomography models, both global and regional, as well as different viscosity models to investigate the

source of this gravity low.

Introduction

Very few methods exist that can provide us with information about the deep Earth. One way to learn about

the deep earth is through seismic tomography, which uses seismic waves to image the Earth’s interior.

Mapping gravity anomalies provides another way of understanding the Earth’s internal structure. It has

been shown by Hager [1984], Hager and Richards [1989] that the Earth’s long wavelength geoid is highly

correlated with density models of subducted slabs. Other studies have also shown how the observed geoid

anomalies can be explained by density anomalies in the Earth’s mantle. Recent satellite images from

GRACE and GOCE missions are yielding a more and more detailed map of the gravity anomalies and

hence a clearer image of the Earth’s mantle. Two of the most important parameters that control the dy-

namics of the deep Earth are density and rheology or viscosity of the rocks that make up the Earth’s mantle.

While we have some knowledge about the density structure from seismic tomography, the viscosity struc-

ture is largely unknown and the observed geoid anomalies can be crucial in constraining the viscosity

structure of the Earth.

The lowest geoid anomaly on Earth lies just south of the tip of the Indian peninsula in the Indian

Ocean. Origin of this geoid low is controversial. Several theories have been proposed to explain this geoid

low, most of which invoke past subduction [Chase and Sprowl, 1983; Hager and Richards, 1989; Richards

and Engebretson, 1992; Steinberger, 2000]. But, there is no general consensus regarding the source of this

geoid low. This project aims to investigate the source of this anomaly by using forward models of mantle

convection. The models aim to test various tomography models with different radial and lateral viscosity

variations. A proper reproduction of this gravity anomaly will not only provide a greater understanding

into the density and viscosity structure of the deep mantle, but may also provide insight regarding the role

of chemical composition in the mantle, which at present is poorly understood.

Numerical models of mantle convection

In the past few months, we have explored some preliminary models of mantle convection. These models

use different tomography (density) structures and different structures of radial viscosity variations.

1


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with radial viscosity structure.

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Ocean region from the same model as in Figure 1.

All these models were run using the HC code

[Hager and O’Connell, 1981; Milner et al., 2009],

which is a semi-analytical code that solves the

equations of conservation of mass and momentum

as well as the constitutive equation between stress

and strain rate. Of the models run so far, a few of

them yield a better fit to the global geoid (Figure

1) as well as to the Indian Ocean geoid low (Fig

2). The next step would be to run models of higher

complexity, such as those with lateral strength vari-

ations, using the parallel finite element mantle con-

vection code CitcomS, which we have just success-

fully installed on one of the SERC supercomputers.

Acknowledgement

The above work has been accomplished with the help of project assistant Ms. Shree Sumanas Badrinath.

2

References

Chase, C. G., and D. R. Sprowl, The modern geoid and ancient plate boundaries, Earth Planet. Sci. Lett.,

62, 314–320, 1983.

Hager, B. H., Subducted slabs and the geoid: Constraints on mantle rheology and flow, J. Geophys. Res.,

89, 6003–6015, 1984.

Hager, B. H., and R. J. O’Connell, A simple global model of plate dynamics and mantle convection, J.

Geophys. Res., 86, 4843–4867, 1981.

Hager, B. H., and M. A. Richards, Long-wavelength variations in Earth’s geoid—physical models and

dynamic implications, Phil. Trans. R. Soc. Lond., 328, 309–327, 1989.

Milner, K., T. W. Becker, L. Boschi, J. Sain, D. Schorlemmer, and H. Waterhouse, The solid earth research

and teaching environment: a new software framework to share research tools in the classroom and across

disciplines, EOS Trans. AGU, 90, 12, 2009.

Richards, M., and D. Engebretson, Large-scale mantle convection and the history of subduction, Nature,

355, 437–440, 1992.

Steinberger, B., Slabs in the lower mantle - results of dynamic modelling compared with tomographic

images and the geoid, Phys. Earth Planet. Int., 118, 241–257, 2000.

3

Evolution of Mesoproterozoic suture zones western India: Implications on India-Madagascar correlations

C. Ishwar-Kumar and K. Sajeev

Abstract-The Kumta suture zone in western India is in the point of paleogeographic study of India and Madagascar. The ca. 1300 Ma Kumta suture separates the Karwar and Dharwar blocks within the western Dharwar craton of India (Ishwar-Kumar et al., 2013a). Towards the east the Sirsi shelf is unconformable on gneisses of the Dharwar craton and towards the west the Karwar block is mainly composed of undeformed tonalite-trondhjemite-granodiorite (TTG) with enclaves of amphibolites. The TTGs from Karwar block have U-Pb zircon magmatic ages of ca. 3200 Ma (Ishwar-Kumar et al., 2013a). The Coorg block mainly contains ca. 3200 Ma granulite grade rocks and separated from Dharwar craton by ca. 1200 Ma Coorg (Mercara) suture (Chetty et al., 2012; Ishwar-Kumar et al., 2013b; Santosh et al., 2014). Integration of the structural, geological and geochronological results suggests the presence of a 1300-1200 Ma suture in western India, which is possibly an eastern extension of the Betsimisaraka suture zone in Madagascar.

I. INTRODUCTION

The amalgamation of Gondwana supercontinent in the period 590-550 Ma and its subsequent fragmentation into the continental blocks of India, Madagascar, Australia, Africa, Sri Lanka, South America and Antarctica were key tectonic events in the earth history (Fig. 1). Much multi-disciplinary research in the last few decades has been focused on finding tectonic criteria to satisfactorily link, match and thus re-assemble the separated blocks into their former parent

(1e.g., Yoshida et al., 1992; Mishra et al., 2006; Grantham et al., 2008), and in particular Eastern Gondwana (Yoshida, 1995; Meert, 2003a; Collins and Pisarevsky, 2005). For example, much effort has been applied to finding ways to convincingly re-unite Madagascar and India (e.g., Katz and Premoli, 1979; Agarwal et al., 1992; Windley et al., 1994; Yoshida et al., 1999; Torsvik et al., 2000; Collins et al., 2007a).

Fig. 1. Map showing the assembly of Eastern Gondwana (after, Meert, 2003) consists of India, Madagascar, Sri Lanka, Antarctica, Australia, Congo craton and Kalahari craton. The study area India and Madagascar lies in the central part of Eastern Gondwana.

The position and morphological relationship of India relative to Madagascar in

ISTC/CEAS/SJK/291


the past is one of the most debated and current problems in understanding the tectonics of eastern Gondwana (e.g., Katz and Premoli, 1979; Collins and Windley, 2002; Braun and

Fig. 2. The regional geology map of southern India (modified after, Geological Survey of India, 2005), overlaid by reclassified shear zones. The rectangles show the detailed study area Karwar region and Coorg region. Acronyms: KSZ- Kumta Shear Zone, ChSZ- Chitradurga Shear Zone, MeSZ- Mettur Shear Zone, KolSZ- Kolar Shear Zone; NSZ- Nallamalai Shear Zone, MSZ- Moyar Shear Zone, McSZ- Mercara Shear Zone, BSZ- Bhavani Shear Zone, SASZ- Salem Attur Shear Zone, CaSZ- Cauvery Shear Zone, PCSZ-Palghat Cauvery Shear Zone, ASZ- Achankovil Shear Zone.

Kriegsman, 2003; Ghosh et al., 2004; Collins and Pisarevsky, 2005; Collins, 2006; Ashwal et al., 2013; Gibbons et al., 2013; Ishwar-Kumar et al., 2013; Rekha et al., 2013). The breakup of Gondwana started with the roughly simultaneous rifting of Madagascar, Seychelles, India, Antarctica and Australia from Africa at around ca. 150 Ma. Then, Madagascar, Seychelles and India separated together from Antarctica and Australia at about 128-130 Ma (Biswas, 1999).

II. GEOLOGICAL BACKGROUND

The period between the assembly of Rodinia and its break-up to form the Gondwana supercontinent in the Neoproterozoic marks an important stage in the tectonic history of southern Peninsular India. The position and morphological relationship of India relative to Madagascar in the past is one of the most debated and current problems in understanding the tectonics of eastern Gondwana (e.g., Katz and Premoli, 1979; Collins and Windley, 2002; Braun and Kriegsman, 2003; Ghosh et al., 2004; Collins and Pisarevsky, 2005; Collins, 2006; Ashwal et al., 2013; Gibbons et al., 2013; Ishwar-Kumar et al., 2013; Rekha et al., 2013, 2014; Ratheesh-Kumar et al., 2014). Katz and Premoli (1979) suggested that crustal-scale tectonic lineaments/shear zones/suture zones across the continental fragments are one of the major tools that can be used for paleo-geographic reconstruction, based on which they proposed a correlation between India andMadagascar.

Fig. 3. Sample locations and structural lineaments overlayed on the geological map of the Karwar block (modified after Geological Survey of India, 1993, 1996 and 2005; structural lineaments were extracted from Landsat ETM+ satellite imagery and ASTER digital elevation model).

Following this study, several workers attempted to place India and Madagascar into the Gondwana reconstruction, based on regional lithological, structural, geochronological, geochemical and geophysical evidence (e.g., Agarwal et al., 1992; Windley et al., 1994; Windley and Razakamanana, 1996; Collins and Windley, 2002; Raval and Veerasamy, 2003; Ghosh et al., 2004; Raharimahefa and Kusky, 2006, 2009; Ishwar-Kumar et al., 2013b; Rekha et al., 2013, 2014; Rekha and Bhattacharya, 2014; Ratheesh-Kumar et al., 2014). However, the many and varied models have led to considerable inconsistency, mismatch and disagreements about specific correlations. Some of the reasons for this inconsistency arevariation in age and exact position/extent of the shear/suture zones, problems with bathymetry, and the variation or distortion in scale, among other factors.

III. ANALYTICAL TECHNIQUES

A. Electron Microprobe Analysis

Chemical analyses of all the minerals were obtained using an electron microprobe analyser (JEOL JXA-8900R microprobe) housed at the Okayama University of Science, Japan and by JXA-8530F at the University of Tsukuba, Japan. The analyses were performed under conditions of 15-20 kV accelerating voltage, 10-12 nA sample current and a spot size of 3 µm. The data were regressed using the oxide-ZAF correction method. Natural and synthetic silicates and oxides were used for calibration. All the major minerals were analysed for SiO2, TiO2, Cr2O3, Al2O3, FeO, MgO, MnO, CaO, Na2O, and K2O.

B. SHRIMP U-Pb zircon geochronology and zircon Lu-Hf isotopic analysis

Zircon U-Pb dates were obtained using a sensitive high-resolution ion microprobe (SHRIMP II) at Curtin University, Australia. Analytical procedures for the U-Pb analysis followed those outlined by Williams

(1998). Zircons from each sample were mounted together with standards in an epoxy resin disc that was polished to obtain cross-sections through the grains. Prior to the U-Pb analyses, the mount was cleaned and coated with 40Å thickness of gold. To determine the internal structure of individual grains and to identify suitable analytical sites, backscattered electron (BSE) and cathodoluminescence (CL) images were obtained using a scanning

Fig. 4. P-T phase diagram modeled using the calualted bulk composition of quartz-phengite schist (IK-2302L) in the SiO2-TiO2-Al2O3-FeO-MnO-MgO-CaO-Na2O-K2O-H2O chemical system.

electron microscope (SEM; JEOL JSM-5900 LV) at Curtin University. To obtain the SEM images a 0.2nA electron beam current and a 15kV acceleration voltage was used on the gold coated mounts. For SHRIMP analyses, an O2− primary ion beam of 2 nA intensity was utilized to sputter the analytical spot of 25-30µm diameter. The zircon standard BR266 with a 206Pb/238U age of 599 Ma (Stern, 2001) was used as external standard to correct instrumental mass bias and isotopic fractionation. A correction for common Pb was made on the basis of the measured 204Pb and the model for common Pb compositions was that proposed by Stacey and Kramers (1975). The U-Pb data were reduced using the SQUID 2 and Isoplot 3 software (Ludwig, 2003, 2005). Uncertainties reported for

individual analyses are at the 1σ level, and for pooled ages they are at the 95% confidence level. In situ zircon hafnium isotopic analysis was carried out at the Institute of Geology

Fig.5.a. Representative cathodolu-minescence images of quartz-phengite schist (IK-2302L) illustrating zircon structures. b. SHRIMP U-Pb zircon concordia plot. Inset shows representative CL images for zircons for each sample, probability density graph for quartz-phengite schist and bar chart.

and Geophysics, Chinese Academy of Sciences, Beijing. Sites previously dated by SHRIMP were selected for Lu-Hf analyses, and the measurements were performed using a Neptune MC-ICPMS, fitted with a 193 nm ArF laser, with spot sizes 40-50µm and laser repetition rate of 8 Hz at a laser power of 100 mJ/pulse. Analytical and data correction procedures were as described by Wu et al. (2006). To calculate the 176Lu/ 177Hf ratio, interference of 176Lu on 176Hf was corrected by measuring the intensity of the interference free 175Lu, by using the recommended 176Lu/175Lu ratio of 0.02669 (DeBievre and Taylor, 1993). To calculate the 176Hf/177Hf ratio isobaric interference of 176Yb on 176Hf was corrected by using the 176Yb/172Yb ratio of 0.5886 (Chu et

al., 2002). Each ten analysis were bracketed by analyses of standards Mud Tank and GJ-1.

IV. RESULTS

A. P-T evolution A metamorphic pressure –temperature

estimation has done for quartz-phengite schist from the Kumta suture (Fig. 3). The mineral pair phengite ± quartz is stable in all fields (Fig. 10) and kyanite is stable on the high-pressure/temperature side. Chlorite appears when the pressure drops and chloritoid disappears at an even lower pressure. Lawsonite is stable only in the low-temperature/high-pressure corner of the phase diagram (Okamoto and Maruyama, 1999). There are no significant variations in the composition of phengite in Kumta suture samples. Based on the assemblage (phengite-quartz-rutile-chlorite-chloritoid-H2O) and the XMg-XNa composition of phengite, the quartz-phengite schist is calculated to have been stable at c. 13 kbar at 525ºC (Fig. 4). The presence of fine-grained alluminosillicates (sillimanite/ kyanite) in the phengite matrix (Fig. 4) (revealed by X-ray elemental mapping using a field emission microprobe) extends the P-T segment into the kyanite stability field. Thus, based on the present composition, the rock exhumed from a P-T condition of c. 18 kbar and 550ºC (Fig. 4) in the eclogite facies and underwent re-equilibration in the amphibolites facies (Liou et al., 1998; Oh and Liou, 1998). Such a P-T evolution provides confirmation that the Kumta suture is sited on a subduction zone, which was capable of such subduction and exhumation.

B. Timing of metamorphism Zircons from quartz-phengite schist

(IK-110123-02L) sample have rounded to sub-rounded morphology (grain size <100 µm) and no metamorphic overgrowths (Fig. 5a). These are detrital zircons with no metamorphic rims. A total of 32 zircon grains were analyzed, 14 spots out of 32 spots in 14 zircon grains were

used. The zircons from quartz-phengite schist (IK-110123-02L) sample gave mainly four age populations, ca. 2993 Ma, ca. 3101 Ma, ca. 3126 Ma, and ca. 3280 Ma (Fig. 5b). The lower intercept ages ranges from 1000-1200 Ma and used to broadly constrain the timing of metamorphism (Ishwar-Kumar et al., 2013).

Fig. 6. Concordia LA-ICPMS U-Pb plots of zircons. a.. KR23-20C- Garnet-biotite-kyanite-gedrite-cordierite gneiss. b. KR23-20K- Garnet-biotite-kyanite gneiss. c. KR23-20F- Garnet-biotite-hornblende gneiss.

Metasedimentary rocks from the Mercara suture show prominent Paleoarchean to Mesoproterozoic sources. The lower intercept of zircons in all rocks suggest a metamorphic event during the mid- to late

Fig. 7. A ɛHf (t) vs. U-Pb zircon age (207Pb/206Pb age) plot for the metasedimentary samples from the Kumta and Mercara suture zones.

Mesoproterozoic (Fig. 6). Surprisingly, all the zircons from samples KR23-20C, KR23-20K and KR23-20F fall on a common discordia line, suggesting source rocks of a similar age. However the spread in ɛHf (t) values indicates arrange from juvenile to ancient protolith materials, suggesting either magma mixing or a variety of rock-types with a similar age. The hafnium isotopic compositions suggest that the sediments were derived from mixed sources including both juvenile and recycled continental crust. The ɛHf (t) vs. U-Pb zircon age (207Pb/206Pb age) plot defines a major distribution along the 3.0 to 4.0 Ga Archean crustal growth line (Fig. 7).

V. DISCUSSION

Based on the present results from western India and eastern Madagascar, integrating with published results, we propose a close-fit correlation between India and Madagascar. The Betsimisaraka suture zone is mainly composed of paragneisses (with common augen, cataclastic and mylonitic fabrics) associated with pelitic mica schists

containing garnet, staurolite, kyanite, sillimanite and graphite) (Hottin, 1969; Collins and Windley, 2002). The metasedimentary rocks enclose abundant mafic-ultramafic lenses. These rocks and relations can be correlated with those of the Kumta and Mercara suture zones that include quartz-phengite schist, chlorite schist, garnet-biotite schist, marble and amphibolite- to granulite-facies garnet-, kyanite-, sillimanite- and quartz-, feldspar-bearing paragneisses with metagabbro and calc-silicate granulites. The Betsimisaraka suture zone of northeastern Madagascar (Kröner et al., 2000; Collins and Windley, 2002; Collins et al., 2006) correlates with the Kumta suture zone of southern India (Ishwar-Kumar et al., 2013). The southern end of the Betsimisaraka suture of Madagascar extend into the Mercara suture zone (Reported as Coorg suture zone in Ishwar-Kumar et al., 2013 and same suture mentioned as Mercara suture zone in Santosh et al., 2014. But since the block is named as Coorg block, suture is hereafter named as Mercara suture zone (McSZ)) of southern India (Ishwar-Kumar et al., 2013). The Palghat-Cauvery shear zone of southern India has been correlated with the Neoproterozoic Angavo shear zone of Madagascar, and the Tranomaro shear zone of the southern Madagascar has been correlated with the Achankovil shear zone of southern India.

VI. CONCLUSIONS AND FUTURE WORK

Structural and regional geological evidence suggest the presence of shear zones in western India. Textural evidence and metamorphic P-T estimations for the metasedimentary rocks suggest that the sediments were subducted to depths of about 40-50 km depth, and were later exhumed.

The zircon SHRIMP and LA-ICPMS U-Pb geochronology of metasedimentary rocks from the Kumta and Mercara suture zones suggest a common metamorphic event in the Mesoproterozoic.

Integration of results with published data indicates that the Archean blocks in western Peninsular India were sutured during the Mesoproterozoic. We therefore propose that the Kumta and Mercara sutures are the eastern extension of the northern and southern parts of the Mesoproterozoic Betsimisaraka suture of north-eastern Madagascar.

There are several challenges still exist in this field and needs more detailed study in future.

A. Variation in metamorphic grade of Karwar and Coorg region

The Kumta suture contains amphibolite- to greenschist-facies schistose rocks and the Mercara suture contains amphibolite- to granulite-facies rocks. The Karwar block on the western side of the Kumta suture contains ca. 3200 Ma TTG gneisses with enclaves of amphibolite (Ishwar-Kumar et al., 2013b). The Coorg block on the western side of the Mercara suture consists of ca. 3200 Ma gneisses and ca. 3200 charnockites with enclaves of amphibolite and mafic granulite (Santosh et al., 2014). The Coorg block can be considered as a high-grade equivalent of the Karwar block, and the Mercara suture as the high-grade equivalent of the Kumta suture, with deeper crust exposed in the Coorg area.

B. Extension of the Betsimisaraka-Kumta-Mercara suture zone

The northern and southern ends of Betsimisaraka suture in eastern coast of Madagascar are correlated with the Kumta and Mercara suture respectively. However, the further continuation of Mercara suture zone is unclear. The N-S trending eastern end of Mesoproterozoic Mercara suture zone is structurally discordant with NNW-SSE trending Moyar shear zone. The Moyar shear zone is proposed as early Paleoproterozoic suture by Plavsa et al. (2014). The detailed study including structures and geochronology are required to understand this problem.

ISTC/CCE/MS/302

Estimation of soil hydraulic properties in a catchment using agro-hydrological models and microwave remote sensing

K. Sreelash, M. Sekhar, S. K. Tomer, S. Bandyopadhyayand M.S. Mohan Kumar

Abstract–This paper illustrates with results the methodology of estimating multilayered soil hydraulic properties by inversion of a crop model using surface soil moisture and crop bio-physical parameters from microwave remote sensing data. Crop biophysical parameters such as leaf area index (LAI) and above ground biomass are sensitive to the properties of the root zone soil and hence these observations can be useful for estimating the properties of deeper soil layers. We used the radar vegetation index (RVI) and a parametric growth curve of LAI to estimate LAI from radar data. The surface soil moisture (SSM) for vegetated areas is estimated using the water cloud model (WCM). The LAI and SSM estimated from RADARSAT-2 data are then used to estimate multilayered soil hydraulic properties by inversion of STICS crop model. The methodology is tested with marigold crop at AMBHAS field site (www.ambhas.com) for different soil and climate combinations, theresults showthat the proposed approach is a promising one in estimating multilayered soil hydraulic properties.

I. INTRODUCTION

Good estimates of soil hydraulic parameters and their distribution in a catchment is essential for crop and hydrological models. Measurements of soil properties by experimental methods are expensive and often time consuming, and in order to account for spatial variability of these parameters in the catchment, it becomes necessary to conduct large number of measurements.

K. Sreelashis a CNES post-doctoral research fellow at INRA, Avignon, France. [email protected] M. Sekhar, Associate Professor in the Department of Civil Engineering, IISc, Bangalore. [email protected] S. K. Tomer is a post-doctoral research fellow at CESBIO, Observatoire Midi-Pyrenees, Toulouse, France. M.S. Mohan Kumar is a Professor in the Department of Civil Engineering, IISc,Bangalore. S. Bandyopadhyay is a scientist at EOS, ISRO headquarters at Bangalore.

Estimation of soil parameters by inverse modelling using observations on either surface soil moisture or crop variables has been successfully attempted in many studies, but difficulties to estimate root zone properties arise for heterogeneous layered soils. Inverse modeling has been extensively used at column scale (Kumar et al., 2010) but the spatial variability of the parameters and insufficient data sets restricted its applicability at the catchment scale (Vereeckenet al. 2008). Montzkaet al. (2011) demonstrated the possibility of estimating the soil hydraulic parameters using remotely-sensed surface soil moisture measurements by applying a sequential filtering technique to the mechanistic soil-water model HYDRUS 1-D.However, this approach is not applicable for multilayered soils without soil moisture measurements of the deeper horizons. Ines and Mohanty (2008) used the assimilation of near-surface soil moisture in a crop model and concluded that additional soil moisture data from deeper depths may be needed to better estimate the hydraulic properties of layered soils. Vereeckenet al. (2008) indicated that the use of surface soil moisture measurements alone is not sufficient to provide unique and physically reasonable estimates of hydraulic properties at the field scale.

Crop variables such as LAI and biomass are sensitive to the properties of the soil horizons belonging to the root zone and hence these observations could be useful for estimating soil properties of deeper horizons. Recently, Jana and Mohanty (2011) showed that using remote sensed crop variables (LAI) in addition to classical soil texture data improved estimation of soil hydraulic parameters (SHPs) by pedo-transfer functions. Remotely sensed data of crop variables have been used to estimate soil parameters of crop models. Varellaet al. (2010) performed the inversion of a crop model using LAI and yield at harvest to estimate soil parameters in a two-layer soil system and concluded that the estimation of parameters related to soil water content and soil depth could be significantly improved as


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compared to prior information especially when water stress. Charoenhirunyingyos et al., (2011) and Sreelash et al., (2012) showed that a combination of crop and soil moisture variable can provide good estimates of soil water content parameters. Availability of remote sensed information of surface soil moisture and leaf area index makes this approach applicable at a larger scale.

Microwave methods have extensively been used to characterize biophysical crop parameters. Many studies have shown that there is a clear interdependence between biomass, LAI and observed backscattering coefficients from active microwave systems. Leaf area index and biomass can be estimated from microwave remote sensing data using the radar vegetation index (RVI). RVI has been found to one of the promising approaches in estimating the crop biophysical parameters and our initial studies showed good correlation between RVI and LAI.A number of studies have been carried out to investigate the relationship between radar backscattering and soil moisture for different study areas. Various theoretical and empirical models have been developed to retrieve the soil moisture from active microwave data. The major challenge to the theoretical and empirical models is the modeling of backscatter behavior under the vegetation canopy. A model which is frequently used in the modeling of soil moisture in vegetated areas is the water-cloud model (WCM) (Attema and Ulaby, 1978). The variations in the canopy descriptors used in the models that describe canopy backscattering are due to the complexity of vegetation structure and relative simplicity of the models. The parameters of the WCM depend on the crop and soil type, and it is necessary to calibrate the WCM for site specific conditions using suitable vegetation descriptors.

In this study we illustrate and test the methodology of estimating multilayered soil properties by inversion of a crop model using the generalized likelihood uncertainty estimation (GLUE) approach. The approach of estimating LAI from RVI using the parametric growth curve approach is also discussed. Calibration of WCM is carried for site specific crops. Estimability of multilayered soil properties is tested for marigold crop in different soil and climate combinations.

II. EXPERIMENTS, DATA AND MODEL

The experiments were carried out on several agricultural plots of approximately one hectare in size in the Berambadi watershed, 84 km2area (AMBHAS Research Observatory, www.ambhas.com) located in the Kabini river basin in South India, which is an experimental watershed for carrying out agro-hydrological, remote sensing and hydrological investigations. The main crops are turmeric, sunflower, maize, marigold, sugarcane, finger millet, groundnut etc. Soil and crop related measurements were performed during the cropping season from May- 2011 to Dec-2013. Surface soil moisture, profile soil moisture (PSM), leaf area index (LAI) and above-ground biomass were measured at ten day frequencyfrom the sowing to harvest and yield was measured at harvest date.

Table 1.Activities in the experimental plots.

Duration of crop growth (in days): 100 (May to August)

No of plots monitored: 26

Measures Variables: SSM, PSM, LAI, biomass, yield.

Frequency of measurements: 10 day frequency

Satellite Data: RADARSAT-2, 4 images in 2011

3 images in 2012 and 3 images in 2013

STICS crop model (Brisson et al., 2008) is used in this study. STICSis a dynamic, daily time-step model which simulates the functioning of a soil-crop system over a single or several successive crop cycles. STICS simulates the daily carbon balance, the water balance (evaporation and transpiration) and the nitrogen balance in the system, which makes it possible to calculate both agricultural and environmental variables in a variety of agricultural situations. STICS model has been calibrated for the crops specific to the study area.

II. LAIFROM RADAR DATA

RVI (Kim and Van Zyl, (2009) has been proposed as a method for monitoring the level of canopy growth variables like LAI, biomass and VWC. RVI is near zero for a smooth bare surface soil and increases as a crop grows. In this study we used the parametric

ISTC/CCE/MS/302

growth curve described in the previous section to develop the RVI growth curve, since RVI represents the LAI growth characteristics of the crop in some form.

The RVI is given by

𝑅𝑅𝑅𝑅𝑅𝑅 = 8𝜎𝜎𝑜𝑜𝐻𝐻𝑅𝑅

𝜎𝜎𝑜𝑜𝐻𝐻𝐻𝐻 + 𝜎𝜎𝑜𝑜𝑅𝑅𝑅𝑅+ 2𝜎𝜎𝑜𝑜𝐻𝐻𝑅𝑅 (1)

whereσHV is the cross-polarization backscattering cross section and σHH and σVV are the co-polarization backscattering cross sections represented in power units. RVI generally ranges between 0 and 1 and is a measure of the randomness of the scattering. RVI is near zero for a smooth bare surface and increases as a crop grows (up to a point in the growth cycle).

We used the standard “Parametric Growth Curve” (Baret, 1986) which reproduces the time variations of canopy variables like LAI, biomass and vegetation water content (VWC). The growth curve for the vegetation variable LAI is based on five parameters (K, a, b, Ti and Tf) and is given by

𝐿𝐿𝐿𝐿𝑅𝑅(𝑑𝑑) = 𝐾𝐾[1

1 + exp−𝑏𝑏(𝑇𝑇(𝑑𝑑) − 𝑇𝑇𝑖𝑖)

− exp𝑎𝑎(𝑇𝑇(𝑑𝑑) − 𝑇𝑇𝑓𝑓)) (2)

Where K is related to the amplitude of the LAI variations, a and b are responsible for the trend of the crop growth and senescence and the time parameters Ti and Tfcorresponds to the temporal characteristics of the growth curve. The time related parameters Ti and Tfto be constant for all plots in a particular year. The LAI growth curve for each field plots are then generated using the parametric growth curve by estimating the values of a, b and K using the field observed LAI values.

The RVI growth curve can be expressed as,

𝑅𝑅𝑅𝑅𝑅𝑅(𝑑𝑑) = 𝐾𝐾′[1

1 + exp−𝑏𝑏′(𝑡𝑡 − 𝑇𝑇𝑖𝑖′)− exp𝑎𝑎′(𝑡𝑡 − 𝑇𝑇𝑓𝑓′)) (3)

In this equation 𝐾𝐾′ represents the amplitude of the RVI variations similar to the parameter K in the LAI growth curve. The parameters𝑎𝑎′,𝑏𝑏′, 𝑇𝑇𝑖𝑖 ′ and 𝑇𝑇𝑓𝑓′ of the

RVI growth curve corresponds to parameters a, b, 𝑇𝑇𝑖𝑖 and 𝑇𝑇𝑓𝑓 of the LAI growth curve. These parameters can be estimated using the time series of RVI data.The shape of the RVI growth curve will be similar to that of the LAI growth curve, since the parametric growth curve is used in both cases. The relation between the LAI and RVI growth curve now depends on the amplifying factor K and 𝐾𝐾′, and the relation can be established as

𝐿𝐿𝐿𝐿𝑅𝑅(𝑑𝑑) = 𝑘𝑘′ ∗ 𝑅𝑅𝑅𝑅𝑅𝑅(𝑑𝑑) (4)

Where 𝑘𝑘′ defines the relation between K and 𝐾𝐾′ .

The value of K, depends on the maximum LAI of the crop and hence the 𝐾𝐾′can be expected to be a function of the maximum value of RVI.

IV. SSM FROM RADAR DATA

In the general form the WCM (Attema and Ulaby 1978) for a given incidence angle θican be expressed as a linear combination of backscattering by vegetation and backscattering by soil and is given by the following equation,

𝜎𝜎𝑝𝑝𝑝𝑝𝑜𝑜 = 𝜎𝜎𝑣𝑣𝑣𝑣𝑣𝑣𝑜𝑜 + 𝜏𝜏2𝜎𝜎𝑠𝑠𝑜𝑜𝑖𝑖𝑠𝑠𝑜𝑜 (5)

Where 𝜎𝜎𝑝𝑝𝑝𝑝𝑜𝑜 is the co-polarized total backscatter coefficient, 𝜎𝜎𝑠𝑠𝑜𝑜𝑖𝑖𝑠𝑠𝑜𝑜 is the backscatter contribution of the soil surface, 𝜎𝜎𝑣𝑣𝑣𝑣𝑣𝑣𝑜𝑜 is the backscatter contribution of the vegetation cover, expressed as,

𝜎𝜎𝑣𝑣𝑣𝑣𝑣𝑣𝑜𝑜 = 𝐿𝐿𝑅𝑅1𝑐𝑐𝑜𝑜𝑠𝑠𝜃𝜃(1 − 𝜏𝜏2) (6)

and𝜏𝜏2 is the two attenuation by the vegetation, expressed as,

𝜏𝜏2 = exp(−2𝐵𝐵𝑅𝑅2𝑠𝑠𝑣𝑣𝑐𝑐𝜃𝜃) (7)

Where A and B are the vegetation parameters, 𝑅𝑅1 and 𝑅𝑅2 are the vegetation descriptors.

Following Lievens and Verhoest (2011), the combination 𝑅𝑅1 = 𝐿𝐿𝐿𝐿𝑅𝑅 and 𝑅𝑅2 = 𝐿𝐿𝐿𝐿𝑅𝑅 is used in this study primarily because of the interest to estimate soil moisture and LAI for each vegetation and experimental plots can be retrieved using RVI as discussed in the previous section. The parameters A

ISTC/CCE/MS/302

and B are obtained through nonlinear regression or least square fitting approach.

The underlying soil contribution 𝜎𝜎𝑠𝑠𝑜𝑜𝑖𝑖𝑠𝑠𝑜𝑜 can be evaluated from a surface scattering model or, more simply, from a linear function of its soil moisture𝑚𝑚𝑠𝑠.

𝜎𝜎𝑠𝑠𝑜𝑜𝑖𝑖𝑠𝑠𝑜𝑜 = 𝐶𝐶 + 𝐷𝐷𝑚𝑚𝑠𝑠 (8)

The parameters C and D are obtained with linear model fitting.

V. SOIL PARAMETERS FROM RADAR DATA

Parameter estimation by inversion of a dynamic crop model like STICS is a complex process, since such models involve parameter interactions and hence obtaining a single optimum soil parameter set is not realistic. Generalized Likelihood Uncertainty Estimation (GLUE) (Beven, 2008) an informal Bayesian method using prior information about parameter values for estimating model parametersis used for the parameter estimation process. The sum of absolute errors (SAE),is used in this study as the likelihood measure. The likelihood SAE is calculated for each variable considered in the inversion for each model run as,

𝑆𝑆𝐿𝐿𝑆𝑆𝑖𝑖𝑘𝑘 = y𝑖𝑖 ,𝑗𝑗

𝑘𝑘− 𝑦𝑦𝑖𝑖𝑘𝑘

𝑀𝑀𝑖𝑖

𝑗𝑗=1

𝑖𝑖 = 1 𝑡𝑡𝑜𝑜 𝑛𝑛 (9)

where, 𝑆𝑆𝐿𝐿𝑆𝑆𝑖𝑖𝑘𝑘 is the sum of the absolute errors for any parameter set k, k=1,…N (N being the number of ensembles, 20000 in this study), variable i, i = 1 to n corresponding to each variable considered with n being the total number of variables, and measurement date j, j=1,…,Mi (Mi being the total measurements for the variable I) 𝑦𝑦is the simulated value of the variable for the (i,j)th parameter set and y is the measured value of the variable. The parameters related to soil water storage characteristics such as field capacity, wilting point and thickness of soil layer are estimated in this study.

V. RESULTS AND DISCUSSIONS

The parameters of Eq. (2), (3) and (4) are estimated using the GLUE approach using the observed values

of LAI and RVI values retrieved from RVI. It was observed that the slope parameters and temporal characteristics of LAI and RVI curves modeled by Eq.(2) and (3) were close to each other, whereas the amplification factor K and K’ varied with plot. The k’values for each field plots are estimated. The values of k’ varied from 3.8 to 5.2 for field. The observed LAI growth curve and the RVI growth curve modeled using Eq. (2) is shown in Fig.1 along with the corresponding observed values (median and standard deviation of all plots.

Figure 1: RADARSAT-2 retrieved RVI and the RVI growth curve, observed LAI and LAI growth curve for Marigold crop (2011).

Figure 2: Scatter plot of observed LAI and LAI estimated from RVI for different days after sowing (DAS).

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Using Eq. (2) and Eq. (3) in combination will provide a time series of LAI from a time series of RVI data. Fig. 2 shows the scatter plot of observed LAI and the LAI obtained using the RVI and growth curve approach for 10, 20, 30, 40, 50, 60, 70, 80 and 90 days after sowing for a marigold crop.

The water cloud model was calibrated for the marigold crop using LAI and the vegetation descriptor. The parameters of the WCM A and B are found to be 4.52 and 0.024 respectively. The parameter C and D depends on soil type and are estimated separately for each soil class. The calibrated WCM was first used to estimate the σHH and then using inversion method the surface soil moisture was estimated. The estimated σHH and the RADATSAT-2 retrieved σHHagreed closely. The scatter plots of the modelled σHHand retrieved σHHshown in Fig. 5(a) and the scatter plots of surface soil moisture is shown in Fig. 5(b).

Figure 3: Scatter plot of (a) σHH and (b) surface soil moisture.

The LAI and SSM estimated from RADARSAR-2 data were used to estimate the soil hydraulic properties of two-layered vertically heterogeneous soils in the experimental plots. The scatter plot of the estimated field capacity of both horizons and the observed values are shown in Fig.4 (a) and (b). The SHP’s obtained from model inversion agrees closely with the SHP’s obtained from field experiments.The close agreement between the field experiments and the estimates obtained from inversion of satellite data shows that this approach has the potential to estimates soil hydraulic properties at a catchment scale, since remote sensing data of LAI and surface soil moisture are available at that scale.

Figure 3 shows the scatter plot of the field capacity and wilting points of different layers (2 layers in this study) estimated from field based experiments and estimates obtained using inversion of satellite LAI and surface soil moisture.

VI. SUMMARY AND CONCLUSIONS

ISTC/CCE/MS/302

The approach of estimating multilayered soil hydraulic properties using remote sensing data is a promising approach as demonstrated in this study. In this paper we examined the applicability of the approach usingmarigold crop. The suitability of the method for different climatic conditions (low and high rainfall) is examined using data from three seasons with different rainfall and water stress conditions. The approach of estimating LAI from RVI using the growth curve approach is a promising method for application in areas with data gaps. The calibrated water cloud model was found to be suitable for estimating the surface soil moisture under vegetated areas.

Acknowledgement: We thank Dr. Laurent Ruiz (INRA, Rennes), Dr Martine Guerif&DrSamuel Buis (INRA, Avignon) andDrSamuel Corgne (University of Rennes)for their technical support and advice at various stages of the work.

REFERENCES

[1] Attema, E.P.W. and Ulaby, F.T. (1978). Vegetation modeled as a water cloud. Radio Science, 13 (2), 357–364.

[2] Baret, F. (1986). Contribution au suive radiométrique de cultures de céréales. Phd Thesis Submitted to University of Paris South (Université Paris Sud).

[3] Beven, K. J., Smith, P., and Freer, J. 2008. So just why would a modeller choose to be incoherent? J. Hydrol., 354 (1-4), 15–32.

[4] Brisson, N., Mary, B., Ripoche, D., Jeuffroy, M.H., Ruget, F., Nicoullaud, B., Gate, P.,Devienne-Baret, F., Antonioletti, R., Durr, C., Richard, G., Beaudoin, N., Recous, S.,Tayot, X.P.D., Cellier, P., Machet, J.-M., Meynard, J.-M. andDelecolle, R. 1998. STICS:a generic model for simulating crops and their water and nitrogen balances.I. Theory and parameterization applied to wheat and corn. Agronomie 18,311-346.

[5] Charoenhirunyingyosa., S, Hondaa., K., Kamthonkiatb., D. and Ines., A. V.M. (2011). Soil moisture estimation from inverse modeling using multiple criteria functions. Journal of Computers and Electronics in Agriculture, 75(2), 278–287.

[6] Ines, A. V. M. and Mohanty, B. P. 2008. Near-Surface soil moisture assimilation for quantifying effective soil hydraulic properties under different

hydroclimatic conditions. Vadose Zone Journal, 7(1), 39-52.

[7] Jana, R. B. and Mohanty, B. P. 2011. Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation. Journal of Hydrology, 399, 201-211,

[8] Kim Y. J., and Van Zyl, J. (2009) “A time-series approach to estimate soil moisture using polarimetric radar data,” IEEE Trans. Geosci. Remote Sens., vol. 47,no. 8, pp. 2519–2527, Aug. 2009.

[9] Kumar, S., Sekhar, M., Reddy, D. V. and Mohan Kumar, M. S. 2010. Estimation of soil hydraulic properties and their uncertainty: comparison between laboratory and field experiments. Hydrological Processes, 24, 3426-3435.

[10] Lievens, H. and Verhoest, N.E.C. (2011). On the retrieval of soil moisture in wheat fields from L-band SAR based on water cloud modeling, the IEM, and effective roughness parameters. IEEE Geoscience and Remote Sensing Letters. 8(4): 740-744.

[11] Montzka, C., Moradkhani, H., Weihermuller, L., Franssen, H.H., Canty, M., Vereecken, H., 2011. Hydraulic parameter estimation by remotely-sensed top soil moisture observations with the particle filter. Journal of Hydrology 399, 410-421.

[12] Sreelash, K., Sekhar, M., Ruiz, L., Tomer, S.K., Guerif, M., Buis, S., Durand, P. and Gascuel-Odoux, C. 2012. Parameter estimation of a two-horizon soil profile by combining canopy and surface soil observations using GLUE. Journal of Hydrology. 456-457, 57-67.

[13] Varella, H., Guerif, M., Buis, S. and Beaudoin, N. 2010b. Soil properties estimation by inversion of a crop model and observations on crops improves the prediction of agro-environmental variables. Europ.J.Agronomy 33, 139-147.

[14] Vereecken, H., Huisman, J.A., Bogena, H., Vanderborght, J., Vrugt, J.A. and Hopmans, J.W. 2008. On the value of soil moisture measurements in vadose zone hydrology: a review. Water Resources Research 44.

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III.DEVELOPMENT OF CRYOTRIBOMETER

The cryotribometer has been designed and developed by extending the concept of conventional pin on disc tribometer to suit cryogenic environment.

The entire system is housed in a metallic frame of size 700X600X920mm. This tribometer assembly is mounted to the top cover plate. The frame is assembled with sufficient stiffness to absorb dynamic forces developed during tests. The entire assembly is supported on table top with pads.

A 0.4kW AC motor mounted below the top cover drives a spindle assembly with a V belt. The spindle made out of hylam is made to rotate at a constant speed of 400rpm with the help of reduction

gear box. Hylam is specifically used since it is compatible with cryogenic environment and exhibits low value of thermal conductivity and good mechanical strength properties. This shaft is mounted on the cover plate with a labyrinth seal housing to avoid cold leakages. The shaft has circular platform at the bottom. Abrasive discs or papers of 100mm diameter can be rigidly held on this platform.

Parallel to this spindle assembly, a plunger is mounted on the cover plate at an axial distance of 30mm. This plunger made of hylam extends to both top and bottom of the cover plate and it is housed within a Teflon labyrinth seal which prevents cold leaks. The plunger moves up/down freely inside two stroke bearings enclosed within the bearing housing. A circular platform is located on the top to hold dead weights. These dead weights impart pressing force on the pin against the rotating abrasive disc.

At the bottom of this plunger, test pins of 6mm diameter and 15mm length can be vertically clamped on to a holder which is connected with the plunger. The pin is located at a constant track radius of 30mm from the axis of the spindle. As the disc rotates at a constant speed of 400 rpm for the selected time duration, the pin continuously wears due to the friction and its length reduces. This reduction is the total wear.

Wear of the test pin is measured with the help of an Linear Variable Differential Transducer (LVDT) which has the maximum sensing limit of 4 mm. LVDT is fixed vertically in inverted position on the top cover plate with its spring loaded plunger pressing against a horizontal reference plate which is connected with the load carrying plunger. As the pin wears, the reference plate moves downwards pushing the LVDT plunger inwards. The continuous inward movement of the plunger is a measure of wear.

The frictional force between the pin and abrasive disc is measured with the help of a button type load cell which has the maximum sensing limit of 50N. The frictional force sensor is firmly secured on a stationary bracket mounted on the plunger. The frictional force between the pin and disc is directly transmitted to the sensor through the bracket. Since the load cell pin is pressed against the sensor, the frictional force is transmitted to the load cell. The output readings of the calibrated load cell are the readings of the frictional force.

This tribometer can be used to study the tribological properties at cryogenic temperature using liquid nitrogen and a cryogenic chamber. The cryogenic chamber made of SS304 is a double walled insulated cylindrical unit. The space between the inner and outer vessel is filled with polyurethane

foam (PUF) to minimise heat in leaks from the ambient atmosphere.[4]This chamber encloses the test area comprising of pin and disc. The chamber can be raised with the help of a motorised jack so that its top flange butts against the bottom surface of the cover plate. An O ring is placed between them to prevent leakage of LN2.The top cover consists of two ports for inlet and exhaust of LN2, pressure relief valve, coupling for electrical connections and a pressure gauge. The motor of the jack is operated by a portable remote switch.PT 100 sensor is used to measure the temperature of the test region.

The tribometer is operated and monitored by an electronic controller. The front panel displays the wear, frictional force, temperature, speed and time. Various experimental parameters like duration, speed, applied load etc can be initialised before start of the experiment. The graphical and digital output readings of the tribometer like wear, frictional force, speed, temperature and time can be continuously observed on the monitor with a Data Acquisition System (DAQ) connected with the controller. The photographs of the cryotribometer assembly and the pin on disc assembly are shown in figures 3 and 4.

Figure 3. Experimental system

Figure 4. Pin on disc assembly

IV.EXPERIMENTS

In this experimental work, tribological tests were carried on two types of materials namely plain and PTFE with 30% carbon composition. Trials were conducted both at room and cryogenic temperature using the developed cryotribometer. Large of test specimens (test pins) with 7 mm diameter and 27mm length were fabricated from plain and carbon filled PTFE cylindrical rods. In order to obtain reliable test results, it was planned to conduct many repeated trials for various combinations of applied load and various grades of abrasive emery paper for fixed speed of 400 rpm, time duration of 10 minutes and track diameter of 60mm respectively. Initial trials were conducted with various combinations of 10N, 15N and 20N load and abrasive papers of grade 320, 600, 800, 1000 and 1200. Consistent results were obtained with applied load of 10N and emery paper of grade 1000.

The test pins were pre worn with the abrasive paper with the same experimental parameters to ensure parallelism between the mating surfaces of the pin and the paper. This pre run ensures identical rough surfaces for all the pins before the actual test run.

Before start of the test run, the pin was securely clamped on to the pin holder and the abrasive paper was rigidly held on the top of the circular platform. After placing the selected dead weight of 10N on the weight platform, the pin surface was made to come in surface contact with the abrasive paper. The initial digital outputs of displacement (wear) and frictional force were set to zero through the control panel. Ensuring these set parameters, the tribometer was switched on for the test duration of 10 minutes. As the test progressed, continuous data of wear, fictional force and coefficient of friction displayed on the monitor were monitored. After the test run for 10 minutes, the output data was stored both in digital and graphical form for further study and analysis.

The same tribometer was used to carry out the wear tests in cryogenic environment with the help of pressurised LN2. The wear takes place within the space of a cryochamber. The cryochamber was partially filled with LN2 and allowed to cool and stabilise for some time. The chamber was raised up with the motorised jack till its top flange seals with the bottom surface of the cover plate with an O ring. Pressurised LN2 was supplied into this chamber allowing liquid to flow over the pin-disc interface. A stabilised cryogenic temperature of 98K could be obtained within the chamber. Wear tests were carried out following the same procedure adopted for room temperature tests. These results were compared with the room temperature data and analyzed.

V.RESULTS

The tribological behaviour of a material depends on various factors like wear, coefficient of friction, frictional force between contact surfaces, roughness surface, applied load, rotational speed, duration, etc. As the temperature changes, the material properties also tend to change and in turn influence the tribological behaviour. As mentioned earlier, the emphasis was laid on the influence of cryogenic temperature on the wear properties of PTFE.

Frictional force is a measure of the actual force between the rubbing surfaces. This depends on the applied load and is measured with the help of load cell. Coefficient of friction is the ratio of the frictional force to the applied load. This can range between 0 to1 in the extreme cases. Higher values of coefficient of friction indicate higher transmission of applied load to the interfacing surfaces. The total wear of the material in a wear test depends on the frictional force and the coefficient of friction.

The wear test results at room and cryogenic temperature are shown in the following figures 5, 6 and 7.

Figure 5. Wear at room and cryogenic temperature

PTFE is relatively a soft material. It exhibits low coefficient of friction and high wear rate. This limitation of high wear rate is overcome by impregnating PTFE with carbon since carbon particles exhibit low wear properties. As the temperature is lowered to cryogenic zone, strength and hardness of both PTFE and carbon filled PTFE increase. [5, 6] Due to these combined effects, the wear is expected to reduce at cryogenic temperature. As per the test results, the fall in total wear was significant for plain PTFE at cryogenic temperature. Carbon filled PTFE exhibited lower wear at room temperature as compared with plain material due to the inherent wear resistance characteristics of carbon particles. The reduction in wear at cryogenic temperature was less as compared with the data of plain PTFE.

Wear property of a material depends on the frictional force and the coefficient of friction in the interface between the pin and disc assembly. The results of both coefficient of friction and frictional force at room and cryogenic temperature are shown in the figures 6 and 7.

Figure 6. Frictional force at room and cryogenic temperature

Figure 7. Coefficient of friction at room and cryogenic temperature

Both frictional force and coefficient of friction are temperature dependent. As compared with plain material, carbon filled PTFE exhibits lower coefficient of friction and frictional force at room and cryogenic temperature. Since both these values are comparatively lower at cryogenic temperature, wear should be expected to reduce at cryogenic temperatures. This effect has been observed by the reduction in wear at low temperature zone.

VI.CONCLUSION Experiments have been have been carried to

study the tribological properties of plain and carbon filled PTFE material at room and cryogenic temperatures. The results indicated reduction in wear properties for both plain and carbon filled PTFE at cryogenic temperature.

There is scope for further research to study the tribological properties of PTFE at cryogenic temperature with various percentage compositions of carbon. It is also worth to extend studies on

tribological properties of cryotreated PTFE and its composites at cryogenic temperatures.

REFERENCES

[1] G.Theiler, “PTFE and PEEK Matrix composites for tribological applications at cryogenic temperature and in Hydrogen”,Federal Institute for Materials Research and Testing,Berlin (2005)

[2] G. Theiler, “ Friction and wear of PTFE composites at cryogenic temperature”, Tribology International Vol 35,pp 449-458 (2002)

[3] S.K.Biswas, “Friction and wear of PTFE – a review”, Wear Vol 158,pp 193-211(1992)

[4] T.Gradt et al,” Low temperature tribometers and the behaviour of ADLC coatings in cryogenic environment”, Tribology International Vol 34,pp 225-230, (2001)

[5] “Properties Handbook-PTFE”, Dupont [6] Randall F Barron “Cryogenic systems”, Oxford University

Press (1985)

Portable Imaging Flow Analyzer for Biological Research Applications

in Space

Veerendra Kalyan Jagannadh and Sai Siva Gorthi

Abstract—Cell viability assays are performed to assess theresponse of a population of biological cells to an external stimuli.Conventional optical microscopy is one of the standard techniquesused to perform cell viability assay. Due to their bulky nature,microscopes are not ideal candidates for studying the behaviourof biological specimen in space. In this article, we report theuse of a portable imaging flow analyzer based on a mobilephone to perform an automated cell viability assesment of agiven population of yeast cells. With the use of a cellphoneaugmented with off-the-shelf optical components and customdesigned microfluidics, we demonstrate a portable optofluidicdigital microscope. We demonstrate an enhanced throughput ofabout 450 cells/second, by implementing a multiple microfluidicchannel geometry.

Keywords—Portable Microscopy, Microfluidics, Flow Cytometry,High-throughput Imaging, Cell viability assessment.

I. INTRODUCTION

In the recent past, there has been a significant growth ofinterest in studying and understanding behaviour of biolog-ical cells in space. Researchers have attempted to performcell-culture and tissue engineering experiments in space tounderstand the behaviour of biological cells in low-gravityenvironments [1]–[3]. Cell viability assay is a fundamentalmethod used to study proliferation of cells in a given pop-ulation subjected to any given external stimuli.

Morphological changes or changes in membrane perme-ability are used to asses the viability of cells [4]–[6]. Viabilityassays can be loosely classified into two types: analysis ofwhole population and analysis of individual cells in a givenpopulation. Assessment of viability on a single cell levelprovides a more detailed result, when compared to bulkmeasurements carried out on the whole population [5]. Themembrane permeability and/or physiological state is inferredby assessment of exclusion of certain types of dyes or uptakeand retention of other types of dyes. One of the widelyused viability assessment techniques is the trypan blue dyeexclusion test. It is based on the principle that live cellswith an intact plasma membrane exclude trypan blue; whereasnon-viable cells take up trypan blue as their cell membraneis not intact and hence, unable to control the passage ofmacromolecules through the cell membrane.

In general, optical detection techniques: flow cytometryand microscopy are employed to asses the dye exclusion orretention in cells and thereby infer their viability [7]. Both

Veerendra Kalyan Jagannadh is a graduate student at the Department ofInstrumentation and Applied Physics, Indian Institute of Science

Dr. Sai Siva Gorthi is an assistant professor at the Department of In-strumentation and Applied Physics, Indian Institute of Science. [email protected]

STC Project Code - ISTC/IAP/SSG/299/2013

flow cytometry and microscopy are gold-standard techniquesemployed in medical diagnostics as well as basic biologi-cal research. Flow cytometry enables automated high-speedquantitative multi-parameter analysis of large population ofcells. In contrast to flow cytometry, microscopy has a simplerarchitecture and enables viability assessment of cells viaexamination of images of cells smeared on a slide. Whileboth flow cytometry and microscopy are widely used standardtechniques, their implementation in space / on flight is quitedifficult owing to their bulkyness.

While it is possible to perform cell viability assesment post-flight, the availability of compact, low-weight and automatedimaging systems would enable on flight cellular studies andfurther the progress of bio-medical / biological research inspace. Some of the recently reported portable microscopes [8]–[13] are relevant in this context. However, these portable mi-croscopes inherit some limitations of conventional microscopy:requirement of skilled personnel for sample handling andpreparation, need for focus adjustments and manual scanningof the slide through multiple field of views to acquire imagesof larger number of cells on the smear/slide etc.

On the other hand, there has been recent trend of enhancingthe throughput of conventional microscopy with the synergisticuse of optics and microfluidics. These imaging systems are ingeneral referred to as Imaging Flow Cytometry (IFC) systems[14], [15]. Most of the reported IFC systems in literatureemployed high-speed cameras, which tend to be bulky andhence are not so ideal for realization of imaging flow analyzers,which can be used on flight.

Following a similar approach, there have been a fewattempts to develop portable imaging flow analyzers [16],which employ low weight consumer electronic devices (likecellphones) for imaging and microfluidics for sample handling.However, throughputs in these devices are limited by the lowframe rates of acquisition. The possibility of enhancing thethroughput of portable imaging systems with the use of customdesigned microfluidic devices has not been investigated uponpreviously.

In this paper, while employing the principles of imagingflow cytometry, we demonstrate a microfluidics based portablebright-field imaging system with the use of a cell-phoneand low cost off-the shelf optical components. In this work,with the use of a multiple microfluidic channel geometry, wedemonstrate the enhancement of throughput in the contextof imaging flow analyzer based on low-frame rate imagingdevices. With the frame rate of acquisition, being only 30frames per second (fps), we demonstrate a throughput of about450 cells per second. Further, we demonstrate an importantapplication of such a portable imaging system by performinga cell viability assay on a given population of yeast cells.


Fig. 1. Schematic of the cellphone based portable microscope.

II. CELL-PHONE BASED PORTABLE DIGITALMICROSCOPE

As mentioned earlier, there have been several demonstra-tions of portable microscopy systems. Most of the previouslydemonstrated systems predominantly fall into two distinctcategories, they are lens-based [8], [10]–[12], [17] and lens-free systems [9]. In this work, we have adopted a lens-basedsystem, wherein we have used an eye-piece (10X) and ageneric objective (40X, N.A = 0.65) to turn a cellphone intoa microscope. A 3W LED augmented with a ground glassdiffuser (Grit = 600, Diameter = 1 inch) and an asphericcondenser lens (f = 20 mm, Diameter = 1 inch) have beenused to provide a uniform illumination on the sample plane.The schematic of the imaging system is shown in figure 1.

A. Characterization of the Imaging System

A 1951 USAF Negative resolution target (Edmund Optics55-622) has been used to characterize the imaging system. Thesystem field of view was estimated to be about ∼ 180 µmin diameter. The system is easily able to resolve the smallestfeature present on the target, which is a 0.78 µm feature(shown in figure 2). The line profiles across the images of1st, 2nd, 3rd elements of the 9th group have been shown (figure2 (c)).

III. INTEGRATED MICROFLUIDIC PORTABLE IMAGINGSYSTEM

Custom-fabricated microfluidic devices were integratedinto the portable microscope system so as to enable automatedimaging as in the case of a typical IFC system. Conventionalphoto-lithography and soft-lithography [18] techniques wereused to fabricate microfluidic devices compatible with thecompact digital microscope. The microfluidic devices werebonded to microscope cover slips (thickness = 0.13 mm) usingoxygen-plasma treatment, in order to seal the microfluidicchannels. Each device had parallel channels with a width =20 µm, depth = 6 µm and an inter-channel spacing of about60 µm . At the inlet of the microfluidic device, a spatialfilter was incorporated so as to filter out particles bigger thandimensions of the narrow portions of the channel to avoid

Fig. 2. Imaging system characterization using USAF 1951 negative resolutiontarget. (a)-(c) Images of 1st, 2nd and 3rd elements of the 9th group and theircorresponding line profiles. Length of scale bar is 5 µm

clogging of the channels/device. The representative schematicof the microfluidic device is shown in figure 3.

Fig. 3. Design of the microfluidic device. Device consisted of straightchannels which were 20 µm wide in the central portion of the device and48 µm at other locations.

An attachment to the cell-phone, for housing the optics(LED, diffuser screen, condenser and objective lens) and thereplaceable microfluidic device (cartridge) was fabricated. Theattachment consists of a slot (opening), so that inserting the mi-crofluidic cartridge into this slot positions it at the sample planeof the imaging system. The positioning of the microfluidicdevice in the sample plane, enables us to image the biologicalspecimen flowing through the microfluidic channels of thedevice. The image of the fully integrated automated portablemicroscope is shown in figure 4.

IV. SYSTEM THROUGHPUT CHARACTERIZATION IN FLOW

The achievable throughputs using the portable optofluidicmicroscope were investigated by imaging a suspension ofHuman Red Blood Cells (RBCs) in flow. The suspensionwas flown through the microfluidic device at a flow rate ofabout 40 µlh−1 and the videos of the flow stream wererecorded. Images of different cells were extracted from thevideos using background subtraction and morphological imageprocessing toolbox of MATLAB. Using the presented system,

Fig. 4. Image of the integrated opto-fluidic imaging flow analyzer.

Fig. 5. [Color Figure] Imaging system field of view, while imaging themicrofluidic device with lesser inter-channel spacing. Field of View - 196µm× 180 µm. Length of scale bar is 20 µm

we have been able to achieve imaging throughputs of upto3000 cells per minute. Higher flow rates can be used toensure more particles in the field of view and thereby higherpossible imaging throughputs. However, it was observed thatfurther increase in flow rates resulted in loss of fidelity inthe acquired images due to motion-blur. In order to enhancethe throughput of the system, a multiple channel geometrywith inter-channel spacing about 9.8 µm was employed. Animage of the field of view of the optofluidic microscope,while imaging the device with closely spaced channels isshown in figure 5. Multiple channels were used in order tosimultaneously image cells flowing across different channelsand thereby obtain an optimally high throughput. Using thismultiple channel geometry and optimizing the concentrationand pumping speeds for the given frame rate and exposure,we have been able to enhance the throughput of the system toabout 27000 cells per minute.

V. CELL VIABILITY ASSESSMENT OF YEAST CELLS

In order to demonstrate the relevance of the presentedsystem to perform cellular studies in space / on flight settings,

Fig. 6. [Color Figure] Quantitative assessment of staining in the given yeastcell population. Color and saturation level images of cells corresponding todifferent gated regions of the histogram have been shown. Length of scale baris 8 µm.

we have performed an automated cell viability assay of agiven population of yeast cells. Yeast species Saccharomycescerevisiae was cultured in YPD (yeast extract, peptone anddextrose) (Himedia G037) liquid medium overnight at 37Cin orbital shaking incubator. The cells were harvested afterincubation by centrifuging at 500g for about 10 minutes.Harvested cells were resuspended in distilled water. The yeastsuspension of appropriate dilution was divided into two partsand one part of it was subjected to heating at 55C for 5minutes to prepare dead cells. The dead cells were stainedwith 5% methylene blue for 10 minutes at room temperatureand were separated by centrifuging at 500g for 10 minutesand supernatant was discarded. The stained dead cells and theviable cells were mixed in distilled water. It is known fact thatonly dead cells would take up and retain the methylene bluestain [19].

The suspension of yeast cells was assessed using theportable optofluidic imaging flow analyzer system. The sus-pension was flown through the microfluidic device at a flowrate of about 40 µlh−1 and the videos of the flow stream wererecorded. A snapshot of the field of view (196 µm× 180 µm),while imaging the suspension of yeast cells is shown in figure5. By computationally assessing the color content of the imageof a given cell, we determine its viability status. Since, boththe process of image acquisition and assessment of viabilityare automated, errors / variations due to subjective perceptionwould be reduced to a minimum.

The frames of the video were extracted and processedusing the background subtraction to extract the images of cellsin MATLAB (R2013b, Mathworks Inc.). Images of the cellsextracted from videos were further processed to quantitativelyassess the relative amount of dye retention/exclusion in a givencell. The saturation (color content) in a given image of cellwas quantitatively assessed by counting the total number ofpixels above the saturation level of the background (of theimage). To get an insight into the distribution of the stainingwithin the population, the histogram of total number of stained

pixels for different images has been plotted (shown in figure6). The histogram corresponds to images of about 4006 cellscorresponding to data recorded for about 9 seconds.

As it can be understood from the histogram, the cellspresent in the gated region (a) are very feebly stained andhave their cell membranes intact, while the cells present inthe other regions allowed the dye to permeate through the cellmembrane and hence, their cell membranes were disrupted tosome extent. Automated assessment of dye exclusion or uptakeand retention provides a quantitative insight into the stage ofcell death over a large population of cells.

VI. CONCLUSION AND FUTURE SCOPE

In this work, we have presented a cellphone based imagingflow analyzer for applications in basic biological researchfor on flight cellular studies in space. We have presented aninvestigation into achievable throughputs with microfluidicsbased system-level integrated portable imaging systems, whichemploy inexpensive low frame-rate cameras (in our case 30fps). In addition, we have demonstrated an enhanced through-put of about 450 cells/second with the use of closely spacedmultiple channel geometry. With the use of the presented sys-tem, we have performed an automated cell viability assessmentof a given population of yeast cells. Cell viability assessmentis an important technique for use in cytotoxicity and othercell-based biological research.

Future work in this approach would involve improvingthe design of our microfluidic catridge to perform on-chipmixing of the stain / dye with the bio-sample [20] to enableautomatation of sample preparation needed for performingvariety of diagnostics and biological assays with the presentedoptofluidic imaging analyzer.

ACKNOWLEDGMENT

This work was carried out under a project funded by SpaceTechnology Cell(ISRO-IISc).

REFERENCES

[1] L. E. Freed, R. Langer, I. Martin, N. R. Pellis, andG. Vunjak-Novakovic, “Tissue engineering of cartilage in space,”Proceedings of the National Academy of Sciences, vol. 94,no. 25, pp. 13 885–13 890, Dec. 1997. [Online]. Available:http://www.pnas.org/content/94/25/13885

[2] W. M. Saltzman, “Weaving cartilage at zero g: the reality of tissueengineering in space,” Proceedings of the National Academy ofSciences, vol. 94, no. 25, pp. 13 380–13 382, Dec. 1997. [Online].Available: http://www.pnas.org/content/94/25/13380

[3] C. A. Nickerson, C. M. Ott, S. J. Mister, B. J. Morrow, L. Burns-Keliher, and D. L. Pierson, “Microgravity as a novel environmentalsignal affecting salmonella enterica serovar typhimurium virulence,”Infection and Immunity, vol. 68, no. 6, pp. 3147–3152, Jun. 2000.[Online]. Available: http://iai.asm.org/content/68/6/3147

[4] W. Strober, “Trypan blue exclusion test of cell viability,” Curr ProtocImmunol, vol. Appendix 3, May 2001.

[5] M. J. Stoddart, “Cell viability assays: introduction,” Methods Mol. Biol.,vol. 740, pp. 1–6, 2011.

[6] S. Johnson, V. Nguyen, and D. Coder, “Assessment of cell viability,”Curr Protoc Cytom, vol. Chapter 9, p. Unit9.2, 2013.

[7] A. Kummrow, M. Frankowski, N. Bock, C. Werner, T. Dziekan, andJ. Neukammer, “Quantitative assessment of cell viability based on flowcytometry and microscopy,” Cytometry, vol. 83A, no. 2, pp. 197–204,Feb. 2013.

[8] A. Skandarajah, C. D. Reber, N. A. Switz, and D. A. Fletcher,“Quantitative imaging with a mobile phone microscope,” PLoSONE, vol. 9, no. 5, p. e96906, May 2014. [Online]. Available:http://dx.doi.org/10.1371/journal.pone.0096906

[9] M. Lee, O. Yaglidere, and A. Ozcan, “Field-portable reflection andtransmission microscopy based on lensless holography,” BiomedicalOptics Express, vol. 2, no. 9, pp. 2721–2730, Sep. 2011. [Online].Available: http://www.opticsinfobase.org/boe/abstract.cfm?URI=boe-2-9-2721

[10] J. S. Cybulski, J. Clements, and M. Prakash, “Foldscope: Origami-basedpaper microscope,” PLoS ONE, vol. 9, no. 6, p. e98781, Jun. 2014.[Online]. Available: http://dx.doi.org/10.1371/journal.pone.0098781

[11] A. Arpa, G. Wetzstein, D. Lanman, and R. Raskar, “Single lens off-chipcellphone microscopy,” in 2012 IEEE Computer Society Conference onComputer Vision and Pattern Recognition Workshops (CVPRW), 2012,pp. 23–28.

[12] S. Schaefer, S. A. Boehm, and K. J. Chau, “Automated,portable, low-cost bright-field and fluorescence microscope withautofocus and autoscanning capabilities,” Applied Optics, vol. 51,no. 14, pp. 2581–2588, May 2012. [Online]. Available:http://ao.osa.org/abstract.cfm?URI=ao-51-14-2581

[13] D. Shin, M. C. Pierce, A. M. Gillenwater, M. D. Williams, andR. R. Richards-Kortum, “A fiber-optic fluorescence microscope usinga consumer-grade digital camera for in vivo cellular imaging,” PLoSONE, vol. 5, no. 6, p. e11218, Jun. 2010. [Online]. Available:http://dx.doi.org/10.1371/journal.pone.0011218

[14] K. Goda, A. Ayazi, D. R. Gossett, J. Sadasivam, C. K. Lonappan,E. Sollier, A. M. Fard, S. C. Hur, J. Adam, C. Murray,C. Wang, N. Brackbill, D. D. Carlo, and B. Jalali, “High-throughput single-microparticle imaging flow analyzer,” PNAS, vol.109, no. 29, pp. 11 630–11 635, Jul. 2012. [Online]. Available:http://www.pnas.org/content/109/29/11630

[15] E. Schonbrun, S. S. Gorthi, and D. Schaak, “Microfabricatedmultiple field of view imaging flow cytometry,” Lab on a Chip,vol. 12, no. 2, pp. 268–273, Dec. 2011. [Online]. Available:http://pubs.rsc.org/en/content/articlelanding/2012/lc/c1lc20843h

[16] H. Zhu, S. Mavandadi, A. F. Coskun, O. Yaglidere, and A. Ozcan,“Optofluidic fluorescent imaging cytometry on a cell phone,” AnalyticalChemistry, vol. 83, no. 17, pp. 6641–6647, Sep. 2011. [Online].Available: http://dx.doi.org/10.1021/ac201587a

[17] Z. J. Smith, K. Chu, A. R. Espenson, M. Rahimzadeh, A. Gryshuk,M. Molinaro, D. M. Dwyre, S. Lane, D. Matthews, andS. Wachsmann-Hogiu, “Cell-phone-based platform for biomedicaldevice development and education applications,” PLoS ONE,vol. 6, no. 3, p. e17150, Mar. 2011. [Online]. Available:http://dx.doi.org/10.1371/journal.pone.0017150

[18] Y. Xia and G. M. Whitesides, “Soft lithography,” Annual Reviewof Materials Science, vol. 28, no. 1, pp. 153–184, 1998. [Online].Available: http://dx.doi.org/10.1146/annurev.matsci.28.1.153

[19] A. Bonora and D. Mares, “A simple colorimetric method fordetecting cell viability in cultures of eukaryotic microorganisms,”Current Microbiology, vol. 7, no. 4, pp. 217–221, Jul. 1982. [Online].Available: http://link.springer.com/article/10.1007/BF01568802

[20] A. P. Tan, J. S. Dudani, A. Arshi, R. J. Lee, H. T. K.Tse, D. R. Gossett, and D. D. Carlo, “Continuous-flowcytomorphological staining and analysis,” Lab on a Chip,vol. 14, no. 3, pp. 522–531, Dec. 2013. [Online]. Available:http://pubs.rsc.org/en/content/articlelanding/2014/lc/c3lc50870f

ISTC/MET/PK/0305

PROCESSING OF METALLIC THERMAL INTERFACE MATERIALS USING LIQUID

PHASE SINTERING FOLLOWED BY ACCUMULATIVE ROLL-BONDING

Deepak Sharma1, Rajesh Kumar Tiwari

2, Ramesh Narayan P.

3 and Praveen Kumar

1

1 Department of Materials Engineering

Indian Institute of Science, Bangalore 2Department of Manufacturing

National Institute of Foundry and Forge Technology, Ranchi 3MCD / MMG,

VSSC, ISRO P.O., Trivandrum

[email protected]

Abstract — This study reports on the processing and

characterization of composite solders, which comprise Cu, a

high-melting phase (HMP) embedded in In matrix, a low-

melting phase (LMP), produced by liquid phase sintering (LPS)

followed by accumulative roll-bonding (ARB). These solders

combine high electrical and thermal conductivities with high

mechanical compliance, and are suitable for a range of next-

generation thermal interface material (TIM) and interconnect

applications. Following the previously published work, an In-

40%Cu was found to possess the requisite combination of

compliance and conductivity, we start with effect of different

short sintering periods (30 and 60 s at 160 ºC each) on

compressive strength and electrical conductivities for both as-

sintered samples and samples processed through 0-10 passes of

ARB. Furthermore, rolling to a high compressive strain (50%)

increases the conductivity without significant increase in flow

stress; these findings prove to be promising for producing novel

TIM for new generation, advanced microelectronic packages.

I. INTRODUCTION

A continuous increase in computational power and the

miniaturization of the modern microelectronic devices, lead a

drastic increase in requirements for heat dissipation via TIMs

which cannot be met by the existing TIMs. Hence, the

packaging of advanced next generation microelectronic

devices demands development of new alloys with (1) high

thermal conductivity, (2) high shear compliance with

moderate compressive stiffness under creep conditions, and

(3) low melting temperature. To simultaneously attain the

above characteristics in a material, a novel composite

architecture, as shown in Fig. 1, composing of a highly

conductive HMP (e.g., Cu, Sn, etc.) uniformly distributed in

highly compliant LMP (e.g., In, Bi, etc.) matrix, has been

proposed and produced via LPS [1-5].

Fig. 1: Schematic of idealized microstructure with a uniform distribution of

HMP and LMP [1].

LPS of a HMP and a LMP above the melting temperature

of LMP has been suggested as a method to produce a

composite with the architecture shown in Fig. 1 [1,6]. A

composite system with Cu as the HMP and In as LMP was

chosen, one of the major advantages associated with In-Cu

system is that Cu is one of the materials with highest (thermal

as well as electrical) conductivity. As indicated in survey [1,

3, 6], current work involves an In-40vol.% Cu composition,

which possess the requisite combination of compliance and

conductivity. Preliminary experiments on Cu-In showed that

thermal conductivity of the composite solders were

significantly greater than that of In, although the flow stress

was high, most probably because of reactions between Cu and

In, and the formation of a solid skeleton structure of the HMP

and intermetallic compounds (IMCs) [6]. However, a critical

observation of the microstructures produced in the previous

works [1, 3, 6] reveals that: (i) the dispersion of Cu particles

was non-uniform in In matrix, resulting in agglomeration of

Cu particles in certain regions; this resulted in non-uniform

compliance (and equivalently, stiffness) and hence pre-mature

failure of the material, and (ii) the minimum thickness of the

Cu-In composites cannot be arbitrarily small, as the thickness

of such a composite is controlled by the diameter of Cu

particles and their agglomerates. These make these materials

unsuitable for the advanced microelectronic packages, which

have space only for few hundred microns thick TIM.

This work addresses the above two challenges by

suggesting implementation of a novel processing route

consisting of a combination of LPS and ARB, which is a

severe plastic deformation (SPD) technique. It is speculated

that ARB will not only flatten the Cu particulates but will also

increase the uniformity in mixing of Cu particles in Indium

matrix. Not only such an approach shows potential to resolve

the above two issues with Cu-In based TIMs, but it also

improve the thermal conductivity of such a composite, in

corroboration with the preliminary finite element analysis [7].

II. EXPERIMENTAL

Spherical Cu powders with average particle sizes of 10 µm

and 99.9% purity, were etched using 10% HCl solution for 10

min to remove the native oxide layer. The etched powder was

thoroughly rinsed using deionized water and isopropyl

alcohol (IPA) and dried by storing in vacuum (10-4

torr) for

12 h. An appropriate amount of Cu powder (40% volume

fraction) was mixed with10-40 µm diameter, 99.99% pure In

powder by vigorously shaking in a glass-vial inside a glove-

box under N2 atmosphere. Subsequently, the mixed powder

was uniaxially compressed by using hydraulic press with a

load of 4 MPa for 45 seconds, in a lubricated hardened steel

die to produce green pellets of 10.1 mm diameter and 1.5

mm height with a relative density of 88 ± 1%. The density

measurement for green pellets was conducted by using


mailto:[email protected]

geometry method, which was confirmed to be consistent with

that of Archimedes principle.

The green pellets were encapsulated in Al foil, and LPS

was done at 160 ºC by dipping them in a pre-heated, properly

agitated (using a magnetic stirrer for establishing a uniform

temperature distribution) 400 mL silicone-oil bath for

different lengths of time (30 and 60 s), followed by quenching

in water so as to inhibit the formation of IMCs. Sintered

relative density measurement was performed using

Archimedes principle so as to avoid the errors caused by

possible deviation in cylindrical shape of pellets during LPS.

ARB, ranging from 1 to 10 numbers of passes, and each

imposing a strain of ~50%, were conducted on the sintered

samples. A sintered sample was rolled, sectioned in two equal

halves along the “original” longitudinal direction, degreased

by generously applying acetone, stacked in such a way that

the surfaces that were originally perpendicular to the

cylindrical axis were lying adjacent to each other, and again

rolled for next cycle.

A few samples were prepared where reduced graphene

oxide (rGO) was placed in between the two surfaces of the

sample prior to the ARB processing. Electrical conductivity

and hence thermal conductivity of these samples were

measured.

Microstructures of the samples were characterized (both of

as-sintered and ARBed samples). The as-sintered pellet was

cross sectioned along transverse direction while the ARBed

samples were cross-sectioned along both transverse and

longitudinal directions. The cross-sections were observed

under a scanning electron microscope (SEM), using

backscattered and secondary electrons (BSE and SE,

respectively), as well as energy dispersive X-ray (EDX)

spectroscopy (FEI Quanta model 200).

The stress-strain behaviour of the LPS solder was

characterized by using an Instron Testing Machine (Instron

5967), which uses a 5 kN load cell to record the applied load.

Finally, the electrical resistivity of the sintered samples were

measured by using a custom designed 4-point Kelvin probe in

conjunction with a nano-voltmeter (Keithley Multi-meter,

Model 2010) to eliminate any contact resistance. Since in

metallic materials, electronic conduction is responsible for

current and heat flow, the electrical and thermal

conductivities are proportional to each other. Thus the

electrical resistivity measurements were used to calculate

thermal conductivity of the composite using Wiedemann-

Franz Law, which is given as follows:

K

LT

(1)

where K and σ are the thermal and electrical conductivities,

respectively, L is Lorenz number, and T is the ambient

temperature, which was room temperature (i.e., ~303 K) in

the present study.


Fig. 2 shows the effect of green density and sintering time

on sintered density of In-40%Cu composite; density values

are given in terms of relative density (calculated by taking

percentage of fully dense or theoretical density of composite

that is 7.962 g/cm3) and densification was calculated by

taking the increase in density relative to initial density. As

shown in Fig. 2, a densification of > 10% occurred when

green density was small (< 85%) and the densification

monotonically decreased with the green density. Higher

densification in these low aspect ratio samples can be

attributed to high heat transfer rate due to large surface area.

A decrease in densification with green density can be

attributed to the decrease in the volume fraction of open pores

which, unlike closed pores, are removed during sintering

process.

0

2

4

6

8

10

12

84 85 86 87 88 89 90

604530

Den

sific

atio

n (%

)

Relative Green Density (%)

In - 40 Vol. % CuSintered @160 0CSintering time (s)

(b)

Fig. 2: Variation of densification over LPS with relative green density for

different LPS periods.

Fig. 3 shows the effect of ARB on the relative density of

Cu-In composite. Interestingly, ARB did not monotonically

affect the densification of the sintered samples: At first, the

density of the sample decreased; however, then it increased

monotonically approaching almost the as-sintered density. A

decrease in relative density of sample was not expected and

this might be attributed to collapse of “apparent” well bonded

microstructure of as-sintered sample.

84

86

88

90

92

94

0 1 2 3 4 5 6

In-40Vol%Cu

Rel

ativ

e de

nsity

(%)

Number of ARB passes

Sintered @1600 for 60 s

Fig. 3: Effect of ARB processing on the relative density of sintered samples.

Fig. 4 shows representative SEM micrographs of as-

sintered samples. As shown in Fig. 4a, a low magnification

micrograph of as sintered sample, the sample consisted of a

few agglomerated regions of cavities or pores (black regions)

and also the distribution of Cu and In was not uniform. The

latter is shown better in Fig. 4b, which is a high

magnification micrograph. A close inspection of Fig 4b also

shows that the bonding between Cu and In may not be very

strong following sintering for 60 s; this may explain the initial

drop in density following the first ARB pass (Fig 3).

(a) (b)

Fig. 4: SEM micrographs of as-sintered samples produced after LPS for 60

seconds (a) low and (b) high magnification. Images were taken using back-

scattered electron where dark and bright regions are Cu and In, respectively, whereas black regions denote pores.

Fig. 5 shows the effect of ARB processing on the

microstructure of In-Cu samples. A comparison of Figs 4 and

5 readily reveals the following: (i) overall the pore density did

not change in the beginning of the ARB processing, (ii) the

distribution of Cu and In became more uniform with an

increase in ARB passes, (iii) In truly became continuous

matrix where Cu was spread following ARB processing, and

(iv) the interface between Cu and In appears to be free of

pores indicating better contact between Cu and In. While the

outcome (i) is not dersired but the other aforementioned

outcomes of ARB were intended effects.

(a) (b)

(c)

Fig. 5: High magnification SEM micrographs of samples processed through ARB for (a) 1, (b) 2 and (c) 5 passes.

The impact of sintering time on stress-strain (σ−ε)

behaviour at strain rates varying from 10-3

s-1

to 10-1

s-1

is

shown in Fig. 6. All samples were tested at room temperature

with relative green densities of ~85% and an aspect ratio of

~0.5. Fig. 6 reveals that flow stress of composite increased

with sintering time (this may be attributed to an increase in

volume fraction of IMCs) and also with applied strain rate.

We can speculate some strengthening due to Cu-In interface,

which may act as dislocation source [3]. With few exceptions,

all samples showed significant softening after limited strain

hardening. This indicates that dislocation generation rate was

significantly lower than dislocation recovery. This is due to In

as test temperature is close to 0.7Tm, where Tm is melting

temperature of In, and this enables fast recovery. To further

analyse the effect of strain rate on mechanical behaviour,

yield strength (σYS) and strain rate sensitivity was calculated

as follows:

m

0.2% YS 1K (2)

where m is strain rate sensitivity and K1 is a constant.

0

5 106

1 107

1.5 107

2 107

2.5 107

0 0.25 0.5 0.75

True

Stre

ss (P

a)

True Strain

60 sec 45 sec 30 sec

10-310-210-1

Strain Rate (s-1)

Fig. 6: True Stress-Strain Plot of as-sintered In-40%Cu having relative sintered density of 87%. For plotting stress-strain data for 45s and 30s

sintered samples, strain axis was shifted by 0.25 & 0.5, respectively.

Fig. 7 shows the effect of sintering time and strain rate on

σYS of as-sintered samples. As shown in Fig. 7 that Eq. (2)

aptly captures the effect of strain rate on σYS and the as-

sintered In-Cu composites showed significant strain rate

sensitivity. Fig. 7 also reveals that ‘m’ monotonically

increased with decreasing sintering period. ‘K1’ seemed to be

independent of sintering period, this implies that σYS,

irrespective of sintering period, would be same if loading rate

was 1, and at very small strain rate, σYS would be widely

different.

6 106

7 106

8 106

9 106107

2 107

10-5 10-4 10-3 10-2 10-1 100

30 sec45 sec60 sec

y = 1.47e+07 * x^(0.066638) R= 0.72987

y = 1.6489e+07 * x^(0.054552) R= 0.98435

y = 1.6872e+07 * x^(0.022814) R= 0.94306

YS

(Pa)

Strain Rate (s-1)

In - 40 Vol. % CuSintered @160 0C

Fig. 7: Dependence of YS on applied strain rate.

Study of stress-strain behaviour of ARBed samples was

done at room temperature, with varying strain rate of 10-4

to

10-1

s-1

; Figs 8a and 8b show representative stress-strain and

effect of strain rate on σYS for samples processed through 1

pass of ARB. Comparison of Fig. 8 with Figs 6 and 7 shows

that the general stress strain behaviour of samples following

ARB processing qualitatively remained the same, i.e., the

samples showed some strain hardening as well as the strain

rate sensitivity.

0

5 106

1 107

1.5 107

2 107

2.5 107

0 0.05 0.1 0.15 0.2 0.25

10-1

10-3

True

Stre

ss (P

a)

True Strain

In-40Vol% CuSintering @ 160

0C for 30s ARB Pass: 1 (Strain : 50%)

(a)

6 106

7 1068 1069 106

107

2 107

3 107

10-5 10-4 10-3 10-2 10-1 100

30 sec45 sec

y = 2.3912e+07 * x^(0.10075) R= 0.93768

y = 2.3741e+07 * x^(0.080788) R= 0.97864

Y

S (Pa)

Strain Rate (s-1)

In-40 Vol.%CuSintering @ 160

0C, 1 pass, strain = 50 %

(b)

Fig. 8: (a) True stress-strain behaviour and (b) σYS versus strain rate plots of

single pass rolled In-40Cu composites.

Fig. 9 shows the effect of ARB passes on the stress-strain

response of the In-Cu samples at the strain rate of 10-3

s-1

.

Since, as shown in Fig. 3, the density of samples following

ARB processing did not show a monotonous variation in the

density of the sample and the density of a sample affects the

flow stress of a material, the stress endured by the samples

were normalized by the square of the density of the sample.

Such normalization with respect to the density was conducted

following standard results on the cellular structure. Fig. 9

readily reveals that the flow stress of the material did not

change with the ARB passes. This can be attributed to the fact

that ARB proffered attainment of continuous channel of In,

which would then accommodate most of the applied strain, as

envisioned for such material system.

0

0.001

0.002

0.003

0.004

0.005

0.006

0 0.05 0.1 0.15 0.2 0.25

In-40Vol%Cu

0 pass1 pass2 pass5 pass

True

stre

ss/(D

ensi

ty)2

True strain

Strain rate: 10-2 s-1

Sintered@1600C

ARB strain per pass: 50%

Fig. 9: True stress-strain behaviour of sintered In-40Cu composites processed

through various ARB passes, each pass imposing a strain of 50 %.

Electrical resistivity of composites was measured using a

custom design 4-wire Kelvin probe shown in Fig. 10.

Electrical conductivity and derived thermal conductivity

using Wiedemann-Franz Law (Eq. (1)) was calculated by

electrical resistivity for as-sintered and ARBed samples

(shown in Fig. 11b). Fig. 11a shows that electrical resistivity

decreased with increase in sintering time and number of ARB

passes. It appears to be against the observation of Cu-In IMC

for higher sintering period; however, it indicates the existence

of good contact or wetting between Cu and In; the latter may

be responsible for reducing the interface contact resistance

between the two phases of the composite. Fig. 11b shows that

thermal conductivity of as-sintered samples were actually still

considerably small compared to pure In and Cu, and this can

be attributed to poor relative density and existence of pores as

shown in Figs 4. However, the conductivity of the In-Cu

composites significantly increased with an increase of the

ARB passes. As shown in Fig. 11b, thermal (as well as

electrical) conductivity of In-Cu composites became greater

than that of pure In and Ni after 10 passes of ARB. Although

the maximum value attained in this study is smaller than that

of Al or Cu, it clearly shows that ARB can greatly improve

the thermal conductivities of samples.

Fig. 10: Custom made stage for mechanically fixing the sample

(parallelepiped shape) and probes.

Fig. 12 establishes the effect of relative density on the

electrical (and hence thermal) conductivity of In-Cu

composites following ARB processing. The linear variation

between these two shows that density of the sample is an

important parameter determining the conductivity of the

sample. Also, the improvement of Cu-In interface or

significant reduction in the interfacial contact resistance, if

any, occurred only just after the first pass of ARB.

1 10-7

2 10-7

3 10-7

4 10-7

5 10-7

6 10-7

25 30 35 40 45 50 55 60 65

As-sintered1 Pass2 Passes

Res

istiv

ity (

m)

Sintering time (s)

In - 40 vol. % CuSintered @160 0C

(a)

0

100

200

300

400

500

0 1 2 3 4 5 6 7Pb

SnIn

Ni

Al

Ag

Cu

Au

Ther

mal

Cond

uct

ivit

y (

W-K

)

Electrical Conductivity (x 107/ -m)

Wiedemann-Franz LawIn - 40 vol. % Cu

Sintered @160 oC for 45 sec

13 Pass

(b)

Fig. 11: Variation of (a) electrical resistivity with sintering time, and (b)

thermal conductivity of as-sintered and ARBed samples of In-Vol.40% Cu with electrical conductivity. The broken line in (b) represents Eq. (1).

7 106

8 106

9 106

1 107

1.1 107

1.2 107

1.3 107

1.4 107

1.5 107

84 86 88 90 92 94

In-40Vol. % Cu

3060

Ele

ctric

al c

ondu

ctiv

ity(/

m)

Relative density after ARB

Sintered@1600CSintering time (s)

Fig. 12: Variation of electrical conductivity with density of the In-40 vol. %

Cu samples processed through ARB.

Fig. 13 shows the variation of electrical (and hence thermal)

conductivity of ARBed In-Cu composite as function of their

yield strength; the variation is monotonous. However, Fig. 13

reveals a striking result: an increase in the conductivity by

almost of factor of 2 was observed in In-Cu samples

following ARB processing while the yield strength increased

only by ~10% in between the same range. This actually

satisfies the most stringent criteria of the desired process of

attaining high conductivity without compromising

compliance.

8 106

1 107

1.2 107

1.4 107

1.6 107

39 40 41 42 43 44 45

In-40Vol%Cu

Ele

ctric

al c

ondu

ctiv

ity(/

m)

Yeild stress (MPa)

Sintering@1600C for 30 sec

Fig. 13: Variation of electrical conductivity with yield strength of the In-40

vol. % Cu samples processed through ARB.

Further microstructural study was conducted to quantify

the homogenisation and breaking of agglomerates of the Cu

particles, as these are the probable changes that are affecting

the conductivity. Size distribution plots shown in Fig. 14

clearly reveal a narrowing of the range of Cu-particle size

with number of ARB passes as well as consistent with Fig. 5,

the distribution of Cu and In became homogenous with ARB

passes. This supports the proposed hypothesis of

homogenization with ARB.

0

10

20

30

40

50

3 5 7 9 11 13 15 17 19

Cou

nt

Diameter range (m) (a)

0

20

40

60

80

100

3 5 7 9 11 13 15 17 19

Cou

nt

Diameter range (m) (b)

0

20

40

60

80

100

120

3 5 7 9 11 13 15 17 19

Cou

nt

Diameter range (m) (c)

Fig. 14: SEM micrographs of sintered In-Cu samples processed through

ARB for (a) 1 pass, (b) 2 passes, (c) 5 passes. The right column histograms

show Cu-particle size in μm. SEM micrographs were taken using back-scattered electrons wherein dark and bright regions are Cu and In,

respectively, and black regions represent pores.

Fig. 15 shows the variation of the mean Cu particle size as

function of the ARB passes; Fig. 15 shows Cu particle size as

measured along the thickness (longitudinal) and rolling

direction (transverse) directions. In each direction, the Cu

particle size decreased with increase in the ARB pass.

8

8.5

9

9.5

10

0 1 2 3 4 5 6

LongitudinalTransverse

Cu-

size

(m

)


Cross sectional view

In - 40 vol. % Cu

Sintered @ 160 oC for 45 s

Fig. 15: Variation of Cu-particle size with number of ARB passes for In-Cu samples sintered for 45sec.

Fig. 16 shows a representative microstructure of In-Cu

composites processed through 1 and 2 passes of ARB wherein

a thin sheet of rGO was placed in between the “mating”

surfaces of the two In-Cu pieces. Fig. 16 clearly shows that

highly conductive materials, such as graphene, carbon

nanotubes, etc., can easily be embedded in In-Cu composites

through ARB processing of sintered sample.

(a) (b)

Fig. 16: Representative micrographs showing the incorporation of reduced

graphene oxide (rGO) in In-Cu composites. The black elongated region

shows rGo layer.

Fig. 17 shows the effect of rGO on the thermal

conductivity of In-Cu composites. As expected, Fig. 17

clearly indicates a dramatic increase in the total thermal

conductivity of the In-Cu composites if rGO is placed

embedded into the In-Cu composites.

0

100

200

300

400

500

600

700

800

1 2

In-40Vol%Cu + rGO

Without GrapheneWith Graphene

Ther

mal

Con

duct

ivity

(W/m

k)


Sintering @ 160 oC for 30 s

Fig. 17: Variation of Cu-particle size with number of ARB passes for In-Cu samples sintered for 45sec.

IV. CONCLUSIONS AND FUTURE WORKS

This study reports on the processing and characterization

of LPS In-Cu solders, starting with a green pellet, which

consists predominantly of particles of low melting In (60 %

by vol.) and smaller amount of particles of high melting Cu.

Short term LPS was conducted, which showed densification

dependence on sintering time. Once the metal compact

densified by LPS, it was processed through 1 to 10 passes of

ARB wherein each pass of rolling imposed a compressive

strain of 50%. Although the ARB did not cause a monotonous

increase in the density of the sample, the distribution of Cu

and In became uniform. ARB processing actually led to

uniform distribution of Cu (the HMP) into continuous matrix

of In (the LMP) which was never attained just after liquid

phase sintering. ARB also led to decrease in the Cu particle

size and hominization in the particle size distribution. The

electrical and thermal conductivities of composite were

observed to be increasing with number of passes of ARB and

with sintering period. Furthermore, the electrical and thermal

conductivities of In-Cu composite solders were increased by

more than an order of magnitude if a thin layer of rGO was

embedded into In-Cu composite through ARB processing.

Electrical Conductivity measurements confirm that

combining LPS with ARB is a promising route to meet the

requirements of a TIM.

Future work involves the mechanical characterization of

In-Cu/rGO samples. Also, mechanical response of all

processed In-Cu composite solders (with or without rGO)

under shear loading and creep conditions will be evaluated.

ACKNOWLEDGMENT

Authors would like to thank IISc-ISRO STC for the

financial support.

REFERENCES

[1] P. Kumar, I. Dutta, R. Raj, M. Renavikar, and V. Wakharkar, Proc.

Conf. on Thermal Issues in Emerging Technologies (ThETA 2), Cairo,

Egypt, IEEE, 17-20 Dec.(2008) pp. 339-346.

[2] “Solder Thermal Interphase Materials”. Available online:

http://www.indium.com/TIM/solutions/index.php.

[3] J. Liu, P. Kumar, I. Dutta, R. Raj, M. Renavikar and V. Wakharkar, J Mater Sci 46 (2009) 7012.

[4] J. Liu, P. Rottmann, S. Dutta, P. Kumar, R. Raj, M. Renavikar and I.

Dutta, Proceeding of the 12th Electronics Packaging Technology Conference (EPTC) (IEEE, Singapore) (2009) pp. 506-511.

[5] J. Liu, P. Kumar, I. Dutta, and M. Renavikar, Inter PACK, San

Francisco, USA (2009). [6] I. Dutta, R. Raj, P. Kumar, T. Chen, C. M. Nagaraj, J. Liu, M.

Renavikar and V. Wakharkar, J Electron Mater 38 (2009) 2735.

[7] P. Kumar and S. Awasthi, J Comp Mater. 48 (2014) 1391

Development of Nanoparticle Based Radiation Detectors for Space Applications

B.H.M. Darukesha1, V. Radhakrishna1, M. Ravindra1 and K. Rajanna2

`Abstract: This paper contains the details of the work carried out for the development of nanoparticle based radiation detectors for space applications. The design of experiments and fabrication of detection medium are explained. Detailed testing and characterization work planned are indicated. Key words: Radiation Detectors, Nanoparticles, Scintillators

I. INTRODUCTION

Main idea of this research is to use nanoparticles as energy conversion sites for the detection of ionising radiation. Nanoparticles embedded in a plastic scintillating medium would convert radiation energy into fast electron and the later would scintillate the medium. Conventional techniques using photomultiplier tubes and photodiodes would be used to detect the light output. The Motivation of this work is to enhance the efficiency, resolution and range of radiation detection. Flexibility in ‘form factor’ is a significant advantage in Space Applications. The design of experiments involves optimization of species of nanoparticles, their size, and percentage of loading and thickness of the medium. Fabrication of 150 numbers of detection medium covering these parameters is completed. The First cut results are expected soon. 1ISRO Satellite Centre, Bangalore-560017, 2Department of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore-560012 Project Number: ISTC/PIN/KR/300

II. THEORY Upon interacting with the nanoparticle in the medium, part of the energy of radiation is converted as fast electrons. The resin chosen to embed nanoparticles is such that these fast electrons scintillate to release light in the suitable wavelength of around 425 nm. This range is suitable because most of the light counting apparatus viz. photomultiplier tubes and photodiodes have the sensitivity peak around it. Efficiency of a nanoparticle in energy conversion is proportional to Z4

, where Z is the effective

atomic number. Since nanoparticles are smaller in size than the wavelength of the light scintillated, light would escape from the medium.

III. EXPERIMENTS Nanoparticles of Lead oxide, Tungsten oxide, Silicon oxide and Gadolinium oxide are chosen in this study. Commercial plastic scintillator which has a peak emission around 425 nm has been used. Plastic scintillator used has 3 parts. Catalyst and the solvent are mixed first and then with the resin in the specified ratios. Vacuum settling is carried out at 10 mbar for about 20 minutes to get rid of air-bubbles. As control samples, without nanoparticles, the compound is poured into moulds prepared by machining the Teflon. Vacuum settling was again performed at 10 mbar for 10 minutes. Whole of the mould was kept in de-ionised water at 47 ºC for 14 days. Temperature was monitored twice a day using a thermocouple and whenever required, de-ionised water pre-heated to 47 ºC was poured to ensure that the cast is always immersed. In order to form a thin film for the detection medium, on a molded Teflon, the compound was spun. Curing as mentioned above was performed for this also.


After 14 days of curing, the mold was taken out and dried. The medium were baked at 80 ºC for 8 hours in inert Nitrogen atmosphere. A dedicated oven was used for this purpose. Subsequently, the detection medium was separated from the mould using a surgical blade as shown in Fig. 1.

Fig. 1 Control samples, without any nanoparticles It is found that separation becomes difficult when Teflon mold is used. Surface of the mold showed roughness complementing to the surface of the Teflon. Cleaning the surface of the mold was tried with alcohol and acetone. While neither of them was able to clean the surface, acetone showed an adverse effect to soften the mold. Slicing of top surface with blade was found to leave the surface clean. It was noted that there would be reduction of the thickness of mold so as to form a curved top surface. This reduction was considered for calculating the percentage of loading of nanoparticles during the fabrication of samples with nanoparticles. Fabrication of nanoparticle based medium followed the similar sequence. After the first vacuum settling, nanoparticles with the chosen species & size, percentage of loading were mixed with resin and were cast as shown in Fig. 2 (a) and (b).

Fig. 2 (a) Mixing of nanoparticle and the resin

Fig. 2 (b) poring the compound into mold

Molds were prepared out of glass. Glass tubes of 10 mm diameter were cut in barrel shape. Size of 10 mm is chosen to suit the typical diameter of the photomultiplier tube. Teflon tubes with 9 mm diameter and 12.5 mm diameter were also used. These molds are shown in Fig. 3.

Fig. 3 Different types of molds used Percentage of loading was set to 0.5%, 1%, 1.5%, 5%, 10%, 20% and 40%. Last percentage was to develop radiation shield for protecting the electronic devices used in space applications. Different thicknesses of medium were controlled by pouring the compound to different heights of these molds. One-quarter, on-half, three-fourth and full heights were filled.

Curing of the mold was performed in an oven as shown in Fig. 4.

Fig. 4 Curing of mold at 47 ºC for 14 days in oven The Glass tubes were broken easily and Teflon tubes were cut vertically. The Surfaces were smooth. The detection media thus obtained are shown in Fig. 5.

Fig. 5 Nanoparticle based radiation detection medium

IV. TESTING & CHARACTERISATION

Nanoparticle based radiation detection medium are being subjected to SEM study to ascertain the uniformity of distribution. The test set-up as shown in Fig. 6 is being arranged at ISITE, ISAC, ISRO. Co-60 Gamma source is chosen for the initial testing. Further to testing, effect of various environmental tests viz. high temperature storage, thermal cycling, and vibration and thermo-vacuum tests would be performed. The performance of samples before and after the tests will be compared.

Optical grease

Fig. 6 Test set-up

V. CONCLUSIONS Fabrication of nanoparticle based radiation detectors is completed. Fabrication steps are discussed in detail. Experiments for the evaluation of uniform distribution of NPs in the medium are being performed. Characterization of detection medium is expected to be completed soon.

REFERENCE

[1] A multichannel nanoparticle scintillation microdevice with integrated waveguides for Alpha, Beta, Gamma, X-ray and Neutron Detection, Scott M Pellegrin, Chad Whitney and Chester G. Wilson, Journal of Microelectromechanical Systems, Vol.19, No. 5, October 2010, p 1207 – 1214. [2] Luminescence and Scintillation Properties at the Nanoscale; Christophe Dujardin, David Amans, Andrei Belsky, Chaput, Gilles Ledoux and Anne Pilonnet, IEEE Transactions on Nuclear Science, Vol., 57, No. 3, (June 2010), P 1348 – 1354. [3] Synthesis of tungsten oxide nanoparticles by acid precipitation method; Sitthisuntorn Supothina, Ceramics International, Volume 33, Issue 6, August 2007, Pages 931–936

NP Radiation Detector

Si Photodiode/PMT

Front-end Electronics

Processing Electronics

Data Acquisition

Spatial Coherence of Tropical Rain

R. Ratan1, V. Venugopal∗1, J. Sukhatme1 and R. Murtugudde2

1Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore, India.2Earth System Science Interdisciplinary Center, University of Maryland, Maryland,USA.

Abstract - We characterise the spatial coherence of tropical

rain from observations (TRMM) and assess if models (CMIP5)

are able to reproduce the observed features. Based on 15 years

(1998-2012) of TRMM 3B42 (V7) 1-degree, daily rainfall, we

estimate the spatial decorrelation scale (e-folding distance) of

rain at each location in the tropics. A ratio of zonal to merid-

ional spatial scales clearly illustrates that while rain patterns

tend to be anisotropic (ratio of 3-4) over tropical ocean re-

gions, over land regions, rain tends to be mostly isotropic (ra-

tio of 1-1.5). This contrast between ocean and land appears to

be reasonably well captured by CMIP5 models, although the

anisotropy (ratio) over ocean is much higher than in observa-

tions.

1 Introduction

In a recent study (Ratan and Venugopal, 2013), we sta-

tistically analysed and documented the wet and dry spell

characteristics of tropical rainfall, using Tropical Rainfall

Measurement Mission (TRMM) satellite-based (3B42

V6) daily 1-degree rainfall. Using an Intensity-Duration-

Frequency (IDF)-like approach (common in hydrology, but

seldom used in meteorology), they found that while both

ocean and land regions with high seasonal rainfall accumu-

lation (humid regions; e.g., India, Amazon, Pacific Ocean)

show a predominance of 2-4 day wet spells, those regions

with low seasonal rainfall accumulation (arid regions; e.g.,

South Atlantic, South Australia) exhibit a wet spell duration

distribution that is essentially exponential in nature, with

a peak at 1 day. The behavior that is observed for wet

spells is reversed for the dry spell characteristics. In other

words, the main contribution to the non-rainy part of the

season comes from 3-4 day dry spells in the arid regions, as

opposed to 1-day dry spells in the humid regions. The total

rainfall accumulated in each wet spell was also analyzed,

and we find that the major contribution to seasonal rainfall

for arid regions comes from 1-5 day wet spells; however,

for humid regions, this contribution comes from wet spells

of duration as long as 30 days. We also explored the role

of chance as well as the influence of organized convection

in determining some of the observed features. Specifically,

we showed that while the 2-4 day mode might be present

in random realizations of rainfall in a humid region as well

as rainfall observations, the differences in contribution of

∗venucaos.iisc.ernet.in; STC 320

longer duration wet spells to the total number of rainy days

or seasonal accumulation separate reality from chance.

As a follow-up, in this work, we have begun to explore

the role of organised convection in determining some of

these observed features. Specifically, the recent availability

of high-quality satellite observations of rain proxies or

derived observations of rain over the globe provided an

ideal opportunity to study the spatial scales associated

with convection and rainfall. Among previous studies of

rainfall proxies which focus on spatial scales of convection,

Roca and Ramanathan (2000) used infrared imagery (from

INSAT 1-B) to analyse the scale-dependence of convective

systems in the tropical Indian ocean. They found that

cloud systems span a wide range of spatial scales from

100 to 500 km2. Nesbitt et al. (2000), using brightness

temperature measurements from the TRMM satellite, anal-

ysed mesoscale convective systems (MCSs) and identified

differences in the size of the systems over land and ocean

regions. Ricciardulli and Sardeshmukh (2002) have perhaps

provided one of the more comprehensive analyses on local

time and space scales of organized convection over the

global tropics. They analysed cloud imagery data and found

that typical spatial autocorrelation of deep convection is

of the order of 130km and mean duration of convective

events lasted approximately 5 to 6 hours over the global

tropics. More recently, Wood and Field (2011) approached

the cloud size distribution problem from a scaling point

of view, and find that the horizontal size distributions of

clouds are shown to be well captured by a unique power-law

relationship over a range of spatial scales from 0.1 to 1500

km. They also find geographic and seasonal variations, in

that the largest clouds are predominant over west Pacific

and Indian Oceans, as well as land regions influenced by

monsoons, while the smallest clouds are found over arid

regions and the trade wind zones of the tropics.

Moving on to studies which analysed rainfall observations,

Smith et al. (2005) reported that the mean spatial scale of

precipitation for land and ocean is around 90 and 122 km,

respectively. Dai et al. (1997) found that spatial correlation

scale of precipitation is higher over southern tropics then

over the northern tropics (200 km and 550 km for norther

and southern tropics, respectively). Baigorria et al. (2007)

showed that the spatial correlation is higher in winter than

summer in mid latitudes regions, with the type of precipita-

1


tion (frontal in winter versus convective in summer), cited

as the main reason for the observed differences. In this

work, we systematically analyse 15-years of TRMM 3B42

(V7) daily gridded (1-degree) rainfall observations (e.g., see

Kummerow et al., 1998; Adler et al., 2000; Huffman et al.,

2007) to estimate the spatial scale of rainfall and assess if the

coherence has any anisotropy associated with it. The paper

is structured as follows: Section 2 describes the methodol-

ogy used in estimating the spatial scale, followed by a dis-

cussion of the main results in Section 3, and a summary in

Section 4.

2 Methodology

We use the decorrelation or e-folding length as a measure

of the spatial scale of precipitation. Specifically, the cross-

correlation between the daily rainfall time series at a refer-

ence grid and its neighbouring grids is estimated, and the

distance at which the cross correlation becomes less than 1e

is considered to be the decorrelation length. To illustrate the

procedure, Figure 1a shows the daily rainfall (for 2005) at

a reference point over the Pacific Ocean (10N, 130W; red)

and its neighbouring grids in the x-direction, one to the east

(blue) and one to the west (grey) of the reference grid. Fig-

ure 1b shows the cross correlation between the daily rainfall

at the reference grid (10N, 130W) with its neighbours, 20

lags (degrees) to the east and west. The decorrelation or e-

folding length from Figure 1b is ∼5 degrees in the east as

well as west direction. This is repeated for each year, and

a climatological correlation curve is estimated for +x and

−x directions for the reference grid. Noting that the decor-

relation in the positive (east) and negative (west) directions

are mostly similar in magnitude (or decay; e.g., see Figure

1b), a climatological decorrelation length is estimated based

on the average of these two climatological curves (for +xand −x). The spatial scale estimated in this manner yields a

zonal (x) spatial scale at the reference grid. A similar pro-

cedure is followed to estimate a meridional (y) spatial scale

by considering lags in the north and south directions.

3 Results and Discussion

To begin with, we consider four locations - two over ocean

(Pacific and Atlantic) and two over land (India and Ama-

zon). Figure 2 shows the climatological cross-correlation

curves for these four locations It is quite evident from

the decay rates of these curves and the e-folding lengths,

that rain over ocean regions is likely to be more spatially

coherent, at least zonally, than that over land regions.

Specifically, comparing Figures 2 a, b and Figures 2c,

d, we note that zonal scale of rain over oceans is larger

than that over land. This is perhaps not surprising given

the spatial structure of annual mean rainfall over tropical

convergence zones. The meridional spatial scales over land

appear to be more than over ocean. A comparison of the

two scales suggests that perhaps rain over land tends to be

more small-scale (at least zonally) and more isotropic (i.e.,

(a)

J F M A M J J A S O N D0

20

40

60

80

100

Rai

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(10N,129W)(10N,130W)(10N,131W)

(b)

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0

0.2

0.4

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Lag (in degrees)

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Figure 1: (a) TRMM-based daily rainfall in 2005 at a refer-

ence grid (10N, 130W) and at neighbouring locations. (b)

Cross-correlation curve as a function of spatial lag (in de-

grees). The solid black line represents 1/e, and the two filled

red circles show the e-folding lengths in east (+ve lag) and

west (-ve lag) directions.

comparable zonal and meridional spatial scales) compared

to that over ocean.

As mentioned earlier, repeating this procedure for each

1-degree grid for 15 years, we estimate the zonal and merid-

ional climatological decorrelation (e-folding) lengths for

rain over the tropics. Figure 3a, b show the climatological

zonal and meridional decorrelation lengths (also called spa-

tial scale), respectively, for tropical rain. The higher zonal

spatial scale over ocean regions compared to land, which

was partially evident from Figure 2, is now quite clear in

Figure 3a. Specifically, rainfall over most of the ocean

basins, especially over the core of the tropical convergence

zones near the equator, has a spatial scale of 6 degrees. On

the other hand, over many of the monsoon hotspots (India,

northern Australia, South America), the zonal spatial scale

is of the order of 3-4 degrees. Interestingly, the equatorial

0 2 4 6 8 10−0.2

0

0.2

0.4

0.6

0.8

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0 2 4 6 8 10−0.2

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0.2

0.4

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Spatial Lag (in degrees)

Cro

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n

0 2 4 6 8 10−0.2

0

0.2

0.4

0.6

0.8

1(d)

Spatial Lag (in degrees)

ZonalMeridional

Figure 2: Climatological zonal (red) and meridional (blue) cross-correlation curves based on 15 years (1998-2012) of

daily, 1-degree TRMM 3B42 (V7) rainfall for two ocean and two land locations: (a) Pacific (10N, 130W) (b) Atlantic (5N,

30W) (c) Amazon (10S, 60W) and (d) India (20N, 76E). and represent the +ve (x and y) and -ve (x and y) directions,

respectively. The dashed line corresponds to 1/e, based on which decorrelation lengths are estimated.

Africa and South America show a substantially smaller

zonal spatial scale (< 1-degree) suggesting that rain over

those regions is highly localised, even though they receive

rain throughout the year much like the equatorial oceans.

The maritime-continent region also has a very low zonal

spatial scale, despite the fact that it receives high amount of

precipitation throughout the year. One possible reason can

be that this region gets mostly convective type of rain, which

lasts typically for a few hours, suggesting that moisture

transport is not from afar. The high zonal spatial scale over

southern Africa (6 − 8 degrees) could be attributed to the

presence of the intertropical convergence zone (ITCZ) in

southern hemisphere during the boreal winter. Moving

on to the meridional (y) spatial scale of rain (Figure 3b),

over most land regions it (2 − 3 degrees) is lesser than or

comparable to the zonal counterpart. Remarkably, over the

tropical convergence zones (except over equatorial Indian

Ocean) it ( 1-degree) is much lesser than the zonal spatial

scale ( 6 degrees).

The dominance of zonal or meridional scale over each

region can be assessed by their ratio, which can be seen to

be a measure of isotropy (or lack thereof) of rain, shown in

Figure 3c. The most striking aspect seen here is that, over

most land regions, rain tends to be small-scale (lower zonal

and meridional scales) and mostly isotropic (ratio of 1 to

1.5 in Figure 3c). Interestingly, the southern part of Africa,

which showed high zonal scale, also has a ratio between 1

and 2, suggesting that moisture transport is likely to be from

afar in both zonal and meridional directions. On the other

hand, oceanic rain, especially over the Pacific and Atlantic

convergence zones, is highly anisotropic (a ratio of 3 and

higher). This is generally consistent with the climatological

view of the (east-west elongated) spatial structure of rain

over the tropical convergence zones.

Following this, we analysed a set of 4 climate models to see

if they reproduce the feature reported above from observa-

tions. The models selected were from the suite of models

used in the Coupled Model Intercomparison Project (CMIP)

Phase 5. The four CMIP5 models used are CSIRO (Aus-

tralia), MPI (Germany), NCAR-CCSM (USA) and NOAA-

GFDL (USA) with spatial resolutions of 1.87 × 1.86 ,

1.87 × 1.86 , 1.25 × .9 and 2.5 × 2 . At the outset,

it is worth mentioning that the spatial resolution of 3 out

of the 4 models is around 2-degrees and thus a compari-

son between the zonal and meridional scales with observed

(at 1-degree) would not be fair or appropriate. However,

a meaningful assessment, at least qualitatively, can possi-

bly be gleaned from the ratio, i.e., the measure of isotropy.

For completeness sake, Figure 4 shows the estimates of the

zonal (first column) and meridional (second column) spa-

tial scales from model precipitation, using the procedure de-

scribed in Section 2. The over-estimation of zonal spatial

scale is more pronounced than the meridional counterpart,

when compared to observations. Their ratio (shown in the

third column), on the other hand, suggests that the isotropy

of rain over land and anisotropy over ocean (especially, the

Figure 3: Climatology (1998-2010) of the (a) zonal and (b) meridional spatial scales, based on daily, 1-degree rainfall.

Their ratio is shown in panel (c). The white area in the panels corresponds to regions which receive an annual mean rain

less than 2 mm/d.

Pacific and Atlantic convergence zones) are captured partic-

ularly well in 3 of the four models, even though the magni-

tude is 2 to 3 times higher than in the models. Apart from the

fact that models have a coarser resolution, the well-known

issue of the models not knowing when to stop raining (i.e.,

persistent drizzle) could also be a contributing factor to the

discrepancies shown here.

4 Summary

In this study we document the spatial scale characteristics

of tropical rainfall using 15 years (1998-2010) of daily, 1-

degree TRMM-based rainfall. Using decorrelation or e-

folding lengths in zonal and meridional directions as a mea-

sure of the spatial coherence of rain, we find that (i) the zonal

spatial scale of ocean rain is much higher than land rain; (ii)

the meridional counterparts are comparable between ocean

and land; surprisingly, the meridional spatial scale of rain in

the Pacific and Atlantic convergence zones, is much smaller.

The dominance of zonal or meridional scales is captured by

their ratio; it clearly shows that while most land rain tends to

be small-scale and isotropic (a ratio between 1 and 1.5), rain

over ocean tends to be mostly anisotropic, with the ratio of

4 in the Pacific and Atlantic oceans. Preliminary investiga-

tion into the ability of climate models (albeit at coarser res-

olution) to reproduce some of these observed features sug-

gests that even though the two scales are significantly over-

estimated, they are able to capture the qualitative feature of

anisotropy over ocean and isotropy over land.

References

Adler, R. F., G. J. Huffman, D. T. Bolvin, S. Curtis, and

E. J. Nelkin (2000), Tropical rainfall distributions deter-

mined using TRMM combined with other satellite and

rain gauge information, J. Appl. Meteorol., 39, 2007–

2023.

Baigorria, G. A., J. W. Jones, and J. J. O’Brien (2007), Un-

derstanding rainfall spatial variability in southeast USA

at different timescales, International Journal of Climatol-

ogy, 27(6), 749–760, doi:10.1002/joc.1435.

Dai, A., I. Y. Fung, and A. D. Del Genio (1997), Sur-

face Observed Global Land Precipitation Variations dur-

ing 190088, Journal of Climate, 10, 2943–2962, doi:

10.1175/1520-0442(1997)010¡2943:SOGLPV¿2.0.CO;2.

Huffman, G. J., R. F. Adler, D. T. Bolvin, G. Gu, E. J.

Nelkin, K. P. Bowman, Y. Hong, E. F. Stocker, and D. B.

Wolff (2007), The TRMM multi-satellite precipitation

analysis: Quasi-global, multi-year, combined-sensor pre-

cipitation estimates at fine scale, Journal of Hydrometeo-

rology, 8, 38–55.

Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J. Simp-

son (1998), The tropical rainfall measuring mission

(TRMM) sensor package, J. Atmos. Oceanic Technol.,

15(3), 809–817.

Nesbitt, S. W., E. J. Zipser, and D. J. Cecil (2000), A cen-

sus of precipitation features in the tropics using TRMM:

Radar, ice scattering, and lightning observations, Journal

of Climate, 13, 4087–4106.

Figure 4: Same as Figure 3, but for rainfall from 4 CMIP-5 models (a) CSIRO (b) MPI (c) NCAR-CCM (d) NOAA -

GFDL. The first two columns show the zonal and meridional spatial scales, respectively, and their ratio is shown in the

third column.

Ratan, R., and V. Venugopal (2013), Wet and dry spell char-

acteristics of global tropical rainfall, Water Resources Re-

search, 49(6), 3830–3841, doi:10.1002/wrcr.20275.

Ricciardulli, L., and P. D. Sardeshmukh (2002), Local time-

and space scales of organized deep convection, J. Clim.,

15, 2775–2790.

Roca, R., and V. Ramanathan (2000), Scale dependence of

monsoonal convective systems over the Indian Ocean, J.

Clim., 13, 1286–1298.

Smith, D., A. Gasiewski, D. Jackson, and G. Wick (2005),

Spatial scales of tropical precipitation inferred from

TRMM microwave imager data, Geoscience and Remote

Sensing, IEEE Transactions, (7), 1542–1551.

Wood, R., and P. R. Field (2011), The distribution of cloud

horizontal sizes, J. Clim., 24, 4800–4816.

Amorphous Silicon Carbide thinfilms by Pulsed DC Magnetron Sputtering

for Micro Electro-Mecanical Systems (MEMS) applications

Habibuddin Shaik, Sumesh M.A*, Mohan Rao G

Department of Instrumentation and Applied Physics Indian Institute of Science, Bangalore

*LEOS, ISRO, Bangalore

Abstract- We are presenting here the synthesis and feasibility studies of a-SiC thin films for fabrication of a micro machined structure which will be used for development of a Micro-bolometer structure. In relation to this the micro machined structures are demonstrated in different designs on crystalline silicon substrate. Amorphous silicon carbide (a-Si1−xCx) films were deposited on silicon (1 0 0) and quartz substrates by pulsed DC reactive magnetron sputtering of Silicon in methane (CH4)–Argon (Ar) atmosphere. Optical, compositional and mechanical properties were investigated. We were able to synthesize a-Si1−xCx with x=0.5, and 100% Si-C bonding. We used X-ray photoelectron spectroscopy (XPS) to confirm this. These films, with 100% Si-C bonding, are used for the fabrication of micro structures. We studied the influence of substrate temperature and target power on the composition, carbon bonding configuration, band gap and hardness of a-SiC thin films. Increase in substrate temperature favors silicon–carbon (Si-C) bonding with increased hardness. The deposited films are chemically inert.

I. INTRODUCTION

The demand for sensors that can operate at high temperatures and often in severe environments such as aerospace, automotive, combustion processes or gas turbine control and oil industry is always increasing. Amorphous silicon carbide (a-SiC) is an attractive material from a technological point of view due to its excellent bandgap tunability, ability to withstand at high temperatures, good thermal conductivity, good mechanical strength and its inertness to wear and corrosive environments. Each or the combination of above properties have found

applications in different areas like MEMS, photovoltaics and protective coatings etc [1-5].

In the present investigation, the material aspect of a-SiC will be studied giving emphasis on its applications as structural material for the fabrication of MEMS systems, in particular for micro bolometer. We present here the study of the effect of the process parameters on stoichiometry and Si-C bonding configurations of the films and which in turn influence their optical and mechanical properties of the films.

II. EXPERIMENTAL DETAILS

Vacuum chamber with a base pressure of 2×10−6 mbar (with the combination of diffusion pump, cryo-sorption pump and rotary pump) was used to deposit a-SiC films. Single crystal silicon target of 10 cm diameter was sputtered in methane (CH4)–argon (Ar) ambient with a fixed CH4 partial pressure of 1.0 × 10−4 mbar and with working pressure of 2.0×10−3 mbar. Target to substrate distance was maintained as 7 cm. Substrate heater with k-type thermocouple was used. Pulsed (100 kHz) DC voltage (HUTTINGER Electronics, GmBH) was applied to the target.

The deposited films were characterized with DEKTAK profilometer for thickness measurement. Elemental composition and binding energy was analyzed using X-ray photoelectron spectroscopy (SPECS GmbH spectrometer (Phobios 100MCD Energy analyzer)). FTIR spectroscopy (Bruker Tensor 27) was used for identifying the fundamental vibrational modes and UV–VIS spectroscopy (HITACHI 3000) for bandgap calculations (using Tauc’s plot). Mechanical properties of the films were investigated using a nanoindenter (Triboindenter of Hysitron, Minneapolis, USA).

Two sets of films were deposited. The first set contains the films deposited at three different


substrate temperatures (473, 673, and 873 K) with a constant target power of 1200 mW cm−2 and the second set contains the films deposited with four different target powers (360 (110 mA), 540 (150 mA), 740 (200 mA), and 1200 (300 mA) mW cm−2) at a constant substrate temperature of 473 K.

Hard a-SiC films were used for the fabrication of microstructures. Microstructures in different designs, defined by mask and are transferred to the a-SiC coated substrate by using optical lithography. Microstructures were realized by micromachining of the deposited a-SiC films on Si by reactive ion etching (RIE from OXFORD Instruments).


In order to check the composition and of Si-C bonding configuration in the films X-ray photo electron spectroscopy (XPS) was carried out. Al Kα (1486.6 eV) was used as X-ray source. It was observed that, as the substrate temperature is increased from 473 to 873 K, the C concentration in a-Si1−xCx films decreased from 0.3 to 0.2.This is because at higher substrate temperature, the adatoms of C and H reaching the substrate having less kinetic energy will get desorbed from the substrate and hence the percentage of C and H decreases. This reduction in C and H concentration has also been confirmed by the decrease in the bandgap (Fig. 1a). Similarly the C content in the films reduces from 0.54 to 0.3 with increase in the target power density from 360 to 1200 mW cm−2. This is due to the obvious reason that, at higher target powers more Si atoms sputter-out from the target and hence the intake of Si atoms into the film is more and dominates the intake of C. This decrease/increase in C/Si concentration can also be confirmed by (i) the decrease in the bandgap (Fig. 1b). Fig. 2 shows the deconvoluted fine spectra of C 1s core level into C-Si, C-C/C-H peaks for the first set of samples and it indicates that, though the C content is decreasing with increase in the substrate temperature (for a fixed target power), the percentage of C-Si bonding is increasing and C-C/C-H bonding is decreasing. The observed decrease in the bandgap, though the Si-C bonding is increasing with the substrate temperature is due to the fact that, the decrease in the C concentration is dominant than the

increase in Si-C bonds. This may be due to the fact that at high substrate temperatures the weak C-H and Si–H bonds break and the dangling bonds of Si and C combine to form strong Si-C bonds. Basa and Smit [6] had also suggested that the breaking of the Si-H and C-H bonds and the increased tendency towards Si-C bonding.

Fig 1. Variation of bandgap with (a) substrate temperature (b) target power.

Similarly Fig. 3 shows the deconvoluted fine spectra of C 1s core level into C-Si, C-C/C-H peaks for the second set of samples and it indicates that, as the target power is decreased (for a fixed substrate temperature) the percentage of C-Si bonding is increasing and C-C/C-H bonding is decreasing. This is due to the fact that, as the target power is increased (for fixed CH4 partial pressure) more Si atoms sputter-out from the target so along with C-Si bonding there is a more tendency towards Si-Si, O-Si-C, SiOx and Si-Hn bonding. Therefore the Si-C bond density is less at high target powers.

Fig. 2 Deconvoluted fine spectra of C 1s peak for the a-SiC thin film deposited with the target power of 1200 mW cm−2 at substrate temperature of (a) 473 K (b) 673 K and (c) 873 K.

Fig. 3 Deconvoluted fine spectra of C 1s peak for the a-SiC thin film deposited at a substrate temperature of 473 K with a target power of (a) 1200 mW cm−2 (b) 740 mW cm−2 and (c) 540 mW cm−2 (d) 360 mW cm−2.

Mechanical properties such as hardness and modules were calculated using the representative load–displacement curves (P-h curves) of both the sets of films. The maximum penetration depth of the indenter (hmax) at the peak load of 600 µN is less than 10% of the films thickness, which ensures that the substrate influence is negligible on the observed mechanical response. The mechanical properties such as hardness (H) and modulus (E) were calculated by fitting the unloading curve of the P-h curve using Oliver-Pharr method [7]. Fig. 4 and 5 show the variation of hardness and modulus with the substrate temperature and target power. Fig. 4 shows increasing hardness with increase in the substrate temperature for the first set of films. This can be confirmed by XPS analysis (Fig. 2) which indicates that, the Si-C bond density is increasing with substrate temperature leading to an increase in hardness and modulus. Increase in Si-C bond density will lead to enhancement of cross-linkage of Si and C atoms, resulting in a strengthened a-SiC material frame and ultimately improved amorphous network stiffness [8,9]. The same trend was observed for second set of films which were deposited at constant temperature and different target power densities as shown in Fig. 5.

Fig. 4. Variation of hardness and modulus with substrate temperature at constant target power of 1200 mW cm−2.

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00 sccm position: 750

15C 900W 30W

-Si etch ed and fixedshown in fig 7roken beamsrn (Fig 7) are

which is geg. For the g, we observedess than 5µm

in the fixede to the resifilms during

mized

d-free 7 and s or e due etting given d that m are

d-free idual

the

IV.

a-SiC micdesigns ansubstrate. Thaving 100which is vbolometer atemperaturecorrosion rechemically process partarget powstructure ofmechanicalthat, the cawith increathe percentthe C-C/C-decrease incarbon conbonding isdecreasing.(i) target ptemperaturedemonstratemicromachmicro-bolom

[1] I. AfdiYunazKonagai, Sol. En[2] D. Pysch, MFilms 519 (2011[3] C.Y. Chang, Soc. 132 (1985) [4] L.S. Chang, 1882. [5] N. LedermanTellenbach, Surf[6] D.K. Basa, (1990) 439. [7] W.C. Oliver,[8] M.A. El KhaM.E. O’Hern, M96. [9] A. Jean, M.AKieffer, H. Pepi(1993) 2200.

CONCLUSION

cromachined nd dimensionThe synthesiz0% Si-C bon

very crucial foapplication ase and also esistant. The

inert. We srameters like wer on stof the films whl and optical parbon concen

asing the substage of C-Si b-H bonding isn target powncentration, ans increasing So we selecte

power=360 me= 473K for ed the fined structurmeter.

REFER

z, H. Nagashimanergy Mater. Sol.M. Bivour, M. H) 2550. Y.K. Fang, C.F. 418. P.L. Gendler, J

nn, J. Baborowskif. Coat. Technol.F.W. Smith, M

G.M. Pharr, J. Makani, M. Chake

M.F. Ravet, F. Ro

A. El Khakani, Mn, M.F. Ravet, F

NS AND FUTUR

structures wns were reazed a-SiC thnding with gor high temps it should be

it should ba-SiC films astudied the substrate tem

ichiometry ahich in turn inproperties. XPntration is getrate temperatbonding is incs decreasing. er causing a nd the percenand C-C/C-Hed the optimiz

mW.cm−2 and 100% Si-C

feasibility ores towards

RENCES

a, D. Hamashita, Cells. 95 (2011)

Hermle, W.G. St

Huang, B.S. Wu

.H. Jou, J. Mate

i, P. Muralt, N. X125 (2000) 246.

Mater. Res. Soc.

Mater. Res. 7 (199er, A. Jean, S. Boousseaux, J. Mat

M. Chaker, S. BoF. Rousseaux, App

RE WORK

with differentalized on Sihinfilms weregood hardnessperature micro

stable at highbe wear andre found to beeffect of the

mperature andand bondingnfluence their

PS data showsetting reducedture, howevercreasing while

Similarly therise in total

ntage of C-SiH bonding iszed conditions

(ii) substratebonding, and

of differentfabricating a

S. Miyajima, M 107. tefan, Thin Solid

u, J. Electrochem

er. Sci. 26 (1991)

Xantopoulos, J.M

Symp. Proc. 162

92) 1564. oily, J.C. Kiefferter. Res. 9 (1994)

oily, E. Gat, J.Cpl. Phys. Lett. 62

t i e s o h d e e d g r s d , e e l i s s e d t a

M.

d

.

)

M.

2

r, )

C. 2

Low temperature electrical transport studies on carbon nitride films prepared by chemical vapour deposition

K. Ramesh1, M. Prashantha1, R.Venkatesh1, N. Naresh1, M.V.N. Prasad2

1Department of Physics, Indian Institute of Science,

Bangalore 560 012. 2LPSC, ISRO, Bangalore 560 008.

E-mail: [email protected]

Abstract - The carbon nitride films have been prepared by chemical vapour deposition (CVD) at pyrolysis temperatures of 725, 750, 775, 800 and 825 oC. Electrical transport studies at low temperature (RT to 4.5K) show that the carbon nitride films exhibit Metal-Insulator (MI) transition. It is observed that the increase in pyrolysis temperature shifts the MI transition temperature to lower values. The transition temperatures for the samples prepared at 725 oC, 750 oC and 775 oC are 84.7 K, 67.7 K and 9.5 K respectively. The reduced activation energy indicates that the metallic regime of the samples prepared at pyrolysis temperatures > 800 oC lies at low temperatures. It is also observed that the activation energy decreases with the increase in pyrolysis temperature.

1. INTRODUCTION

The search for new materials for advanced application leads to the discovery of new materials with interesting electrical, physical, chemical and mechanical properties. The advancement of society and quality of human life also depends on these advanced materials. Carbon nitrides possess outstanding properties like high hardness [1-3], wide

1 Project Code: ISTC/PPH/KR/266.

band gap, excellent thermal conductivity, wear resistance, oxidation resistance, chemical inertness, high velocity of sound (~104 m/s), low dielectric constant (k~2), etc. These properties make them as a promising material for various applications such as organic semiconductors, fuel cells and photocatalysis, mechanical cutting tools, protective coatings, biomedical applications, electroluminescence devices, optical materials etc [4-6]. Medical devices such as bone joints, dental roots, heart valve etc which will be implanted into human body can be coated with carbon nitride films. The coating can reduce the wear and corrosion, which occurs due to the reaction of the device with body fluid. So coating the devices using carbon nitride can result in increase of life time. Apart from that, the coating is biocompatible [7]. In this article, the electrical transport properties of carbon nitride films have been reported. The films show the effect of disorderness and effect of incorporation of nitrogen on electrical transport properties. From earlier studies by Mott [8], some of the disordered materials show a transition from metallic to insulating behaviour at a particular condition of temperature, pressure, doping level etc. In view of this, the transition from metallic to insulator behaviour has also been observed in the prepared carbon nitride films.

2. EXPERIMENTAL

The samples were prepared by pyrolysis assisted chemical vapor deposition (CVD) method with the help of a home-built two zone furnace. 4-Azabenzimidazole (C6H5N3) of purity 99% was used as a precursor. About 0.1g of precursor was taken in a quartz tube of 85 cm length and 10mm diameter. The precursor was heated in the Zone I of a two zone furnace at 400 oC. The dense vapours of the precursor enter the zone II which was kept at a desired temperature where the pyrolysis occurs and the vapours get deposited on the quartz substrates and inner walls of the quartz tube. The samples were prepared at different pyrolysis temperatures of 725, 750, 775, 800 and 825 oC. As the C-N bond is significantly strong, even at high temperatures a considerable amount of the C-N bonding is retained. This method is simple and enables one to have control over the amount of nitrogen in the system by controlling the pyrolysis temperature, volume of the liquid and the process time.


mailto:[email protected]

Sample at% of C at% of N at% of H

CN 725 70.70 25.98 3.31 CN 750 70.89 26.03 3.08 CN 775 70.54 26.30 3.16

CN 800 75.36 22.52 2.12 CN 825 77.81 20.85 1.34

Table 2. CHN data for samples prepared at different pyrolysis temperatures.

3. RESULTS AND DISCUSSION

The Raman spectrum of the prepared samples, shown in FIG.1, exhibits the D and G bands characteristic of amorphous carbon. The D (disorder) and G (graphite)

bands are observed in disordered polycrystalline and amorphous graphitic carbons. The G band is observed in the region 1530-1600 cm-1 and D band approximately at 1360 cm-1 respectively [9-11]. For single crystalline graphite only G band is observed. The G band arises due to the bond stretching of all

pairs of sp2 atoms in the ring structure. When nitrogen

is incorporated into this ring structure, the carbon-nitrogen bond breaks the symmetry in sp2 domains. As a result, Raman spectra exhibits a D band. Similarly,

graphitic materials with nanometric-size crystallites, impurities, doping, C≡N, imperfections, edges, exhibit a Raman D band. The relative intensity ratio of disorder band to graphite band (ID/IG) is a measure of the degree of order in the carbon nitride sample. Higher the value of ID/IG, higher the disorder in the sample. In the present work, Raman studies on the CNx film coated on the surface of quartz substrates show the D band at 1365 cm-1 and G band around 1575 cm-1. From FIG 2 it is seen that the ratio ID/IG increases with increase in preparation (pyrolysis) temperature and the result indicates that structural disorder increases in the samples with increase in pyrolysis temperature. CHN elemental analysis (Table 2) shows that the at% of nitrogen is about 26% in the samples from CN725 to CN775 and gradually decreases for the samples prepared at 800 and 825 oC.

3.1. Electrical transport study Electrical transport study was done from room temperature to 4.5 K. It is observed that the electrical resistivities of prepared carbon nitride film decreases with increase in preparation (pyrolysis) temperature. The samples CN725, CN750 and CN775 clearly show the metal-insulator transition at temperatures of 84.7K, 67.7K and 9.5K respectively. The results indicate that the disorderness and the preparation temperature of the films have a major role deciding the transition temperature. The plot of normalized resistivity versus temperature in FIG.3 shows a Metal-Insulator transition. In the figure, it can be observed that the transition temperature shifts to a lower value with increase in preparation (pyrolysis) temperature. Prasad et.al [12] reported that the electrical transport properties of amorphous carbon films prepared at 900 oC and above show Metal-Insulator transition. But our

Sample ID/IG

CN725 3.21 CN750 3.71 CN775 3.93

CN800 4.38 CN825 4.48

Table 1. Disorderness in the samples and crystallite size.

FIG.1. Raman spectra of the prepared Carbon nitride samples

FIG.2. Variation of ID/IG with pyrolysis temperature

FIG.3. Normalized resistivity vs Temperature in log scale for samples prepared at different pyrolysis temperatures showing the Metal-Insulator transition.

FIG.4. Reduced activation energy vs temperature. The positive slope shows that the samples are in metallic regime at low temperature

Sample Activation Energy (meV)

Resistivity at RT (Ω.cm)

CN725 94.36 4.89×10-1 CN750 60.22 1.22×10-1 CN775 24.22 3.65×10-3 CN800 18.61 3.13×10-3 CN825 8.58 1.19×10-3

Table. 3. Variation of activation energy and resistivity of samples with pyrolysis temperature.

study shows the Metal-Insulator transition for the CNx films prepared well below 900 oC. Moreover, the metallic regimes of CN800 and CN825 at low temperature are confirmed by the positive slope of the plot of reduced activation energy versus temperature. The reduced activation energy is calculated using the equation,

w(T) = -(∂lnρ/∂lnT) (1) From the plot of reduced activation energy (w) versus

temperature as shown in FIG 4 we can conclude that both CN800 and CN825 show metallic regime at low temperatures, the positive slope is the indication of metallic regime. Apart from the effect of preparation temperature, the impurity present in the amorphous network of carbon

also has an important role deciding the transition temperature. Some of the amorphous systems also show the metal-insulator transition because of the possible reason that the dopants can make the system more ordered at low temperatures [13,14] and so reducing the resistivity with decrease in temperature. Similar kind of metal to insulator transition at low temperature was observed in some disordered systems also [15,16]. A reason for the metal-insulator

transition at low temperature may be due to the formation of impurity band conduction, which occurs because of overlapping of electron wavefunctions and as a result a finite conductivity can be observed at 0K [13]. Decrease in resistance may also be due to weak phonon scattering of charge carriers. So, the samples show a decrease in resistivity with decreasing temperature below the transition temperature. Apart from that, the electrical conductivity depends on the density of states close to Fermi energy and the radius of localization of the charge carriers bound to the impurity states. From the results, it is observed that even though the amount of nitrogen is almost same for first three

FIG.5. Calculation of activation energy for CN825.

samples, the metal-insulator transition temperature gradually shifted to lower values with increase in preparation temperature. At higher temperatures, resistivity follows Arrhenius equation.

KT

Eaexp0 (2)

where, ρ0 is the pre-exponential factor, Ea is activation energy and K is the Boltzmann constant. From the values of activation energies (Table 3) it is observed that the activation energy decreases with increase in preparation temperature. Consequently, the resistivity of the carbon nitride samples at room temperature decreases with increase in pyrolysis temperature. The room temperature resistivity decreases gradually from 4.89×10-1 Ω.cm for the films prepared at 725 oC to 1.19×10-3 Ω.cm for the films prepared at 825 oC. FIG.5 shows the plot of resistivity versus temperature for the sample CN825 for the calculation of activation energy at high temperature (200-300K).

4. CONCLUSIONS

In conclusion, CNx films have been prepared using chemical vapour deposition at different pyrolysis temperatures using an organic precursor Azabenzimidazole. Disorderness of films increases with increase in pyrolysis temperature. Electrical transport studies show that these films exhibit metal to insulator transition at low temperatures.

REFERENCES [1]. A.Y. Liu and M.H. Cohen, Prediction of New

Compressibility Solids, Science, 245, 841-842, 1989.

[2]. D.M. Teter, and R.J. Hemley, Low-Compressibility Carbon Nitrides, Science, 271, 53-55, 1996.

[3]. A.Y. Liu and R.M. Wentzcovitch, Stability of carbon nitride solids, Phy Rev B 50 (14), 10362-10365, 1994.

[4]. X. Wang, K. Maeda, X. Chen, K. Takanabe, K. Domen, Y. Hou, X. Fu and M. Antonietti, Polymer Semiconductors for Artificial Photosynthesis: Hydrogen Evolution by Mesoporous Graphitic Carbon Nitride with Visible Light, JACS, 131, 1680–1681, 2009.

[5]. M. Zang, Y. Nakayama and M. Kume, Room temperature electroluminescence from hydrogenated amorphous carbon nitride film, Solid State Commun. 110, 679-683, 1999.

[6]. Y. Zheng, J. Liu, J. Liang, M. Jaroniec, S.Z.Qiao, Graphitic carbon nitride materials: controllable synthesis and applications in fuel cells and photocatalysis, Energy Environ. Sci, 5, 6717-6731, 2012.

[7]. F.Z. Cui and D.J. Li, A review of investigations on biocompatibility of diamond-like carbon and carbon nitride films, Surf and Coat Tech 131, 481-487, 2000.

[8]. N.F. Mott, Metal-Insulator Transition, Rev of Mod Phys, 40, 677-683, 1968.

[9]. R.O. Dillon, J.A. Woollam, and V. Katkanant, Use of Raman scattering to investigate disorder and crystallite formation in as-deposited and annealed carbon films, Phy Rev B, 29, 3482-3489, 1984.

[10]. A.K.M.S. Chowdhury, D.C. Cameron and M.S.J. Hashmi, Vibrational properties of carbon nitride films by Raman spectroscopy, Thin Solid Films. 332, 62-68, 1998.

[11]. D. Das, K.H. Chen, S. Chattopadhyay, L.C. Chen, Spectroscopic studies of nitrogenated amorphous carbon films prepared by ion beam sputtering, J. Appl. Phys, 91, 4944-4955, 2002.

[12]. V. Prasad and S.V. Subramanyam, Magnetotransport in the amorphous carbon films prepared from succinic anhydride, Physica B: Cond Mat, 369, 168-176, 2005.

[13]. B.I. Shklovskii, A.L. Efros, Electronic properties of doped semiconductors, Springer, 1984.

[14]. N.F. Mott, Electrons in disordered structures, Advances in Physics, 16:61, 49-144, 1967.

[15]. C. Uher and L.M. Sander, Unusual temperature dependence of the resistivity of exfoliated graphites, Phy Rev B, 27, 1326-1332, 1983.

[16]. I.L. Spain, K.J. Volin, H.A. Goldberg and I.L. Kalnin, Unusual electrical resistivity behaviour of carbon fibers, Solid State Commun, 45, 817-819, 1983.

http://pubs.acs.org/doi/abs/10.1021/ja809307s

1

Abstract— Generation of unmodulated microwave signals by

heterodyning optical signals is well established in literature. In

this paper it is shown that by modulating one of the optical

signals before heterodyning, a modulated microwave signal can

be synthesized. However, when the optical sources are free-

running, the resulting microwave signal lacks frequency stability

and exhibits poor phase noise. A novel approach for

implementing OPLL is proposed wherein angle modulated

microwave signals exhibiting high frequency stability can be

generated. Generation of BPSK and QPSK modulated microwave

signals by optical heterodyning is discussed in this paper. The

same principle can be extended to higher order modulation

schemes as well. Since optical modulators support very high bit-

rates as compared to microwave modulators, the proposed scheme

supports generation of high bit rate modulated microwave

signals.

Index Terms—Integrated optics, Microwave generation,

Microwave photonics, Optical modulation, BPSK, QPSK

I. INTRODUCTION

ENERATION of microwave signals using optical

techniques has been an active area of research. Several

heterodyne methods have been studied and proposed for the

optical generation of microwave signals. The optical signals

for heterodyning can be derived from two independent lasers

[1-2] or from one laser using the modulation-sideband

technique [3] or from a dual mode laser [4]. Optical

heterodyning using harmonic up-conversion in nonlinear lasers

[5], mode-locked lasers and pulsed lasers [6] have also been

reported. Techniques for improving the phase noise

characteristics have also been proposed [7]. These techniques

offer great flexibility in choosing the microwave frequency as

it is determined by the frequency spacing of the two lasers.

Generation of signals with frequencies ranging from few

megahertz up to terahertz is possible by optical heterodyning.

Most of the techniques based on optical heterodyning are

aimed at generating an unmodulated signal in microwave or

mm-wave region. Generation of angle modulated microwave

signals are discussed in this paper.

Section II of this paper discusses generation of unmodulated

microwaves signals by optical heterodyning. Optical Phase-

Locked-Loop (OPLL) for achieving frequency stability of

heterodyned output is discussed in Section III. Optical

synthesis of BPSK/QPSK modulated microwave signals is

discussed in Section IV. Limitations imposed by OPLL in

generation of angle modulated microwave signals are brought

out in Section V. A novel scheme for generation of BPSK and

QPSK modulated microwave signals that overcomes these

limitations is proposed in Section VI. Conclusions are

presented in Section VII.

II. GENERATION OF UNMODULATED MICROWAVE SIGNALS BY

OPTICAL HETERODYNING

A. Heterodyning Principle

Consider two monochromatic optical sources at frequencies

f1 and f2, where | f1 - f2| << f1, f2. Their electric fields can be

represented by:

where f1 and f2 are the optical frequencies of the two sources

and 1 and 2 are the phase variations of the two sources. If

the two sources are combined and given to a photodetector

(PD), which is a square-law device, the resultant signal can be

expressed as:

where R is the responsivity of the PD.

We then get:

From the above equation it can be seen that heterodyned

output contains a term that corresponds to the frequency

difference between the two sources. By choosing the optical

frequencies such that their difference lies in the microwave

region, a microwave signal can be generated by optical

heterodyning.

Fig. 1. Block schematic of an optical heterodyning system

The block diagram for generation of microwave signals

based on optical heterodyning is shown in Fig. 1. The optical

outputs of the laser modules LD1 and LD2 are combined using

an optical power combiner and fed to a photodetector (PD) to

obtain microwave signal at frequency determined by the

frequency difference between the lasers.

Generation of BPSK/QPSK Modulated

Microwave Signals Using Optical Techniques

K R Yogesh Prasad, T Srinivas, Gopal Hegde, Abdul Hameed

G


2

III. REALIZATION OF AN OPTICAL PHASE-LOCKED-LOOP FOR

ACHIEVING FREQUENCY STABILITY

When free-running lasers are used for optical heterodyning,

the resulting microwave signal, though centered at the

difference frequency, lacks frequency stability and hence

cannot be used directly for practical purposes. Figs. 2 and 3

show the heterodyned output of free-running lasers, captured

at two different instances. The figures clearly depict the

frequency variations of the generated microwave signal.

Frequency stability can be achieved by phase-locking one of

the lasers to a highly stable reference signal.

Fig. 2 Snapshot # 1 of spectrum of unlocked signal

Fig. 3 Snapshot # 2 of spectrum of unlocked signal

An Optical PLL, block schematic of which is shown in Fig.

4, can be implemented to achieve frequency stability.

Frequency-scaled heterodyned output and a reference signal

derived from a signal generator are compared using a phase

detector. The output of phase detector is passed through a loop

filter, which determines the dynamic performance of the

OPLL. The feedback voltage, which is proportional to the

phase difference between the heterodyned output and the

reference signal, is applied to the tune the laser output. Once

the optical PLL is locked, the frequency of the heterodyned

output is not allowed to deviate from that of the reference

signal. Also, the phase noise of the heterodyned output is

determined by that of the reference signal within the loop

bandwidth of the OPLL.

Fig. 4 Block diagram of OPLL

Phase noise values as low as -105 dBc/Hz at an offset of 10

KHz from the carrier frequency have been achieved.

Fig. 5 Spectrum of locked signal at 5 GHz exhibiting phase noise of

-105 dBc/Hz at an offset of 10 KHz

IV. OPTICAL SYNTHESIS OF ANGLE MODULATED MICROWAVE

SIGNAL

By extending the approach of generating unmodulated

microwave signal based on optical heterodyning, it is shown

that if one of the lasers is angle modulated then the output of

the photodetector contains a term corresponding to angle

modulated difference frequency. When an optical signal is

subjected to BPSK modulation, its phase is shifted by 0 or

radians depending on the modulating binary data being ’0’ or

‘1’ respectively. Such a signal can be expressed as:

where ‘θ’ is 0 or depending on binary data being 0 or 1

respectively.

If vbpsk is combined with an unmodulated laser output, v2, we

get:

where ‘θ’ is 0 or depending on binary data being 0 or 1

respectively.

3

If vc is passed through a square law device such as a

photodetector, we get current proportional to the square of

input voltage given by the expression:

where R is the responsivity of the photodetector.

The difference frequency term in the above expressions

suggests that the phase difference of ‘θ’ radians introduced by

the modulation is preserved in the process of down-

conversion. Thus we can obtain a BPSK modulated microwave

signal by choosing the laser wavelengths appropriately.

Similarly it can be shown that QPSK modulation introduced in

optical domain is preserved in the microwave domain.

V. LIMITATIONS IMPOSED BY OPTICAL PHASE-LOCKED-LOOP

IN GENERATION OF ANGLE MODULATED SIGNALS

In principle, angle modulation introduced in optical domain

will be preserved in the microwave domain by the

heterodyning process. However, when angle modulated

microwave signals are to be generated by optical heterodyning,

the presence of OPLL can prove to be a hindrance. As the

reference signal is unmodulated, angle modulation introduced

by the modulating signal will be tracked out by the OPLL if

the modulating frequency lies within the loop bandwidth.

Generation of BPSK/QPSK modulated microwave signals by

down-converting a modulated optical signal becomes

complicated with OPLL as BPSK/QPSK are suppressed

carrier modulation schemes with main lobe centered on the

carrier frequency. The heterodyned output within the OPLL

loop bandwidth is determined by the reference signal and will

not retain the modulation information as shown in Fig. 6.

Fig. 6 Effect of OPLL on QPSK modulated signal.

Full scale spectrum showing modulation being tracked out by OPLL.

VI. SCHEME FOR GENERATION OF BPSK/QPSK MODULATED

MICROWAVE SIGNAL

In this section, a simple scheme for generation of BPSK and

QPSK modulated microwave signals by heterodyning is

proposed wherein the problems due to phase-locking discussed

in the previous section are overcome. Figs. 7 and 8 show the

block schematics for generation of BPSK and QPSK

modulated signals respectively while still retaining the OPLL.

Fig. 7 OPLL scheme for generation of BPSK modulated microwave signal

Fig. 8 OPLL scheme for generation of QPSK modulated microwave signal

In the case of BPSK, the heterodyned signal containing

modulation information is self-heterodyned to extract the

carrier at the difference frequency of the lasers. The recovered

carrier is phase-locked to the reference signal by OPLL. Thus,

the OPLL tracks the carrier alone and the phase modulation

introduced in the optical domain is preserved in the microwave

domain.

In the case of QPSK, two levels of self-heterodyning are

required to extract the unmodulated carrier. The carrier thus

recovered is locked to the reference frequency as in the case of

BPSK. Optical heterodyne output, centered on difference

frequency of lasers, retains QPSK modulation.

Fig. 9 shows the QPSK modulated microwave signal in

which modulation remains unaffected by OPLL action by the

proposed approach.

Fig. 9 QPSK spectrum achieved by proposed approach.

Spectrum zoomed in as compared to Fig. 6 to clearly show the modulation.

4

VII. CONCLUSION

Generation of BPSK, QPSK modulated microwave signals

by optical heterodyning has been discussed. Since

BPSK/QPSK modulator design is based on electro-optic phase

modulators capable of being operated at frequencies of several

tens of GHz, it is possible to generate extremely high bit-rate

BPSK/QPSK modulated microwave signals by this approach.

REFERENCES

[1] R.P. Braun, G. Grosskopf, D. Rohde, and F. Schmidt, “Fiber optic mm-

wave generation and bandwidth efficient data transmission for 18-20

and 60 GHz-band communications”, in Proc. Int. Top. Meet.

Microwave Photonics, MWP’97, Germany, Sep. 1997, pp. 235-238,

paper FR2-5.

[2] J. B. Georges, J. Park, “Transmission of 300 Mbps BPSK at 39 GHz

using feedforward optical modulation”, Electron. Lett., vol. 30, no. 2,

pp. 160-161, Jan. 1994.

[3] H. Schmuck and R. Heidemann, “Hybrid fiber-radio field experiment at

60 GHz”, in Tech. Dig. 22nd Eur. Conf. Optical Communication,

Norway, Sept. 1996, vol. 4, pp. 59-62, paper ThC.1.2.

[4] D. Wake et al, “Optical generation of mm-wave signals for fiber-radio

systems using a dual-mode DFB semiconductor laser”, IEEE Trans.

MTT, vol.43, pp. 2270-2276, Sept. 1995.

[5] R.P. Braun et al, “Fiberoptic microwave generation for bidirectional

broadband mobile communications”, in Proc. IEEE MTT-S Int.

Microwave Symp., Denver, June 8-13, 1997, pp. 225-228, paper TU3E-

3.

[6] C.H.V. Helmholt et al, “A mobile broadband communication system,

based on mode locked lasers”, IEEE Trans. MTT, vol. 45, pp. 1424-

1430, Aug. 1997.

[7] R.P. Braun et al, “Low phase noise mm-wave generation at 64 GHz and

data transmission using optical sideband injection locking”, IEEE

Photonics Tech. Letters, vol. 10, No. 5, pp. 728-730, May 1998.

Project no. ISTC/MME/MSB/306

Development and characterisation of nano-porous aluminium

alloy surfaces

Arti Yadav, Subrata Chakarbarti, A. Manimaran, M.S Bobji

Abstract- We have obtained ordered porous

alumina nanostructures on aluminium alloy

surfaces of ISRO’s interest. After obtaining the

nanostructures hot sealing and heat

treatment experiments were conducted to test

the samples for any cracks that are observed

in the anodized samples generated according

to anodisation process carried out by ISRO.

We have observed that production of ordered

porous alumina structures reduced the

cracking of the Al 6061 sample after

anodisation, hot water sealing and heat

treatment. Al 7075 samples didn’t show crack

after anodisation and heat treatment but

cracked after hot water sealing.

I. Introduction:

Pure aluminium is a highly reactive metal and

develops a very thin oxide layer, of about 2-8 nm,

on the surface, whenever it is exposed to air. This

oxide layer acts as a protective layer on the

surface and prevents further oxidation of the

aluminium. In industry an anodic oxidation

process is used to produce a abrasive and

corrosion resistant protective oxide coating on

the surface. However, in the anodisation process

two different types of oxide layer forms, one type

is a porous oxide film and the other is a barrier-

type anodic oxide film [1]. However, under

certain controlled conditions anodic process can

form highly ordered nano-porous alumina

(figure 1) coatings on the aluminium surface.

Arti yadav is a graduate student in the Mechanical Engineering

Department, Indian Institute of Science, Bangalore, India

Subratha chakrabarti is Scientist F, LPSC, ISRO, Trivandrum, India.

A. Manimaran is Group Head, Components Production Group,

LPSC, ISRO, Trivandrum, India.

M.S.Bobji is an Associate professor at Mechanical Engineering

Department, Indian Institute of Science, Bangalore, India

Anodic nano-porous aluminium oxides are most

commonly formed in the solution of sulphuric

acid, oxalic acid and phosphoric acid by

anodisation of aluminium [2]. By changing the

anodisation parameters like pH of the solution,

anodizing voltage and temperature of

electrolyte, the diameter and pitch of the pores

in anodic aluminium oxide layer can be

controlled [1,2,3,4]. The pore diameter is

observed to be linearly proportional to the

anodising potential.

Figure 1: Schematic of nano-porous alumina

structure

Surface pretreatment processes like annealing

and electropolishing of the aluminium prior to

anodisation; help to improve the pore regularity.

Annealing helps relieve mechanical stress and

produces a coarse grained structure which

facilitates self-organization of the pores. Electro

polishing helps to produce a smooth surface

finish which plays an important role in regularity

of pore structure. Prior treatment of aluminium

is important because the roughness and other

parameters like voltage, type and concentration

of electrolyte, temperature, and impurities

present in aluminium including agitation of



electrolytic bath influences the formation of self-

ordering porous alumina.

Fig. 2 shows a schematic diagram of pore

formation during the anodisation process. During

the process there is a balance between oxide

dissolution at electrolyte/oxide interface and

oxide formation at oxide/metal interface. This

balance is necessary for porous alumina

formation because it results in the formation of a

constant thickness barrier layer and hence allows

the steady state pore propagation into the

aluminium foil.

With regards to orderliness and pore uniformity,

the response of pure Aluminum to the above

mentioned condition is found to be extremely

good. However, the same cannot be said with

Fig.2. Schematic of porous alumina formation on

aluminum. 1. Formation of uniform barrier oxide.

2. Electric field distribution due to surface

fluctuation 3. Initiation of pores due to field

enhanced diffusion. (4) Stable pore growth.

certainty for structural alloys of aluminum which,

due to their high strength to weight ratio, finds

wide and extensive usage in the aerospace

industry. In this study we focus our attention on

the response of AA 7075 T7352 alloy and

compare it with our observations made on AA 3

6061 T652 alloy to uniform anodisation

parameters involving Sulphuric acid electrolyte.

The comparison is based on our observations

made on both these alloys after completion of

anodisation, dyeing and hot water sealing. The

chemical composition and physical properties of

AA 7075 T 7352 alloy are as follows [5].

This study was necessitated due to the high

rate of incidence of delayed leakage of high

pressure helium gases through O-Ring and plastic

seals involving anodized Aluminium alloys as was

observed in the Liquid Propulsion Systems Centre

of Indian Space Research Organisation. In order

to compare the anodisation response of this alloy

with other Aluminium alloys, an AA 6061 alloy in

its T652 temper condition was also taken and a

study was conducted on both these alloys

simultaneously in an attempt to understand the

differences and similarities.

II. Experimental Details:

We have obtained disc type specimens with AA

6061 T 652 alloy and AA 7075 T 7352 alloy

materials (Ø20 mm and 4 mm thick respectively)

from ISRO and anodised by two step anodisation

process at a constant voltage of 40 V at 18 0C in

0.3M oxalic acid solution in IISc.

Nano-porous alumina was formed by two step

anodisation process [6]. The pre-treatment of the

samples were performed before anodising.

Initially, to remove the impurity from the sample

after etching foil was rinsed with distilled water

for several times. Subsequently electropolishing

was performed in perchloric acid and Ethanol (in

the ratio of 1:4) solution at 10oC.

After pre-treatments the two step anodisation

begins, in the first anodisation process 0.3 M

oxalic acid solution was used to obtain porous

alumina layer. The applied constant voltage in

the anodisation process was 40V at temperature

of 180 C. The oxide layer created during the first


anodisation is chemically etched by using

phosphoric-chromic acid solution. Second

anodisation process and parameters were same

as used in first anodisation.

III. Results and Discussion

Characterisation of Standard anodisation

samples

To understand the existing industrial anodizing

process, we obtained samples from ISRO and

anodized as per their standard practices. SEM

images of the anodized samples show that for AA

7075 T 7352 has cracks on the surface. It is also

apparent that even after dying surface showed

cracks. However, for sealed and dyed AA 6061 T

652 alloy, there are no cracks present on the

sample.

Fig 3 (a) SEM image of sealed Al7075 alloy (b)

magnified image of fig (a), (c) SEM image of dying and

sealing Al7075 alloy (d) magnified image of fig (c), (e)

SEM image of dying and sealing Al6061 alloy (f)

magnified image of fig (e)

Controlled anodisation of Aluminium alloys.

Anodisation on alloy Al 6061

Fig 4 Time variation of current during first

anodisation of aluminium ally Al6061

A typical current–time relation (figure 4)

recorded during anodisation of aluminium in

oxalic acid at constant anodising voltage shows

that initially during the process current density

decreases rapidly with time and a minimum value

of current is achieved within first few seconds.

Current then slightly increases to give a local

maximum. After that it gradually reduced and

achieves a steady state current at around 3-4 min

of anodisation time. The SEM image (figure 5) of

the anodized sample showed non-uniform pores

formed on the surface

Fig 5 SEM image of Al6061 alloy after first

anodisation


Fig 6 Time variation of current during second


Fig 7 (a) SEM image of Al6061 alloy, anodised at

ISRO (b) SEM image of Al6061 alloy after second

anodisation (anodised at IISc)

Current–time relation during second anodisation

of aluminium alloy 6061 (figure 6) looked similar

to that observed for first anodisation. Fig 6 (a)

shows the SEM image of Al6061 alloy, anodised

at ISRO and Fig 6 (b) SEM image of Al 6061 alloy,

anodised at IISc by two step anodisation process.

From the images it is clear that the experimental

sample which was carried out at IISc has more

uniform porous structure compared to the

experimental sample from ISRO.

For Al 7075, current-time relation (figure 8) for

first anodisation is slightly different from that

observed for Al 6061 specially in the initial stages

of anodisation. Though current decreases rapidly

within first few seconds, it showed slight

oscillations in current not observed for Al 6061

alloy. However, after around 3-4 min a steady-

state current of the porous oxide is achieved

similar to that for Al 6061 alloy. SEM image of

Al7075 alloy after first anodisation shows that

the pores were formed on the surface, but it is

not uniform and pore growth is random

compared to Al 6061.

Anodisation on alloy Al 7075

Fig 8 Time variation of current during first


Fig 9: SEM image of Al7075 after first anodisation

From the graph it can be seen that, at the initial

period of the process current decreases quickly


with time similar to alloy 6061. A linear increase

then leads to a local maximum current. After

reaching the second maximum, the current start

decreasing slightly and a steady-state current is

achieved.

Fig 10 Time variation of current during second

anodisation of aluminium alloy Al7075

Fig 11 SEM image of Al7075 alloy, anodised at

ISRO and Fig13 (b) SEM image of Al7075 alloy

after second anodisation

From the Fig 11 (b), it is observed that Al 7075

alloy also have the uniform porous structure

compared to the process carried out at ISRO (Fig

10(b)).

Hot water treatment for 15 min on alloy Al 6061

& alloy Al 7075

After the two step anodisation process of both

the Al6061 and Al7075 aluminium alloys samples,

hot water treatment was performed on both the

surfaces for 15 min in boiling water. Figure 12 (a)

shows the SEM image of Al6061 alloy after

heating with boiling water and Fig 12 (b) SEM

image of Al7075 alloy after hot water treatment

using boiling water. It is found that there are no

cracks formed in the alloy Al6061 (figure 12(a)).

Fig 12 SEM images after hot water treatment

using boiling water for 15 min of (a) Al6061 alloy,

(b) Al7075 alloy

Hot water treatment for 1 hr on alloy Al 6061 &

alloy Al 7075

The hot water treatment on both the surfaces

Al6061 and Al7075 aluminium alloys samples

have been carried out for 1hr in boiled water.

Figure 13 (a) shows the SEM image of Al6061

alloy after hot water treatment using boiling

water and Fig 13 (b) SEM image of Al7075 alloy

after hot water treatment using boiling water.

We have found there are cracks formed in alloy

Al6061 but some cracks are observed in Al 7075

sample.


Fig 13 SEM images after hot water treatment

using boiling water for 1 hr of (a) Al6061 alloy,

(b) Al7075 alloy

Heat treatment for 3 hr on alloy Al 6061 & alloy

Al 7075 at 300oC

We carried out heat treatment experiments to

check whether the cracking is resulting from

formation of hydroxide during sealing or due to

exposure to high temperature. For this we have

heated the sample in absence of water to 300oC

in a furnace preventing hydroxide formation.

Figure 14a, b show that both aluminium alloy

samples did not develop any crack even after a

soaking time of 3 hr at 300oC. This probably

confirms that cracks in aluminium alloy 7075 are

formed due to hydroxide formation. Further

confirmation could come from low temperature

methods of sealing

I. Conclusion:

Standard anodisation procedures in ISRO

produce micro cracks on the surfaces of the Al

7075 alloy. Uniform and ordered nanostructures

in aluminium alloys of both Al 6061 & Al 7075 are

achieved using a lab scale procedure. It is found

that surfaces of the Al 6061 alloy showed no

signs of cracks after anodisation and sealing in

hot water. It is also found that surfaces of the Al

7075 alloy are not forming cracks after

anodisation but form cracks after hot water

sealing. It is also found that there are no cracks

formed on both alloys Al 6061 and Al 7075

anodized and heat treated at 300oC for 3 hr.

Fig 14 SEM images after heat treatment at 300oC

for 3 hr (a) A l6061 alloy, (b) Al 7075 alloy

References 1) Masuda H, Hasegwa F and Ono S 1997 Self‐Ordering of

Cell Arrangement of Anodic Porous Alumina Formed in

Sulfuric Acid Solution J. Electrochem. Soc. 144 L127–30

2) O’Sullivan J P and Wood G C 1970 The Morphology and

Mechanism of Formation of Porous Anodic Films on

Aluminium Proc. R. Soc. Lond. Ser. Math. Phys. Sci. 317

511–43

3) Masuda H and Satoh M 1996 Fabrication of Gold Nanodot

Array Using Anodic Porous Alumina as an Evaporation

Mask Jpn. J. Appl. Phys. 35 L126

4) Lei Y, Cai W and Wilde G 2007 Highly ordered

nanostructures with tunable size, shape and properties:

a new way to surface nano-patterning using ultra-thin

alumina masks Prog. Mater. Sci. 52 465–539

5) Rocca E, Vantelon D, Gehin A, Augros M and Viola A 2011

Chemical reactivity of self-organized alumina nanopores

in aqueous medium Acta Mater. 59 962–70

6) Masuda H and Fukuda K 1995 Ordered metal nanohole

arrays made by a two-step replication of honeycomb

structures of anodic alumina Science 268 1466–8

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