implementation of flow manufacturing and process control in nanoparticle synthesis...
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IMPLEMENTATION OF FLOW MANUFACTURING AND PROCESS CONTROL IN NANOPARTICLE SYNTHESIS BY THE WET CHEMISTRY METHOD
By
JIAQING ZHOU
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2012
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© 2012 Jiaqing Zhou
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To my wife, Xingyu Zhao, who supports me with endless love.
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ACKNOWLEDGMENTS
Five years has passed since I started my PhD study in University of Florida. As the
first experiment of living far away from home, I feel so fortunate to study in such peaceful
but energetic campus. I appreciate all the help and guide that I received in these years.
First, I would like to acknowledge my advisor, Dr. Kevin Powers, for his patient
guidance and great support. His enthusiasm for science and technique always
encouraged me to pursue the knowledge behind superficialities. My sincere thanks go to
the other members of my PhD supervisory committee, Dr. Hassan El-Shall, Dr. Brij
Moudgil, Dr. Wolfgang Sigmund and especially Dr. Spyros Svoronos for their invaluable
discussion and suggestions.
I am grateful to my research group members and all the staff at the Particle
Engineering Research Center (PERC) for their assistant in these years. Many thanks to
Dr. Ajoy Saha, Dr. Megan Hahn and Dr. Parvesh Sharma for their knowledge and
experience about quantum dot synthesis, to Dr. Gill Brubaker and Gary Scheiffele for
their guidance and suggestions on the instruments and particle characterization, to Dr.
Kerry Siebein from Major Analytical Instrumentation Center (MAIC) for her assistance
with TEM and SEM, to Jim from Chemical Engineering for the assistance of machining
and to Paul Carpinone for his help in all aspects of my research.
I would like to acknowledge the Center for Particulate & Surfactant Systems
(CPaSS) and National Science Foundation for financial support and friendly research
environment.
I would like to acknowledge the support I have received from my parents and wife
throughout my academic career with the reliable and warm affection which always
releieves my pressure.
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Finally, I especially thank my uncle, Renliang Xu, who illuminated the way towards
my dream.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 8
LIST OF FIGURES .......................................................................................................... 9
LIST OF ABBREVIATIONS ........................................................................................... 13
ABSTRACT ................................................................................................................... 14
CHAPTER
1 INTRODUCTION .................................................................................................... 16
Promising Materials: Nano-particles ....................................................................... 16
The Emerging market for Nano-particles ................................................................ 16
Synthesis of Nano-particles .................................................................................... 17
The Advantages of Wet Chemistry Processes ........................................................ 18
Current Barriers for Commercialization of Nanotechnology .................................... 18
The Potential Solution: Flow Chemistry .................................................................. 19
Gap Analysis and Statement of Problem ................................................................ 20
2 BACKGROUND ...................................................................................................... 22
Flow Synthesis and Process Cntrol ........................................................................ 22
Silica Synthesis and Applications ............................................................................ 25
Mechanism ...................................................................................................... 25
Reproducibility ................................................................................................ 26
Application of Silica in the Flow/Micro System ................................................ 27
Dye Doped Silica ............................................................................................ 27
Flow Synthesis plus Feedback Control for CdTe Nano-particles ............................ 28
Core-Shell QDs ............................................................................................... 30
QDs in the FSS ............................................................................................... 30
3 STOBER SILICA SYNTHESIS BY FLOW MANUFACTURING WITH PROCESS CONTROL .............................................................................................................. 31
Stober Silica Particles Made by Batch Synthesis .................................................... 31
The Assembly of the Flow Synthesis System ......................................................... 32
Materials ................................................................................................................. 33
Characterization ...................................................................................................... 33
Online detectors .............................................................................................. 34
DelsaNano ............................................................................................... 34
Nanotrac (Microtrac Inc.) ......................................................................... 35
Comparaison between batch and flow synthesized Stober silica particles ..... 36
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Optimization of the Flow System ............................................................................ 36
Sufficient Reaction time .................................................................................. 36
Heating effect .................................................................................................. 37
Stability and accuracy ..................................................................................... 38
Sedimentation in tube ..................................................................................... 38
Process Control ...................................................................................................... 40
Size map based Control .................................................................................. 40
Map the Size Range ................................................................................ 40
Control Algorithm ..................................................................................... 42
Feedback Control ............................................................................................ 43
Methods ................................................................................................... 45
Results and Discussion............................................................................ 45
Dye Doped Silica .................................................................................................... 50
4 HYDROTHERMAL QUANTUM DOT SYNTHESIS IN FSS AND PROCESS CONTROL .............................................................................................................. 88
Conversion from Batch to FSS ............................................................................... 88
Instrument and Design ............................................................................................ 89
Results and Discussion........................................................................................... 90
Effect of reagent concentration on QDs .......................................................... 91
Effect of reaction temperature on QDs ............................................................ 93
Effect of residence time................................................................................... 94
XRD characterization of CdTe QDs ................................................................. 95
TEM characterization of CdTe QDs synthesized at 180°C .............................. 96
Thermal Control ...................................................................................................... 96
Process Control .................................................................................................... 100
Graphical process identification from step responses ................................... 101
Cohen-Coon tuning method .......................................................................... 103
Ziegler–Nichols tuning method ...................................................................... 105
Core-shell QD in FSS ........................................................................................... 106
Materials and method ................................................................................... 107
Results and Discussion ................................................................................. 108
5 CONCLUSION AND FUTURE WORK .................................................................. 149
Summary .............................................................................................................. 149
Conclusion ............................................................................................................ 150 Future Work .......................................................................................................... 150
LIST OF REFERENCES ............................................................................................. 152
BIOGRAPHICAL SKETCH .......................................................................................... 162
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LIST OF TABLES
Table page 3-1 Several formulas for different size of Stober silica .............................................. 56
3-2 Experimental setup for mapping size range ....................................................... 56
3-3 Detailed experiment for covering silica size range.............................................. 56
3-4 Step change of flow rate and resulting particle size ............................................ 57
3-5 K, τ, and D’s found from the step change data. .................................................. 58
4-1 Calculated Step change data for K τ D ............................................................. 115
4-2 Preliminary batch test of coating with sodium thiosulfate ................................. 116
4-3 Residence time and temperature effect on CdS coating .................................. 117
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LIST OF FIGURES
Figure page
3-1 Piston pump from Syrris Co ................................................................................ 54
3-2 Sketch of flow system ......................................................................................... 55
3-3 Detection of bi-dispersed Stober silica particle by multiple techniques ............... 56
3-4 Calibration of DelsaNano by LS13320 ................................................................ 59
3-5 Sketch of online DelsaNano and its dilution system and flow chart for the DelsaNano online detector system ..................................................................... 60
3-6 Particle size distribution of Stober silica measured by the Coulter LS13320 ...... 61
3-7 The relationship between particle size distribution’s standard deviation and mean size of batch made Stober silica ............................................................... 62
3-8 SEM picture of batch made Stober silica. ........................................................... 63
3-9 The relationship of settling distance in 90min with M.V. particle size for the Stober silica suspension. .................................................................................... 64
3-10 Stability of FSS. The residence time was controlled at 30 minutes .................... 65
3-11 Repeat experiments about the flow rate changed in 30min tube reactor ............ 66
3-12 Gradually decrease of particle size during the long-term operation of FSS without ultrasonication. ....................................................................................... 67
3-13 Cross-section of PTFE tubing showing the sedimentation of silica particle on the tube wall ....................................................................................................... 68
3-14 Stability test on FSS with ultrasonicator. ............................................................. 69
3-15 Tri-axial diagram of particle size map. ................................................................ 70
3-16 Three-dimensional graph of particle size map .................................................... 71
3-17 Flow chart of the size map based control. .......................................................... 72
3-18 Size map based on size map control method. .................................................... 73
3-19 Ammonia step up data, with a 2 period moving average, ammonia flow rate increase from 0.15mL/min to 0.17mL/min. ......................................................... 74
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3-20 Ammonia step down data, with a 2 period moving average, ammonia flow rate decreased from 0.15mL/min to 0.13mL/min. ...................................................... 75
3-21 Water step up data, with a 2 period moving average, water flow rate increased from 0.15mL/min to 0.17mL/min. ........................................................................ 76
3-22 Water step down data, with a 4 period moving average, water flow rate decreased from 0.15mL/min to 0.13mL/min. ...................................................... 77
3-23 Bode Plot created using AAS_ECH4323NP. The plot showing Bode Stability lines for our transfer function. ............................................................................. 78
3-24 GM and PM from Bode plot: ............................................................................... 79
3-25 Simulation of simple step change of ammonia flow rate. .................................... 80
3-26 Simulation of feedback control with set point changed from 240 to 300. ............ 81
3-27 Simulation of feedback control with discrete (stepped) flow rate. ....................... 82
3-28 Simulation of feedback control with discrete flow rate and noise of data. ........... 83
3-29 Feedback control in the flow synthesis system including a set point change at time zero. ............................................................................................................ 84
3-30 Flow chart of feedback control algorithm ............................................................ 85
3-31 Dye doped silica samples prepared by FSS. ...................................................... 86
4-1 Flow system for QD synthesis .......................................................................... 110
4-2 Concentration effect of [Cd2+] and [NAC] on QD’s reaction speed and QY ...... 111
4-3 Normalized emission spectra for QDs synthesized at different temperatures and relationship between λmax and temperature ............................................... 112
4-4 Normalized emission spectra for QDs synthesized with different residence time and relationship between λmax and residence time ................................... 113
4-5 The calculated QD average radius as the function of residence time and the plot of cube of average QD radius as a function of residence time. .................. 114
4-6 Images of QDs prepared via continuous flow ................................................... 115
4-7 XRD patterns of the CdTe QD by flow synthesis at different residence time. ... 116
4-8 TEM image of QD produced under 180°C with a residence time of 3.5 seconds. ........................................................................................................... 117
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4-9 Sketch of heating system ................................................................................. 118
4-10 The temperature response of new heating system with increase set point ...... 119
4-11 The temperature response of new heating system with increase set point ...... 120
4-12 The temperature response of new heating system with decrease set point ..... 121
4-13 Step change data for heating system from MV 40 to 35 ................................... 122
4-14 QD emission wavelength disturbed by temperature deviation .......................... 123
4-15 Performance of heating system with PI controller (Kc= 3.6, τI=13.15, set point at 120 / 135 / 145) .................................................................................... 124
4-16 Performance of heating system with PI controller (Kc= 1.8, τI=13.15, set point at 120)...................................................................................................... 127
4-17 Performance of heating system with PI controller (Kc= 0.9, τI=13.15, set point at 130)...................................................................................................... 128
4-18 The performance of heating system with on-off controller and its effect on stabilizing the QD emission wavelength. .......................................................... 129
4-19 The potential relationship between flow rate, reaction time and emission wavelength ....................................................................................................... 131
4-20 Step change from 0.5 to 0.6mL/min, 1.5 to 1.6 mL/min, 2.5 to 2.7 mL/min, 3.5 to 3.8 mL/min .................................................................................................... 132
4-21 K,τ,D calculated and simulated from step change data. ................................... 134
4-22 C-C method Kc and 𝜏𝐼. .................................................................................... 135
4-23 C-C method tuning (Feedback control for Stainless steel tubing with c=(a)1, (b) 0.5, (c) 0.25). .................................................................................................... 136
4-24 The weight of Kc and τI in tuning program for c=0.25(a), 0.5(b) and 1.0 (c). .. 139
4-25 Z-N method Kc 𝜏𝐼. ............................................................................................ 141
4-26 Z-N method tuning (530nm(a), 580nm(b), 637nm(c). ....................................... 142
4-27 The weight of Kc and τI in tuning program for set point=530. ......................... 145
4-28 Red shift of emission wavelength from the coating of CdS shell at 120°C. ...... 146
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LIST OF ABBREVIATIONS
ACF Autocorrelation function
APTS 3-Aminopropyltriethoxysilane
C-C Cohen-Coon
Cy Cyanine
DI Deionized
DLS Dynamic light scattering
DDS Dye doped silica
FITC Fluorescein isothiocyanate
FSS Flow synthesis system
IR Infra-red
LD Laser diffraction
M.V. Mean volume
MV Manipulated variable
PI Proportional-Integral
PL Photon luminescence
QD Quantum dot
QY Quantum yield
SD Standard deviation
SS Stainless-steel
TEOS Tetraethyl orthosilicate
TMR Tetramethylrhodamine
UV Ultraviolet
Z-N Ziegler–Nichols
NAC N-Acetylcysteine
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
IMPLEMENTATION OF FLOW MANUFACTURING AND PROCESS CONTROL IN
NANOPARTICLE SYNTHESIS BY THE WET CHEMISTRY METHOD
Jiaqing Zhou
August 2012
Chair: Kevin William Powers Major: Materials Science and Engineering
Nano-particle manufacturing is a promising industry in the near future. Several
methods are used for nano particle production. One method, called the wet chemistry
technique, is widely used, but lacks reproducibility and scalability when batch processed.
Possible solutions that avoid these problems are the flow synthesis system (FSS) and
process control. However, despite their benefits, these methods are relatively new in the
nano particle field. The combination of these two methods and their benefits shows
potential in novel industrial-scale manufacturing of nano particles.
In order to establish a system that monitors and controls the product quality, both
online/inline measurements and sized map based/feedback process controls are
introduced into the FSS. In order to study the efficacy of the process controls on particle
properties such as size distribution, the Stober silica model was chosen to develop and
test the FSS.
Two types of process control were investigated in the Stober silica process. The
size map based control was established by building an experimental database and using
it to model the relationship between mean volume (M.V.) particle size and reagents’
concentration. The second method used feedback control with a PI controller. Its
parameters were derived from the Cohen-Coon method.
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Two high-value colloidal products, dye doped silica and cadmium telluride quantum
dots (CdTe QDs) were studied in the FSS as case studies. The synthesis of dye doped
silica followed a modification of the Stober process to incorporate various fluorescent
dyes into the product. Rubpy and Rhodamine 6G (R6G) dyes were physical adsorbed
and fluorescein isothiocyanate (FITC) and 7-methoxycoumarin-3-carboxylic acid (MCA)
were chemically bonded in doping the silica particles.
CdTe QDs in the emission range of 500 - 800nm were synthesized hydrothermally
by controlling the reaction temperature and the residence time in the flow reactor. The
effects of temperature, reagent concentration, and residence time on the emission
spectrum were studied. The results indicated that higher concentrations of cadmium
(Cd2+) ions and lower concentrations of N-acetylcysteine (NAC) produce QDs with a high
quantum yield (QY) of 40- 60% in a much reduced reaction time compared to batch
synthesis.
The process control of the CdTe QDs relies on a proportional – integral (PI)
controller. Both the Cohen-Coon and the Ziegler-Nichols tuning methods were used for
the tuning parameters. The control algorithm was able to reach the desired emission
wavelength in around 10 minutes with a precision of 2 nanometers (nm). Furthermore, a
novel coating method for CdTe/CdS core/shell QDs was developed for the FSS using
controlled degradation of sodium thiosulfate in an acidic environment. This resulted in a
Type II quantum dot where the emission spectrum of the QDs was red shifted up to
70nm.
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1CHAPTER 1 INTRODUCTION
Well Promising Material: Nano-particles
Nano-particles, or ultra-fine particles, are defined as materials with at least one
dimension between 1 to 100 nm. Although this definition has been proposed only in
recent times, nano-particles applications have been involved in human history ever since
ancient times. As far back as the 4th century, evidence showed that the Romans already
mastered the technique to generate an optical dichroic effect in glass vessels by using
silver and gold nano-particles1. The pottery from the middle ages and renaissance were
often covered by glaze layers that contained copper and silver nano-particles2. The first
scientific description of nanoparticles was mentioned by Michael Faraday and described
in his paper published in 18573. It was only in the early stages of the 20th century that
nano-particles began to play a significant role in various technologies such as colloidal
systems. Today, these advanced technologies have greatly affected people’s lives in
areas that include energy, healthcare, computers, microelectronics, optical engineering
and many other areas using advanced materials.
The Emerging market for Nano-particles
The Nanotechnology market is rapidly expanding in market value for a wide variety
of applications. According to a report by Electronics.ca Publications®, the global
market value for the nanotechnology was estimated to be $15.7 billion in 2010, and was
expected to have a compound annual growth rate (CAGR) of 11.1% for the next 5 years.
BY 2015, the estimated global market in nanotechnology is expected to increase to over
$27 billion. As the largest segment in the market, the nanomaterials market is expected
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to increase from nearly $10 billion in 2010 to $19.6 billion in 2015 with an annual growth
rate of 14.7%4.
Despite the exploding market value, nanotechnology commercialization is still at a
very early stage. This immature market indication is illustrated by the large difference
between the growth of patents and the number of products. According to the recent
nanotechnology product inventory from the project on emerging nanotechnologies at the
Woodrow Wilson International Centre for Scholars (www.nanotechproject.org), 1317
nanotechnology related products or product lines were being produced globally in 2011,
up substantially from 2006 when it listed only 212 products.
Synthesis of Nano-particles
There are four fundamental routes for nano-materials synthesis including
form-in-place processes, mechanical processes, gas phase synthesis, and wet
chemistry processes5. Each of these methods has its own advantages and limitations so
that the resulting products have unique properties.
Form in place processes. These include lithography, vacuum deposition, and
spray coating. These techniques directly generate nano-materials as surface layers for
other products. They are more suitable for nanostructured layers and coatings, but they
can still be used to manufacture nano-particles by separating deposits from collectors.
The limitations of these methods are the relatively low efficiency when they are used for
dry powders synthesis.
Mechanical processes. These are “top-down” methods that reduce particle sizes
by collision and attrition, i.e. grinding, milling and mechanical alloying techniques.
Advantages of these age-old techniques are simple, widely applicable and low cost.
However, it is hard to achieve fine particles by these methods due to the increasing
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surface energy and the tendency to agglomerate. Other difficulties include broad particle
size distributions and contaminationfrom milling media and equipment.
Gas phase synthesis. This includes flame pyrolysis, electro explosion, laser
ablation, high temperature evaporation, and plasma synthesis techniques. These
processes generate nanomaterials through chemical reactions or physical evaporation at
high temperature. The advantages of gas phase synthesis method are the clean and
controllable environment and temperature. However, the high temperature feature also
excludes the processing of organic materials
Wet chemistry processes. These are fundamentally “bottom-up” techniques that
the formation of insoluble compounds starts from the mixture of ions or molecules. These
processes include colloidal chemistries, hydrothermal methods, sol-gel, and other
precipitation process.
The Advantages of Wet Chemistry Processes
Wet chemistry processes currently provide better quality nano-particles which
result from the following aspects. Firstly, agglomeration and aggregation of the products
can be reduced or eliminated by designed inter-particle forces. Secondly, nano-particles
can be synthesized with narrow or mono-disperse size distribution. Finally, it is capable
of finely controlling nano-particles’ chemical composition, purity and morphology. This is
important for appliations that require high repeatability.
Current Barriers for Commercialization of Nanotechnology
The main barriers for commercialization of nanotechnology deal with four domains
according to the report of “Lowering Barriers for Nanotechnology Commercialization”
project from European Commission6. Those four domains are including manufacturing
domain, technological domain, marketing & strategy domain, and investment &
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organization domain. The Manufacturing domain suffers from lack of maturity including
the lack of funding and equipment to achieve scale-up of production. The technological
domain is related to the reproducibility and long term reliability of the system. The
marketing and strategy domain involves the agreement between market opportunities
with technical development. The investment and organizational domain involves return
on investment and the required dedicated manufacturing infrastructure.
The lack of reproducibility and long term reliability are always technological
problems when the wet chemistry processes scales up to large quantities. The major
reason for this phenomenon is the difficulty in simultaneously controlling all parameters.
The agglomeration of nano-particles is aoften a problem due to the enhanced
temperature and concentration gradients in the pilot scale reactors7.
The Potential Solution: Flow Chemistry
Batch methods are generally used for preparing nano-particles by wet chemistry
methods. They require the precise control of experiment conditions that determine
properties of produced nano-particles. Unpredictable deviations of experimental
conditions often result in disparities between different batches in terms of the size
distribution, the zeta potential etc.
Automated and miniaturized continuous flow synthesis methods, also known as
flow chemistryare a well-established technique for manufacturing large quantities of a
given material and have been proposed as an improved alternative to overcome these
limitations. In flow synthesis methods, chemical reactions run in continuous flow streams
rather than in batch containers, i.e. reactions take place when reactive fluids are driven
into tubes by pumps. Compared to traditional batch mode reactions, flow synthesis
methods have several advantages. Firstly, faster and uniform mixing of reagents can be
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achieved because of the smaller cross-section of tube. Secondly, temperature in flow
system is more controllable due to the decrease in thermal mass. The system
temperature can also be increased above the normal boiling point by applying pressure
using a backpressure regulator. Thirdly, the reaction time can be determined precisely
by calculating residence time in the tubing. The introduction of additional reagents can
be controlled precisely at desired time point8. Fourthly, the flow synthesis system can be
automated with far less expense than batch systems. It is possible to establish an
automated system that can change reaction parameters to optimize products’ qualities
with little intervention and loss9. Finally, the flow synthesis system is able to scale up
without losing control of reaction conditions by increasing the diameter of the system or
the number of tube reactors.
Gap Analysis and State of Problem
Flow synthesis methods are relatively new in the laboratory, especially in the area
of nano-particle synthesis10. Research into flow synthesis methods mostly focuses on
organic reactions that have quite different properties from nano-particles. The effects of
scale-up on system performances and process control are also absent Therefore, the
first objective of this research is to establish a flow synthesis model system in a
well-known and representative nano-particle synthesis process. The Stober silica
process was selected as the model process because the relation between its particle
size and reaction conditions is typical and well understood. The effects of Stober silica’s
properties on the performance of the flow synthesis system are evaluated. The process
control system for particle size is tested on this model system by tuning the reagents’
concentration. Several particle size characterization instruments are modified as
inline/online detectors.
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The second goal is to extend the flow synthesis system into more functional and
high value-added nano-products. Two case studies were selected due to the high
interest and high value added in these products: dye doped silica and quantum dots. Dye
doped silica is one of the important post-products of the Stober silica process. It has
been extensively used in photonics materials11,12, in nonlinear optical materials13, and in
the bioimaging and biochemical analysis applications14. The synthesis of dye doped
silica in the flow synthesis system is studied by using four types of dye molecules.
Quantum dots are another good sample of high value-added materials
($3000-$10000 per gram15). They have been involved in various applications such as
LEDs16, solar cells17, video displays, diode lasers18, and bio-imaging19. In this study, the
hydrothermal synthesis route for CdTe quantum dots is applied in the flow synthesis
system. The system parameters, including temperature, reagent concentration, and
residence time are studied and optimized for the quantum yield. The process control is
based on the quantum dots’ peak emission wavelength by tuning the residence time in
the hot zone of the reactor. As a bonus, an in-line CdS coating process was developed
for the flow synthesis system to generate Type II CdTe/CdS core-shell quantum dots in
this project.
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2CHAPTER 2 BACKGROUND
The Flow Synthesis and the Process control
The flow synthesis system has been found to be applicable in many new fields,
including disciplines in chemistry and biology20. In the field of the microfluidic system,
more and more reports have been reported regarding innovative approaches, where
there is an integration of the advantages from the flow synthesis system and the
economy due to the reduced volume. The microfluidic system is ideal to processing
experiments with less costly materials.
A whole flow synthesis system normally includes the following parts:
Pumps. The precise control of transporting fluid is the foundation of a controllable
FSS. It can be achieved either with an integrated mechanical and electrical actuation or
by temperature and pressure gradients. The syringe pump is one of the mechanical
pumps commonly used in the microfluidic system for non-pulsating flow21. The piston
pump is another source for the continuous and steady flow with a higher pressure and a
larger reservoir. This is achieved by combining the two pistons, which work
interchangeably. In addition to mechanical pumps, other researchers have developed
several micro-pumps based on pH gradients22, pressure23, laser induced cavitation24,
and temperature sensitive hydrogels25 among others.
Mixers. The behavior of fluids at the micro-scale can be different from those at the
macro scale. The Reynolds number becomes very low when the channel diameter
ranges between 100 nanometers to several hundred micrometers26. Therefore,
non-turbulent flow in microfluidic channels makes mixing a challenging task because
diffusion might be too slow so that the time or channel length becomes unacceptable27.
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Current mixing methods for microfluidic systems include static mixers28-35 and external
mechanical actuation methods such as acoustics36 and electro-osmotic based
mixers37-39.
Reactors. Reactors for the flow synthesis system are typically tube like and
fabricated by non-reactive materials such as glass40, silicon41, stainless steel and
polymers42,43. Important properties of a suitable material include the cost of fabrication,
machinability, working temperature, thermal conductivity, inertness, surface charge,
molecular adsorption, optical properties, and others43. Surface modification may be
required due to the specific desired surface properties from the flow system
applications44-46. The types of reactors include spinning disc reactors, multi-cell flow
reactors, oscillatory flow reactors, heat-exchanger reactors, and micro-reactors among
others.
Problems are generated when the reactor’s size scales down to the micrometer
range. Tube blockage becomes the biggest hurdle for an application involving
particulates47,48. Furthermore, any gases that are generated from the reaction, pressure
decrease, or temperature variance may affect the residence time of the reagents by
pushing out fluid faster than expected.
Detectors. Various detection systems have been reported for their reliability and
repeatable online/inline measurements. Maimiroli.et al. reported a free jet micromixer
that was combined with low angle X-ray scattering for the study of fast chemical
reactions49. Amarie et al. introduced the surface plasmon resonance to study glucose
oxidase binding activity in a microcavity50. Staples and co-workers have demonstrated
mass spectroscopy/liquid chromatography detection methods for analyzing
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glycosaminoglycan on a chip51. Carter et al. demonstrated online non-intrusive
measurement of particle size distribution through digital imaging52. Electrostatic sensors
are an additional technique for the particle size measurement53-55. Traditional batch
based instruments also can provide potential online/inline measurements through the
application of flow cells.
Automatic controls: Three types of control systems are available: the open loop,
the feed forward, and the feedback control system. However, the open loop system is a
manual control, with no automatic response to the environmental disturbance. This
system is commonly used in most of the lab experiments.
The feed forward control system has significant benefits when a predictable
disturbance occurs upstream of the system, if the mathematical model is reliable and the
control law is followed entirely (i.e. the controller predicts the incoming disturbance and
compensates for it). The feed forward control relies on the accuracy of the disturbance
measurement as well as the noise and the accuracy of the feed forward gain and the
timing. An ideal feed forward system can overcome the oscillation and the delays of the
output while maintaining the system stability.
The limitation of the feed forward control is obvious. The control system can only
respond to the disturbance in a pre-defined way, which usually means that the
disturbance must be predictably stable with time. The introduction of any unknown
disturbance or input will result in an inaccuracy. Thus, the feed forward control system is
best for a well understood process, or for those processes whose behaviors can be
easily measured and replicated under known operating conditions.
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The feedback control monitors system output through detectors and checks the
difference between the target value and the output, which is defined as the error. The
control system adjusts the input to minimize this error. A familiar and fundamental
example of feedback control is the on and off control, such as those found in ovens
which utilize a common temperature control system to supply or not supply heat to the
oven.
The fact that the feedback control obtains data at the process output brings both
pros and cons. Although a full understanding is not required of the system or the
mathematical model for the control system, the feedback control method requires time to
correct the output after the disturbance occurs. Extreme conditions such as large
magnitude disturbances or large time delays may cause the control system to work
inefficiently.
Silica Synthesis and Application
The Stober silica is an example of a well-characterized process for producing
mono-dispersed silica particles. Since Stober first described the growth of
mono-dispersed silica particles in alcohol in 196856, hundreds of papers have been
published about the various applications of silica particles in bio-imaging57,
nano-carriers58,59, pigments, and stabilizers60 among others.
Mechanism
The Stober process involves the complex reactions between water, Si(OR)4 and
ammonia.
The overall reaction can be shown as:
ROHSiOOHORSi OHNH 42)( 2244 (2-1)
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The reaction indicates that two moles of water are required to stoichiometrically
react with one mole of TEOS. The water, ammonia and Si(OR)4 concentration as well as
the type of alcohol solvent are considered to be critical parameters for affecting particle
size. Unlike the multiple intermediate products during the acid-catalyzed gel synthesis,
the hydrolysis reaction produces only the single-hydrolyzed monomer61-63, which is
accumulated at the beginning, or induction time. Nucleation occurs when
single-hydrolyzed monomers become saturated. Mono-dispersed particles retain the
growth afterwards until all the reactants are exhausted.
The reaction mechanism is explained by the following two models: the LaMer
model64-66, which indicates that nucleation happened only once during the whole process
followed by continuous particle growth, and the controlled aggregation model, which
suggests that the growth of the particles results from the aggregation of the small
particles67-69. Lee et al.63 supported the controlled aggregation model by examining the
profile of the intermediates’ concentration using 29Si-NMR. However, Harris70 and van
Blaaderen et al.71 suggested that both models contribute to the particle growth: the
controlled aggregation model controls the reaction speed while the LaMer model makes
the surface smoother .
Reproducibility
The Stober silica process is a good example of a sensitive reaction in that the
resulting particle size distribution can vary due to the influence of the conditions in the
reaction. The results from Stober et al.56 show an error in the range of hundreds of
nanometers in experiment replications. The precise control of the particle size is difficult
due to the poor reproducibility. It is hard to validate the potential reasons. One
explanation is that the precise control of the nucleation depends heavily on the saturation
26
of the hydrolyzed Si(OR)4, yet any subtle changes of the reagents’ concentration can
alter the induction time. For instance, both the alcohol solvent and ammonia are volatile
materials and these can easily affect the reagents’ concentration.
Application of Silica in Flow/Micro System
Several groups reported the application of the Stober silica process in FSS.
Ferguson et al.72 tested a modified Stober process using a continuously stirred tank
reactor, yet the resulting particle size distribution was quite broad. Her et al.73 reported
the application of a static mixer tubular reactor with a 0.8 cm PTFE tube. They suggested
that the reaction time in the continuous tubular reactor was narrow compared to the
batch method. Herbert Giesche74 established a FSS by peristaltic pumps, a mixer and
3mm/6mm diameter silane tubes. His results showed a broad deviation in repetition.
Furthermore, there was also a phase separation inside the tube, where a particle deposit
existed at the bottom of the tube. Ogihara et al.75 introduced the Couette-Taylor vortex
FSS for the silica particle synthesis, which can continuously work for five hours giving
comparable products to those obtained using the batch system. The process control and
the online measurements was absent in the previous research studies.
Dye Doped Silica
Dye doped silica has wide applications in the biomedical field. It was first
developed in 1992 by Vanblaaderen et al. using the Stober method via fluorescein
isothiocyanate (FITC) dye molecule conjugated with 3-aminopropyltriethoxysilane
(APTS)76. Following this, numerous studies were done in applying different dyes into
silica matrix. Santra et al. reported the FITC doped silica particles using the reverse
microemulsion method77. The Rubpy dye molecule is doped by the reverse
microemulsion method78. The fluorescence spectra, particle size, and size distribution of
27
these particles have been tested for optimization79. Xiaojun Zhao et al. reported
tetramethylrhodamine (TMR) doped silica particles by the reverse microemulsion
method and also tested the leakage of the dyes80.
Core-shell structures have been developed for further protection of dye molecules
from photo bleaching and leaking. Santra et al. reported a core-shell silica particle with
FITC dye doped in the core by both the Stober81 and reverse microemulsion methods82.
Hooisweng Ow et al. synthesized a TRITC doped core-shell silica particle in 200583.
Xichun Zhou et al. reported a hybrid core-shell particle containing an Au core with
Cyanine 3 (Cy3)/Cyanine 5 (Cy5) that was chemisorbed and a silica coating bearing thiol
functional groups for microarray-based DNA bioanalysis84.
Multi-dye doped silica particles have also been developed. Lin Wang et al. created
silica particles entrapped with two fluorophores, OsBpy and RuBpy, simultaneously by
reverse microemulsion85.
Flow Synthesis plus Feedback Control for CdTe Nano-particle
Studies in quantum dots increased after Murray et al.86 developed the conventional
synthesis route. The excellent optical properties, such as the quantum yield and the
resistance to photo bleaching, made quantum dots highly promising for applications in
various fields like solar cells87 and biological labels88.
The characteristics of the QDs came from the quantum confinement effect. When
the size of the QDs is smaller than the critical characteristic length (Exciton Bohr radius),
the original energy levels start to split into smaller ones with gaps between each
successive level. The electronic and optical properties of the particles change with small
enough particle size (typically less than 10nm) with the band gap increasing as particle
size decreases. QDs are direct band gap materials. The fluorescence is a result of the
28
excited valance electron returning to the ground state combining with the hole. The
fluorescent wavelength is determined by the size of the quantum dot since the energy of
the emitted photon can been seen as a sum of band gap energy, the confinement energy
of the hole and the excited electron, and the bound energy of the exciton.
The organometallic method and the hydrothermal method are the two main
methods for synthesis of QDs. Although the organometallic synthesis is the most widely
used technique, there is more and more interest in the aqueous synthesis method since
it was introduced by Gaponik et al.89 Compared with the organometallic synthesis, the
hydrothermal synthesis is less toxic, less costly, and more productive, with a high
stability and biological compatibility89. However, the traditional disadvantages of QDs
made by the hydrothermal method include a broad emission peak, a longer process, and
a relatively low quantum yield.90 These disadvantages are mitgted using the Flow
method described here.
Various attempts have been made to explore the condition of hydrothermal QD
synthesis and to improve its luminescent properties: different thiol compounds were
tested as a stabilizing agent91; the ratio of ligand and monomers were fine-tuned by Guo
et al.92; the relationship between heating temperature and particle growth speed was
reported by Zhang et al.93; Juandria et al.94 developed the rapid hot-injection method by
which the reaction time was reduced down to 1-10min at a high temperature of
200-240°C.
Core-Shell QD
Core-shell QDs are one of the active fields in the QD research because of their
novel properties. By coating higher band gap inorganic materials, the core-shell QDs
have a red-shifted emission wavelength and a longer decay lifetime due to the formation
29
of the indirect electron excitation. The photoluminescence(PL) quantum yield and the
photo-stability are also improved due to the reduction of surface defects95. Despite the
core-shell QDs being prepared through organometallic methods96-98, preparation through
the hydrothermal method is more attractive99 due to the advantages that this provides.
QD in FSS
There has been increasing use of such microfluidic devices in the production of
various QDs100. For example, CdSe101-103, CdS104,105, and InP106 have been synthesized
using the organometallic method with microfluidic techniques. Yang et al. reported
core-shell structure QDs using a microfluidic device by a two-step organometallic
method107. However, no publication has related the hydrothermal method synthesis of
QD in the FSS.
30
3CHAPTER 3 STOBER SILICA SYNTHESIS BY FLOW MANUFACTURING WITH PROCESS
CONTROL
Stober Silica Particles Made by Batch Synthesis
Silica nano-particles were first synthesized by the batch method as a pre-study and
to provide a control for the implementation of the flow synthesis. These batches were
carefully characterized through particle size analysis and by SEM imaging. Reagents
included, ammonia (37%wt, Acros Organics), DI water (Barnstead Nanopure Infinity,
18M/cm-1) and Tetraethoxy Silane (TEOS 98%Acros Organics). The ammonia and
water were carefully measured and mixed with half amount of required pure ethanol (200
proof) in a sealed glass flask with magnetic stirring for 2 minutes. The tetraethyl
orthosilicate (TEOS, 98%, Acros Organics) was diluted with the other half of the ethanol
and poured slowly into the ammonium solution. The solution becomes opaque as the
particles nucleate and grow large enough to scatter light. Induction and growth can take
several minutes to hours depending on the target size. The solution is kept at room
temperature and stirred rapidly until the reaction is completed. The completion of the
reaction is assessed by the cessation of particle growth as determined by laser
diffraction size analysis. Quenching the reaction is possible by two methods:
(1) Trimethylmethoxysilane (( ) ) can be added to react with active
silanol sites on the hydrolyzed TEOS, interrupting the condensation and growth of the
particles.
(2) Pouring water into the system (best if reaction already passed induction time for
several minutes and became turbidity), which dilutes the concentration of all reagents
and inhibits further growth.
31
The reaction time typically varies from 30 minutes to several hours depending on
formula used. An hour is sufficient to end the reaction of those sizes larger than 150nm,
while more time is required for smaller particles. The resulting suspensions are washed
with ethanol and water by centrifuging (Beckman JA-21 Centrifuge) at 5000 rpm. Table
3-1 lists the common formulas for different sizes silica nano-particles.
The Assembly of the Flow Synthesis System
The initial model of FSS was based on the FRX100 from Syrris Co. which included
three piston pumps, two tube reactors, a pressurization module and a sample collector.
The wetted materials in the FSS include sapphire, PTFE, ruby and PEEK, which are
chemically inert. The piston pumps (Figure 3-1) are able to provide a non-impulse
continuous stream by the reverse stroke of two pistons. The flow rate provided by each
pump ranges from 0.01 to 9.99mL/min with a precision of 0.3% (measured at 1mL/min)
and an accuracy of ±1% (measured at 1mL/min). The two reactors are constructed of
0.8mm PTFE tubing with a volume of 4mL and 16mL respectively. The pressurization
module is designed to control the backpressure of the FSS in the range 0-10bar. The
FSS can be controlled manually or by computer using Labview software.
Later on, other add-ons were installed to enhance the system performance.
Different sizes of PTFE tubes (inner diameter 1/16”, 1/8”, Sigma-Aldrich) were
purchased to provide flexibility in controlling the linear flow rate (residence time) and to
facilitate limited scale up studies. A hot plate was used to control the reaction
temperature. An ultrasonicator was modified by connecting its control panel with a USB
relay controller to achieve PC controlled periodic sonication. A dilution system was
constructed and connected at the end of tube reactor to adjust the particle concentration
to that required for the online detector. Two dynamic light scattering (DLS) instruments,
32
the Nanotrac (Micromeritics Corp) and the DelsaNano (Beckman-Coulter Inc.), were
adapted as the online detector for the particle size distribution measurement. Figure 3-2
shows a sketch of the whole system.
Materials
The Stober reaction is well suited as a model for designing and testing the flow
system. The reagents are easily separated into three parts, ammonia, water and TEOS,
and introduced independently by three computer controlled pumps. Since ethanol is
required as a diluent, a 1:5 volume ratio of X (X=ammonia or water or TEOS) to ethanol
is used in each stream. This enhances mixing and prevents the premature reaction of
the precursors. The total flow rate is adjusted to achieve the desired residence time and
initially was set to 0.7mL/min, providing a 30min reaction time in the 20mL FRX tube
reactor and a 90min overall residence time in the system (63mL PTFE tube loop).
Characterization
The LS13320 (Beckman Coulter, Inc.) was chosen to make the external particle
size distribution measurements (as a reference) due to the excellent accuracy and
precision of the laser diffraction (LD) technique. In the size ranges produced here,
Stober silica can be considered an ideal particulate system for virtually all sizing
techniques due to its spherical and monodisperse qualities. This can be seen by
applying several common size measurement techniques as shown in Figure 3-3.
Additional characterization was applied by scanning electron microscopy (SEM, JEOL
6335F FEG-SEM) on filtered and air dried samples. Although all are very close, laser
diffraction had the closest mean value and distribution details (shoulder and tail in the left
side of the distribution) to image analysis carried out by SEM. Consequently, LS13320x
33
was also utilized to calibrate the results from DelsaNano (Beckman Coulter, Inc.) and its
flow cell used as an in-line size measurement.
Online detectors
DelsaNano
The DelsaNanox is based on dynamic light scattering (DLS), which determines the
particle size by detecting fluctuation rates of reflected or scattered laser intensity from
particles’ Brownian motion. The fluctuation rate of the light intensity is transferred into
dynamic information of the particles by autocorrelation function (ACF) which is used to
derive the Diffusion coefficient of the particles. Using the Stokes Einstein relation
(Equation 3-1), the hydrodynamic diameter (size) of the particle is calculated. This
technique is able to measure particle size from 1nm to several microns.
dn =kBT
πηDT (3-1)
Where dh = hydrodynamic diameter, kB = Boltzman Constant, - solvent viscosity and
DT = Translational diffusion constant.
The precision and accuracy of the DelsaNano are lower than LS13320. In order to
minimize these errors, 15 silica samples with gradually increased M.V. particle sizes
from 80nm to 514nm were prepared and measured by both instruments for 5 replicates
as shown in Figure 3-4. The results illustrate that the DelsaNano has a nearly linear
deviation from the Coulter LS13320. A linear compensation factor can be calculated by
the following equation:
y = 1.0806x + 4.1316 (3-2)
Although the DelsaNano is designed as benchtop instrument, it was adapted to
online measurement through the use of a flow cell (International Crystal Laboratories,
34
UV-VIS Cells/Type 42 Flow Through Cell with 10mm light path) instead of normal
cuvettes. The DelsaNano’s working concentration is relatively high because it only
collects the reflection light from inner surface of flow cell instead of transmitted Still, a
dilution system was required to improve the precision when the instrument was used for
more concentrated samples. Figure 3-5 shows the sketch of DelsaNano on-line detector
system and its operation sequence. The keyboard and mouse control software, Quick
Macro (Brother Macro Co.), was installed with DelsaNano’s software to control the
software. The measurement sequence starts with a 2.5 minute flush followed by the
introduction of the new sample into the flow cell. Then a 2 min equilibration took place to
prevent any disturbance on the particles’ Brownian motion. The following size
measurement took 3.5 min to finish. Finally, the measurement result was saved as a text
file and loaded into the database by control software (Labview 6, National Instruments
Corporation) while the flush for next measurement conducted. The design of dilution
system required a very smooth flow of the silica suspension to prevent clogging of the
system which tends to happened at the T-type tube connector and the pressure
regulator. The silica suspension was continuously driven into the dilution system by
slight pressure difference between two tubes. The pressure difference resulted from the
height difference the dilution system and the FSS’s collection end, which was finely
adjusted for a proper shunt ratio.
Nanotrac (Microtrac Inc.)
The Nanotrac is another instrument that relies on DLS technology. Different from
DelsaNano, it has a laser backscatter probe that transmits reflected light from the sample
through a sapphire window. A measurement chamber was designed and constructed
to pass the flowing product over the Nanotrac probe. This arrangement served as an
35
in-line (rather thanthe Delsa’s on-line arrangement) because of the Nanotrac’s high
working concentration. Thus it did not require auto dilution. The measurement sequence
for Nanotrac was similar to that for DelsaNano, except for the absence of this dilution
system.
Comparability between batch and flow synthesized Stober silica particles
Stober silica is well known for its mono-dispersed particle size distribution, as
shown in Figure 3-6a. The SEM result (Figure 3-8) confirmed its spherical shape and
uniform size distribution. The breadth of the size distribution increases slowly as size
increased, as shown in Figure 3-7, from a geometric standard deviation of 10nm (at a
mean M.V. size = 50nm) to about 80nm (M.V. size = 600nm). As particle size increases
above 600nm a shoulder begins to appear indicating a bimodal distribution. This is a
common characteristic of the Stober process and is caused by the high concentration of
TEOS required and a second nucleation event (Figure 3-3). The Stober silica made by
the FSS showed similar properties as batch made silica. As shown in Figure 3-6b, a
typical flow synthesized silica particle was well mono-dispersed and had a standard
deviation equivalent or slightly smaller than batch made silica particles.
Optimization of the Flow System
Sufficient Reaction time
The reaction time of Stober silica process varies with the formula and the target
particle size. In general, the higher the relative concentration of ammonia and TEOS,
the higher the reaction rate, thus the shorter the reaction time to completion. A more
succinct relationship is found between the M.V. particle size and reaction time due to the
straightforward relation between reagent concentration and particle size. Giesche108
quantified the growth of particle size as a function of time by the light scattering method
36
with four formulas. Here, 20min is required for the synthesis of 417nm particles,
(consistent with our experience). As expected, increasing temperature also reduces the
reaction time significantly. Expand and support this statement
During the design of the FSS, the residence time must be considered at the
beginning because it governs the overall flow rate by the equation:
React on t me =Tube volume
Flow rate (3-3)
Sufficient residence time maximizes the yield of reaction, diminishes the residual
reactants and reduces the dead time for quality control. For larger particles, a 30min
residence time is generally sufficient but longer times are required as the target particle
decreases. At a given flow rate, this requires the addition of a longer length of tubing.
Heating effect
Heating is another option for controlling particle size distribution and reaction rate in
the Stober silica reaction. The increase of reaction temperature leads to the decrease of
particle size. According to Giesche108, the particle size dropped from 665nm to 309nm
when the temperature increased from 293k to 313K with the same reactant
concentrations. The particle size further decreased to 186nm at 333K (60°C). The drop
in particle size indicates an enhanced nucleation event which is the primary determinant
of the final particle size. The increase of temperature also speeds up the reaction,
obtaining these smaller particles in shorter time. While heating has several benefits for
the system, the system was not yet configured for controlling temperature during these
early studies. Therefore the flow rate was chosen as the only parameter for particle size
control in the Stober silica study.
37
Stability and accuracy
The accuracy of the FSS was a key to the design of the process control algorithm
since it determined the precision with which the product size could be controlled. A
series of experiments were made with 30min residence time to monitor the consistency
of the M.V. particle size and size distribution (S.D.). Samples were collected every 3min
and measured off-line by laser diffraction (LS13320) for the best accuracy and
resolution. As shown in Figure 3-10, six experiments with different size ranges indicated
that FSS was able to produce silica particles with steady particle size distribution in the
30min residence time reactor. Repeat experiments under the same reaction conditions
(Figure 3-11) confirmed that FSS was able to duplicate the same size silica particle (P
value = 0.072 for 188nm particle and P value= 0.445 for 320nm particle in Student’s
t-test) at the same reaction conditions.
Sedimentation in tube
Although the FSS worked well with 30min residence time in the reactor, the
long-term test with 90min PTFE tube loop was initially interrupted with serious problems.
As shown in Figure 3-12, M.V. particle size continuously decreased during the operation
of the FSS and lost about 400nm size in 18 hours. Further investigation revealed that
silica particles were depositing on the walls of the tubing forming a thick layer which
reduced the residence time (Figure 3-13). There was also settling and stratification of
the larger particles due to the low flow rates and lack of sufficient mixing
The stratification of the suspension results from t sedimentation, and is one of the
causes of tube blockage. This phenomenon is rarely observed in turbulent flow, but
becomes distinct in laminar flow where the reaction is time-consuming and the particle
38
size of products ranges in hundreds of nm scale. Stokes’ law demonstrates the
sedimentation in suspension as shown in Equation 3-4,
𝑣𝑠 =2
9
(𝜌𝑝−𝜌𝑓)
𝜇𝑔𝑅2 (3-4)
where vs is the particles’ setting velocity (m/s), ρp is the mass density of particle
(kg/m3), ρf is the mass density of fluid (kg/m3), μ is the dynamic viscosity (N×s/m2), g is
the gravitational acceleration (m/s2) and R is the radius of the particle (m).
Figure 3-9 shows the calculated distance from Stokes’ law that a certain silica
particle could settle in 90min in the ethanol solution. The 400nm silica particle, for
example, can settle for 0.47mm in 90min, which is significant in a 1/16” (1.6mm) tube.
Considering the gradient of velocity in laminar flow, the silica particles can precipitate
even more at the flow conditions near the tubing wall, where the flow rate is much slower
than that in the center of tube. As a result, particles begin to accumulate at the bottom of
the tube with a much slower movement that are likely to clog the system when tube
diameter changed at connector.
Thus the size decrease of silica particle with time resulted from two conditions: the .
First, the product consisted of primarily the smaller particles exiting the reactor tubing
with the larger particles settling out in the tubing. Thus the results only represented the
tail of the true distribution. Second, more particles were adsorbed on part of the tube wall
which related to the induction section during the operation. The additional particles may
work as the extra nucleus due to their large surface area, thus resulted less silica
precursor per particle, i.e., smaller particle size.
The introduction of sonication to the flow loop solved this problem. The sonication
helped mix the solution as well as preventing particles from adsorbing on the tube wall.
39
The ultrasonicator was set to operate on aperiodic schedule, 1min every 3min and a
cooling system was attached to keep the bath at room temperature. As shown in Figure
3-14, the M.V. particle size in a 22 hour running was stabilized at 285nm. The standard
deviation of the mean volume size distribution throughout the experiment is 10.5nm. This
minor fluctuation in the result is due to the low accuracy of DelsaNano at this size range.
Process control
The FSS, online detector and control software provide the tools for designing either
a size map based or feedback control system. The size map based control relies on the
precise database or library of initial conditions which can be rapidly generated by the
flexibility of the system. The more conventional feedback control is dependent on
designing a suitable algorithm that capitalizes on the real-time on-line/in-line
measurement of the product. These will be introduced in the coming paragraph
respectively.
Size map based Control
Map the Size Range
Mapping the size range of the FSS product was important to the size map based
control system since it provides the information between particle size and flow rate which
is necessary to the control algorithm. An initial design was made for the 20mL tube
reactor to explore the size range as shown in Table 3-2. In this design, the total flow rate
was fixed at 0.7 mL/min to fix the reaction time. With the fixed flow rate, the FSS has
two degrees of freedom left, any two of the concentrations of the three reactants
(ammonia, water, TEOS) can be adjusted but the third must bring the sum to the total
flow rate of 0.7 mL/min. The tri-axial diagram (Figure 3-15) depicts the relationship
between three flow rates and the resulting particle size. The characterization was done
40
by both the LS13320 and Nanotrac to cover the whole range of particle size. The results
show that particle size can be well controlled in the range from 29nm to 401nm.
Interestingly, the tri-axial diagram indicates that in certain cases there are multiple
conditions that can produce the same particle size. Samples with less than 100nm M.V.
particle size were far from 100% yield, since the reaction time for such particle size
usually takes hours.
A more detailed experiment was done to gather information about the relationship
between the particle size and the flow rate. Among the three reagents, the TEOS
concentration is thought to have the smallest effect on the particle size108. Thus, the
ammonia and water flow rate were chosen as the main control parameters. The 63mL
PTFE tube loop was used in this design, ensuring enough residence time (90min) for the
reaction. The DelsaNano and LS13320 were used for the size characterization, the
DelsaNano for in-line and the LS for off-line post reaction sizing. The experimental
design and results are shown in Table 3-3. The test flow rate range was located in the
middle of the tri-axial diagram from 0.2 to 0.3 mL/min for water and 0.1 to 0.2 mL/min for
ammonia for the sake of balancing the consumption of reagents. This design provided a
full coverage of particle size from 90nm to 495nm where both the reaction time and
mono-dispersity can be ensured by FSS. Furthermore, the particle sizes corresponded
to every 0.01mL/min step change of flow rate (Table 3-4). Conditions were selected to
determine what the effect of TEOS concentration on the trend illustrated in the overview
3-D chart in Figure 3-16.
Control Algorithm
Figure 3-17 shows the fundamental regulation of the size map based control
system. The process starts by inputting the target M.V. particle size in the software after
41
the FSS was in the common running state. Once the target size was set, the software
initiated a search for the optimum flow rate combination in the database which was
indexed and calibrated from detailed mapping and calculation step. The flow rate
combination was then delivered to pumps for the tube reaction (dead time: 90min). After
the reaction, the product was delivered by the dilution system to the online detector
where the particle size distributions were measured and sent back to the control
software. A reliability test was applied to the M.V. size from online detector during the
monitoring. Specifically, the standard deviation (SD) of the three latest M.V. size were
calculated and compared with the resolution of DelsaNano at that particular size to
confirm proper operation.
The reliability of the size map based control depends on the validity of the
mathematic model, the. database in this case. An additional control loop was added
alongside with the main route to minimize the error from unpredictable disturbances, for
example, the changes of reagent concentration. After the reliable testing, the average of
last three M.V. size were calculated and further compared with target size. If the S.D. is
within the precision expected of the DelsaNano, it would be added to the database and
reset the flow rate.
Two samples for the size map based control and their final flow rate set are shown
in Figure 3-18. The target sizes were set as 260nm and 430nm respectively and the
resulting products have 1.9% and 2.6% deviations from the target.
Feedback Control
The feedback control model is an important fraction of process analytical technique
that provides the compensation mechanism to an unknown disturbance with relatively
low workload and understanding of the system. The ultimate objective is to create a
42
system that will provide feedback to the control system to obtain a desired particle size
during non-steady state conditions by adjusting the flow rate of inputs. This system is
integrated with a Proportional-Integral (PI) controller.
Methods
First order plus time delay (FOPTD). FOPTD process is one that shows an
exponential response to an input step change with a delayed response. The output
response of FOPTD to a change in input can be mathematically presented as the
following equation109:
𝑦′(𝑡) = {0, 𝑡 < 𝐷
𝐾∆𝑢 [1 − 𝑒−𝑡−𝐷
𝜏 ] , 𝑡 ≥ 𝐷 (3-5)
where K is the process gain, τ is the time constant, D is the time delay and ∆𝑢 is the
input.
The FOPTD Exxon-Three-Point Method is used to determine the above three
unknown parameters. This method involves finding the times when process reached
25% and 75% of output. K, τ and D can be derived by the following equations:
𝐾 =∆𝑌
∆𝑈 (3-6)
τ =𝑡75−𝑡25
1.1 (3-7)
D = 𝑡25 + Ln(0.75)τ (3-8)
where Y is the output and U is the input.
Bode Plot. A bode plot can be constructed to determine frequency response
information of a given transfer function. After calculating Gu (the transfer function of
FOPTD model, Equation 3-9) from the step up and step down experimental data and Gc
(the transfer function of feedback controller, Equation 3-10) from the Cohen-Coon
43
method, the GOL (the product of all transfer function in the loop, Equation 3-11) can be
calculated for the Bode plot.
𝐺𝑢(𝑠) =𝐾
τs+1𝑒−𝐷𝑠 (3-9)
𝐺𝑐(𝑠) = 𝐾𝑐 (1 +1
τ𝐼s) �̅�(𝑠) (3-10)
𝐺0𝐿(𝑠) = 𝐺𝑢(𝑠) × 𝐺𝑐(𝑠) (3-11)
From the Bode plot we can conclude whether the transfer function meets the Bode
Stability Criterion. The criterion says that a closed loop system is stable only if the bode
plot of GOL has:
AR(ωco) < 1 (3-12)
Or
Log (AR(ωco)) < 0 (3-13)
A measure of stability can be determined by calculating the gain margin (GM) and
phase margin (PM) from the Bode plot. The GM is the difference between Log (AR(ωco))
and Log (AR(ωco)) = 0. The PM is the difference between φ(AR=1) and -180º. A rule of
thumb for safety says that the GM should be at least 1.7 and the PM should be at least
30º.
Tuning Methods. The Cohen-Coon (C-C) tuning method is one method to tune the
PI controller. Tuning parameters for control gain (Kc) and integral time constant (τi) for
the PI controller are determined by the following equations:
𝐾𝑐 =1
𝐾
τ
𝐷(0.9 +
𝐷
12τ) (3-14)
τi = 𝐷 0+
3𝐷
τ
9+20𝐷
τ
(3-15)
44
Another tuning method is the Ziegler–Nichols (Z-N) tuning method. Its control
parameter derived from the ultimate gain (Ku) and ultimate period (Pu) that brings the
closed loop system to the verge of instability. Ku and Pu can also be derived from the
Bode plot of Gu’ by the following equations:
𝐾𝑢 = 1
𝐴𝑅 (3-16)
𝑃𝑢 = 2π
ωco (3-17)
The formulas used to find Kc and τi are:
𝐾𝑐 =𝐾𝑢
2.2 (3-18)
τi =𝑃𝑢
1.2 (3-19)
Results and Discussion
First Order Plus Time Delay:
To find the FOPTD model, the FSS was set on manual mode and waited till the
process was at the steady state, with inputs at 0.15, 0.27 and 0.28 mL/min for ammonia,
water and TEOS respectively. A step change was then introduced to the ammonia flow
rate, increasing the flow rate from 0.15mL/min to 0.17 mL/min. After 1.5 hours, when
another steady state was reached, the FSS was set back to nominal steady state. The
step change down was done in a same procedure by decreasing ammonia flow rate from
0.15mL/min to 0.13mL/min for a period of 1.5 hour.
The Exxon method was applied to calculate the process gain, time constant and
time delay109. Figure 3-19 shows the step up increase in flow rate of ammonia, the flow
rate was increased from 0.15mL/min to 0.17mL/min. K, τ, and D were obtained by the
following calculations:
45
𝐾 =∆𝑌
∆𝑈=
(287nm−244nm)
(0.17mL
min−0.15mL
min)=2150nm
mL/min (3-20)
τ =𝑡75−𝑡25
1.1=1.74h−1.51h
1.1= 0.209h (3-21)
D = t25 + Ln(0.75)τ = 1.51h + Ln(0.75) ∗ 0.209h = 1.45h (3-22)
Similarly, Figure 3-20 to Figure 3-22 were used to calculate K, τ, and D for step
changes made in ammonia and water, and the results are shown in Table 3-5.
Gu(s) was derived by using the average values of the step change data from
ammonia step changes, specifically:
𝐺𝑢(𝑠) =𝐾
τs+1𝑒−𝐷𝑠 (3-23)
𝐺𝑢(𝑠) = 2 75
0.171s+1𝑒−1.47𝑠 (3-24)
Tuning Methods:
The average value of both ammonia and water flow rate were applied to calculate
the parameters for the PI controller. In C-C method, Kc and τI were calculated as
followed:
Ammonia:
Kc =1
𝐾
τ
𝐷(0.9 +
D
12τ) =
1
2 75 nm/mL/min
0.171h
1.47h(0.9 +
1.47h
12∗0.171ℎ) = 7.90 ∗ 10−5 m n/(mL ∗
nm) (3-25)
τI = D 0+ 𝐷/τ
9+20 𝐷/τ= 1.47h
0+ ∗1.47h/0.171h
9+20∗1.47h/0.171h= 0.453h (3-26)
Water:
Kc =1
𝐾
τ
𝐷(0.9 +
D
12τ) =
1
1250 nm/mL/min
0.0977h
1.55h(0.9 +
1.55h
12∗0.0977ℎ) = 1.12 ∗ 10−4 m n/(mL ∗
nm) (3-27)
τI = D 0+ 𝐷/τ
9+20 𝐷/τ= 1.55h
0+ ∗1.55h/0.0977h
9+20∗1.55h/0.0977h= 0.369h (3-28)
In Ziegler–Nichols (Z-N) method, the calculation for ammonia is shown below:
𝐺𝑢(𝑠) = 2375 𝑒−1.47𝑠
0.171𝑠 +1 (3-29)
Since Gu(0)= 2375 (positive), Gu’= (sign Gu(0))*Gu
46
From Bode Plot of Gu’ we obtain:
At Phase Lag (φ= -180), Log AR= 3.353, Log W= 0.2849.
𝐾𝑢 = 1
𝐴𝑅 = 0.0004436 (3-30)
𝑃𝑢 = 6.28
𝑊 = 3.2589 (3-31)
𝐾𝑐 = 𝐾𝑢
2.2 = 0.0002016 (3-32)
𝜏𝐼 = 𝑃𝑢
1.2 = 2.7157 (3-33)
𝐺𝑐 = 𝐾𝑐 (1 +1
𝜏𝐼𝑠) = 0.0002016 (1 +
1
2.7157 𝑠) (3-34)
𝐺𝑂𝐿 = (𝐺𝑐) ∗ (𝐺𝑢) (3-35)
𝐺𝑂𝐿 = (1.3𝑠 + 0.4788)𝑒−1.47𝑠
0.46 𝑠2+0.7158𝑠 (3-36)
The calculation for water using averages is shown below:
𝐺𝑢(𝑠) = 1250𝑒−1.55𝑠
0.098𝑠 + 1 (3-37)
Since Gu (0) = 1250 (positive), Gu’= (sign Gu(o))* Gu
From Bode Plot of Gu’ we obtain:
At Phase Lag (φ= -180), log AR=3.089, Log W=0.2822
𝐾𝑢 = 1
𝐴𝑅 = 0.0008147 (3-38)
𝑃𝑢 = 6.28
𝑊 = 3.279 (3-39)
𝐾𝑐 = 𝐾𝑢
2.2 = 0.0003703 (3-40)
𝜏𝐼 = 𝑃𝑢
1.2 = 2.7325 (3-41)
𝐺𝑐 = 𝐾𝑐 (1 +1
𝜏𝐼𝑠) = 0.0003703 (1 +
1
2.7 25 𝑠) (3-42)
𝐺𝑂𝐿 = (𝐺𝑐) ∗ (𝐺𝑢) (3-43)
𝐺𝑂𝐿 = (1.265𝑠 + 0.4628)𝑒−1.55𝑠
0.2678𝑠2+2.7 25𝑠 (3-44)
Bode Stability and safety margins:
Using the Gu calculated form step change and the Gc, GOL was acquired to input
into the Bode plotting software by equation:
𝐺𝑜𝑙 = 𝐺𝑢(𝑠) × 𝐺𝑐(𝑠) =0.2727
(0.0775𝑠2+0.45 𝑠)× 𝑒(−1.47𝑠) (3-45)
47
Figure 3-23 shows the Bode Plot drawn from Equation 3-45, which can be used to
determine the GM and PM mathematically and graphically. The transfer function meets
the Bode stability criterion because the Log (AR(ωco)) is less than zero.
𝐺𝑀 = 1
𝐴𝑅(𝑊𝑐𝑜)=
1
0.6187= 1.62 (3-46)
𝑃𝑀 = (φAR=1) − (−180) = (−146.2) − (−180) = 33.8 (3-47)
The GM of 1.62 is close to the recommended valve of 1.7, indicating the small
possibility of instability, while the PM of 33.8 is already at the safe range (above 30
degree).
Simulation and experiment data:
There are two differences between the ideal feedback model and the model used in
the FSS. The main difference comes from the discreteness. The output flow rate from
the ideal model is continuous, while the flow rate settings are actually discrete because
of the minimum flow rate change of the pumps (0.01mL/min). The input signal (M.V.
particle size) in the ideal model is also continuous, yet the real online detector takes 8min
for each measurement. The other difference comes from the noise of the size
measurement output, which might confuse the feedback control software in the wrong
direction.
The simulation of the above differences gives a prediction of system behavior
before actually applying model into real system. In the simulation, the change of M.V.
particle size was calculated by Equation 3-5. Sum of y’(ti) (0 < ti <t ) is calculated to
decide the value of y at time t. The start point was chosen from step change data where
0.15mL/min of ammonia, 0.27mL/min of water and 0.28 mL/min of TEOS gave 240nm
M.V. particle size. The feedback control model is based on the following equation:
48
𝑢(𝑡𝑘) = 𝑢(𝑡𝑘−1) + 𝐾𝑐(𝑒𝑘 − 𝑒𝑘−1) +𝐾𝑐
𝜏𝐼∗ 𝑒𝑘 ∗ ∆𝑡 (3-48)
The discrete of input M.V. particle size was also taken in account in the simulation
system.
Figure 3-25 shows how the system would react to a simple flow rate change with
the same condition of former step change experiment. The simulated result performed
the same as experiment data with the same amount of size change, indicating that the
simulation operates properly. Figure 3-26 shows an ideal feedback control (continuous
flow rate) using Cohen-Coon method by adjusting ammonia flow rate. 5 hours was
required to reach the target size with no oscillation or steady-state offset. However, the
final flow rate (0.175mL/min) was not available with the real pumps. The discrete flow
rate was then added to the simulation for further prediction as shown in Figure 3-27. The
flow rate calculated by the control algorithm (blue line) was rounded to two decimal (red
line) to mimic the real pump. The resulting response of simulated FSS performed as
cumulative step changes. A periodic oscillation appeared from 5hour due to the
steady-state offset that cannot be avoided by the current setting and instrument in FSS.
But its effect on broaden particle size distribution can be tempered if the offset is
decreased. The noise was finally introduced to simulate the accuracy of online detector
as shown in Figure 3-28. For simplification, noised data was randomly chosen in the
range of 90%~110% of original data point instead of use Gaussian dispersion. The noise
reduced the sensitivity of control algorithm and released the error accumulation speed so
that the oscillation from the discrete flow rate almost disappeared.
The simulation of feedback model reveals two facts: First, the feedback control
should be temporarily shut down to better achieve stabilization when particle size is
49
close enough to the set point so that oscillation can be avoided. Second, a smaller offset
would help eliminate the final oscillation. The Ammonia flow rate was set to be the prime
parameter because its larger step change saves time, while the water flow rate was used
for finer control of particle size. The final control algorithm is shown in Figure 3-30.
Following these rules, the control algorithm was updated and the experiment data
is given in Figure 3-29. The start point was set the same as step change experiment,
with the M.V. particle size at 240nm, and the target size was set at 150nm. The ammonia
flow rate dominates the control algorithm when the M.V. particle size is far from target
size. The water flow rate is introduced at about 2.5hour when the difference between
M.V. particle size and target size was smaller than the minimum adjustment from
ammonia flow rate step change, although it switches between ammonia and water
several time from 2.5hour to 3hour due to the variation of particle size. The fine
adjustment from water flow rate took several hours since the error built up very slowly,
but it reached set point at 7hour finally.
Dye Doped Silica
The synthesis of Dye doped silica (DDS) is one of the extended applications for the
FSS. Among all kinds of DDS synthesis methods, the modified Stober silica methods
with chemical bonding and physical adsorption of dyes were chosen for the similarity and
operability with the earlier work. Four dyes were tested: FITC and
7-methoxycoumarin-3-carboxylic acid (MCA) were chemically bonding to silica particle
by the pre-reaction with APTS while Rubpy and Rhodamine 6G (R6G) were physically
adsorbed. For FITC and MCA’s pre-reaction, 1.5 times of APTS were mixed with
0.03mM sample in 2mL solvent (ethanol for FITC and DMF for MCA). The solution was
placed in darkness for 1 hour to enhance the fully reaction. The dye solution was then
50
mixed with 100mL of TEOS reagent (1:5 TEOS/Ethanol) and ready for FSS. The FSS
system was shielded by aluminum foil to prevent any light induced oxidation. Since it is
the modified Stober silica reaction with tiny amount of additives, the effect of dyes on
particle size is limited and the feedback control algorithm is able to manipulate size
distribution. Figure 3-31 shows a sample for each dye with difference sizes, which
proved the ability of producing selected particle size distribution DDS by FSS.
51
Table 3-1. Several formula for different size of Stober silica
Target size (nm)
Ammonia (mL)
H2O (mL)
Ethanol (mL)
TEOS (mL)
20±3.5 2 0 50 4 127±10 2 2 48 4 271±12 2 3 47 4 355±8.5 2 4 46 4 374±5 2 6 44 4 520±12 3 6 43 4
Table 3-2. Experiment set for mapping size range
No. Ammonia (mL/min)
Water (mL/min)
TEOS (mL/min)
Mean volume Size (nm)
1 0.05 0.05 0.60 No particle observed 2 0.05 0.23 0.42 29 3 0.05 0.42 0.23 54 4 0.05 0.60 0.05 No particle observed 5 0.23 0.05 0.42 60.2 6 0.23 0.23 0.24 347 7 0.23 0.42 0.05 180 8 0.47 0.05 0.23 193 9 0.47 0.23 0.05 401 10 0.60 0.05 0.05 tube clog
Table 3-3. Detailed experiment for covering silica size range
No. Ammonia (mL/min)
Water (mL/min)
TEOS (mL/min)
Mean volume Size (nm)
1 0.1 0.2 0.4 90 2 0.1 0.25 0.35 96 3 0.1 0.3 0.3 153 4 0.15 0.2 0.35 143 5 0.15 0.25 0.3 198 6 0.15 0.3 0.25 365 7 0.2 0.2 0.3 223 8 0.2 0.25 0.25 405 9 0.2 0.3 0.2 495
52
Table 3-4. Step change of flow rate and resulting particle size
Water flow rate, mL/min
0.2 0.21 0.22 0.23 0.24 0.25 0.26 0.27 0.28 0.29 0.3
Ammonia flow rate, mL/min
0.1 90 87.12 86.3 87.5 90.7 96 103.3 112.7 124.1 137.5 153
0.11 98.4 93.6 92.1 94.1 99.3 108 120 135.5 154.2 176.4 202
0.12 108 102.6 101.6 104.8 112.4 124.2 140.3 160.7 185.4 214.4 247.6
0.13 118.6 114.2 114.7 119.9 129.8 144.6 164.1 188.5 217.5 251.4 290
0.14 130.2 128.4 131.3 139.1 151.8 169.2 191.5 218.7 250.7 287.5 329.2
0.15 143 145 151.6 162.6 178 198 222.4 251.4 284.8 322.6 365
0.16 156.8 164.3 175.4 190.3 208.8 231 256.9 286.5 319.9 356.9 397.6
0.17 171.8 186.1 202.9 222.2 243.9 268.2 295 324.2 355.9 390.1 426.8
0.18 187.8 210.4 233.9 258.3 283.5 309.6 336.5 364.4 393 422.5 452.8
0.19 204.8 237.3 268.6 298.7 327.5 355.2 381.7 406.9 431 453.9 475.7
0.2 223 266.8 306.8 343.2 376 405 430.4 452 470 484.4 495
53
Table 3-5. K, τ, and D’s found from the step change data.
Changing Made K (nm/mL/min)
τ (Hours)
D (Hours)
NH3 Step Up 2150 0.209 1.45 NH3 Step Down 2600 0.132 1.49 NH3 Step Averages 2375 0.171 1.47 H2O Step Up 1100 0.105 1.51 H2O Step Down 1400 0.0909 1.58 H2O Step Averages 1250 0.0977 1.55
54
Figure 3-1. Piston pump from Syrris Co. which has two pistons works at reverse stroke.
The self-flush offered cleaning function for the piston.
55
Figure 3-2. Sketch of flow system. The regents are pumped into PTFE tube continuously
in a designed flow rate which is controlled by software. The PTFE tube is used to reduce particle attachment on the inner wall of the tube. All tubes are immersed in the Ultrasonic bath for further prevention particle attachment. Temperature control is a combination of heater and cooling water system in the ultrasonic bath.
Local network
Collection
Driven by height
difference
Ethanol
56
Figure 3-3. Detection of bi-dispersed Stober silica particle by multiple techniques. (a)
LS13320; (b) Nanotrac; (c) DelsaNano; (d)CPS disc centrifuge; (e) image analysis. The mono-dispersity of Stober silica particle is disturbed when M.V. particle size is above 550nm. Multiple test from different techniques shows that DLS method is not optimum for polydispersed samples. Compared with other instrument, the LS method has the best agreement with image analysis data.
Differential Volume (LS13320)
0
5
10
15
20
25
0.01 0.1 1 10 100 1000
Particle diameter (um)
Volume (%)
Differential volume (NanoTrac)
0
5
10
15
20
25
0.01 0.1 1 10 100 1000
Particle Diameter (um)
Volume (%)
Mean value: 534nm SD: 82.20nm
Mean value: 553nm SD: 114nm
A
B
57
Figure 3-3. continued
Differential volume (Delsa)
0
5
10
15
20
0.01 0.1 1 10 100 1000
Particle Diameter (um)
Volume (%)
Differential Volume (CPS)
0
0.5
1
1.5
2
2.5
3
0.01 0.1 1 10 100 1000
Particle diameter (um)
Volume (%)
Mean value: 532.1nm SD: 27.3nm
Mean value: 463.5nm
C
D
58
Figure 3-3. Continued
Differential volume (Image Analysis)
0
5
10
15
20
25
30
35
40
0.01 0.1 1 10 100 1000
Particle DIameter (um)
Volume (%)
Mean value: 555.4nm
E
59
Figure 3-4. Calibrated offset of DelsaNano with LS13320
50
100
150
200
250
300
350
400
450
500
550
600
50 100 150 200 250 300 350 400 450 500 550 600
Mean volume Size from LS13320, nm
Mean volume size from Delsa, nm
60
Figure 3-5. (a)Sketch of online DelsaNano and its dilution system; (b) flow chart for the
DelsaNano online detector system
Collection
Ethanol
Raw Stober silica
suspension
Peristaltic pump
Waste bottle
Received Start command from control software
Peristaltic pump running (2.5min)
Equilibration in flow cell (2min)
Size measurement
(3.5min)
Save data and deliver to control
software
61
Figure 3-6. Particle size distribution of Stober silica measured by the Coulter LS13320
(a) made by batch synthesis, (b) made by flow synthesis.
62
Figure 3-7. The relationship between particle size distribution’s standard deviation and
mean size of batch made Stober silica (trend line added).
0
10
20
30
40
50
60
70
80
90
100
0 100 200 300 400 500 600 700
Sta
nd
ard
dev
iati
on
, n
m
MV particle size, nm
The Standarddeveiation of Stobersilica particles
Poly. (The Standarddeveiation of Stobersilica particles)
63
Figure 3-8. SEM picture of batch made Stober silica.
64
Figure 3-9. The relationship of settling distance in 90min with M.V. particle size for the
Stober silica suspension.
0
0.5
1
1.5
2
2.5
3
3.5
0 200 400 600 800 1000 1200
Sd
imen
tati
on
dis
tan
ce,
mm
MV Particle size, nm
65
Figure 3-10. Stability of FSS. The residence time was controlled at 30 minutes.
Measurements were made every 2.5 minutes.
0
50
100
150
200
250
300
350
0 10 20 30 40 50 60Time,min
Mea
n v
olu
ne
size
, n
m
66
Figure 3-11. Repeat experiments about the flow rate changed from 0.15mL/min,
0.3mL/min, 0.25mL/min (Ammonia, Water, TEOS) to 0.22mL/min, 0.26mL/min, 0.22mL/min in 30min tube reactor
150
160
170
180
190
200
210
220
230
240
250
260
270
280
290
300
310
320
330
340
0 10 20 30 40 50 60 70 80 90 100 110 120 130
Time (min)
Mean volumn size (nm)
The reproducibility of flow system
67
Figure 3-12. Gradually decrease of particle size during the long-term operation of FSS
without ultrasonication.
0
100
200
300
400
500
600
0 5 10 15 20
MV
part
icle
siz
e,
nm
Time, hour
68
Figure 3-13. Cross-section of PTFE tubing showing the sedimentation of silica particle
on the tube wall. The red line is the inner surface of the tube.
69
Figure 3-14. Stability test on FSS with ultrasonicator.
100
150
200
250
300
350
0 1 2 3 4 5 6 7 8 9 10 11time, hour
Mea
n v
olu
me
size
, nm
100
150
200
250
300
350
11 12 13 14 15 16 17 18 19 20 21 22
time, hour
Mea
n v
olu
me
size
, nm
70
Figure 3-15. Tri-axial diagram of particle size map.
347 401
60.
54
No particle
29
No particle
180
193
ml/min
ml/min
ml/min
Agglomeration
71
Figure 3-16. Three-dimensional graph of particle size map
0.1
0.2
0
50
100
150
200
250
300
350
400
450
500
0.2
0.3
Ammonia flow rate, ml./min
MV
pa
rtic
le s
ize,
nm
Water flow rate, ml/min
450-500
400-450
350-400
300-350
250-300
200-250
150-200
100-150
50-100
0-50
72
Figure 3-17. Flow chart of the size map based control.
Stop flow system
Smaller
Set target size
First or not
Database
Calibrate target size
Detector Resolutio
n
SD of last three results
Set flow rate
Tube
Online detector
Display MV size
Large
Reach target?
No
Yes
No
Start flow system
Collect sample
73
Figure 3-18. Size map based on size map control method.
Target: 260nm Target: 430nm
74
Figure 3-19. Ammonia step up data, with a 2 period moving average, ammonia flow rate
increase from 0.15mL/min to 0.17mL/min.
230
235
240
245
250
255
260
265
270
275
280
285
290
295
300
0 0.10.20.30.40.50.60.70.80.9 1 1.11.21.31.41.51.61.71.81.9 2 2.12.22.32.42.52.62.72.82.9 3
Part
icle
Siz
e (
nm
)
Time (h)
Step up NH3
Series1
0.75y
75
Figure 3-20. Ammonia step down data, with a 2 period moving average, ammonia flow
rate decreased from 0.15mL/min to 0.13mL/min.
180
185
190
195
200
205
210
215
220
225
230
235
240
245
250
255
2.82.9 3 3.13.23.33.43.53.63.73.83.9 4 4.14.24.34.44.54.64.74.84.9 5 5.15.25.35.45.5
Part
icle
Siz
e (
nm
)
Time (h)
Step down NH3
Series1
.75dy
.25dy
76
Figure 3-21. Water step up data, with a 2 period moving average, water flow rate
increased from 0.15mL/min to 0.17mL/min.
230.0
235.0
240.0
245.0
250.0
255.0
260.0
265.0
270.0
6.5 7.0 7.5 8.0 8.5
Part
icle
Siz
e (
nm
)
Time (h)
Step up H2O
Data
.75y
.25y
4 per. Mov. Avg. (Data)
77
Figure 3-22. Water step down data, with a 4 period moving average, water flow rate
decreased from 0.15mL/min to 0.13mL/min.
200
205
210
215
220
225
230
235
240
245
9.5 10.0 10.5 11.0 11.5
Part
icle
Siz
e (
nm
)
Time (h)
Step down H2O
.75dy
.25dy
Data
4 per. Mov. Avg. (Data)
78
Figure 3-23. Bode Plot created using AAS_ECH4323NP. The plot showing Bode Stability lines for our transfer function.
79
Figure 3-24. GM and PM from Bode plot:
80
Figure 3-25. Simulation of simple step change of ammonia flow rate.
230
240
250
260
270
280
290
0 1 2 3 4 5 6 7
MV
part
icle
siz
e,
nm
Time, hour
0.145
0.15
0.155
0.16
0.165
0.17
0.175
0 1 2 3 4 5 6 7
Am
mo
nia
flo
w r
ate
, m
l/m
in
Time, hour
81
Figure 3-26. Simulation of feedback control with set point changed from 240 to 300.
0.145
0.15
0.155
0.16
0.165
0.17
0.175
0.18
0 2 4 6 8 10 12
MV
part
icle
szie
, n
m
Time, hour
0
50
100
150
200
250
300
350
0 2 4 6 8 10 12
Am
mo
nia
flo
w r
ate
, m
l/m
in
Time, hour
82
Figure 3-27. Simulation of feedback control with discrete (stepped) flow rate.
200
220
240
260
280
300
320
0 2 4 6 8 10 12
MV
part
icle
siz
e,
nm
Time, hour
0.145
0.15
0.155
0.16
0.165
0.17
0.175
0.18
0.185
0 2 4 6 8 10 12
Am
mo
nia
flo
w r
ate
, m
l/m
in
Time, hour
Calculated
Real
83
Figure 3-28. Simulation of feedback control with discrete flow rate and noise of data.
200
220
240
260
280
300
320
340
0 2 4 6 8 10 12 14
MV
part
icle
siz
e,
nm
Time, hour
Simulated output w/o noise
Simulated data with noise
0.10.110.120.130.140.150.160.170.180.19
0 2 4 6 8 10 12 14
Am
mo
nia
flo
w r
ate
, m
l/m
in
Time, hour
Calculated flow rate
Real flow rate
84
Figure 3-29. Feedback control in the flow synthesis system including a set point change
at time zero.
100
120
140
160
180
200
220
240
260
280
300
0 1 2 3 4 5 6 7 8 9
MV
part
icle
siz
e,
nm
Time, hour
Result from FSS
0.125
0.13
0.135
0.14
0.145
0.15
0.155
0 1 2 3 4 5 6 7 8 9
Am
mo
nia
flo
w r
ate
, m
l/m
in
Time, hour
Real flow rate
Calculated flow rate
Coarse tuning by ammonia flow rate
0.258
0.26
0.262
0.264
0.266
0.268
0.27
0.272
0 1 2 3 4 5 6 7 8 9
Wate
r fl
ow
rate
, m
l/m
in
Time, hour
Real flow rate
Calculated flow rate
Fine tuning by water flow rate
85
Figure 3-30. Flow chart of feedback control algorithm
Online measurement
Start FSS
Set target size
No Yes Is error larger
than threshold?
Ammonia flow rate adjust
Water flow rate adjust
Calculate flow rate output
Set new flow rate
Stop flow system
86
Figure 3-31. Dye doped silica samples prepared by FSS. A: 112nm silica particle doped by Rubpy; B: 171nm silica particle doped by R6G; C: 370nm silica particle doped by MCA; D: 338nm silica particle doped by FITC
87
4CHAPTER 4 HYDROTHERMAL QUANTUM DOT SYNTHESIS IN FSS AND PROCESS CONTROL
Convert from Batch to FSS
The routine route for the hydrothermal CdTe QD synthesis method was reported by
Guo et al.110 First, there is generation of Te2- ions in the water by reducing the tellurium
powder with sodium borohydride (NaBH4, 98%) under nitrogen protection followed by
storage in a refrigerator overnight. Second, the Cd2+, Te2- and the ligand are mixed in a
ratio of 2:1:4 in a Parr acid digestion bomb with an adjusted pH and then heated to the
target temperature for the required time. The resulting QD properties vary with different
temperatures and reaction times. To achieve the desired emission wavelength, the
reaction time can vary from 60 min to several hours111,112.
After repeating the batch method hydrothermal CdTe QD synthesis, several
problems were revealed. First, there were unreacted particles visible at the bottom of the
solution after reduction, which may be contaminants that resulted from the tellurium
powder (99%, FisherSci). Second, the Te2- ions are highly sensitive to oxygen, which
requires nitrogen protection during the entire synthesis process. Any leak can cause the
formation of tellurium nano-particles that will turn the solution to pink or black. This poses
a problem in determining the true concentration of Te2- ions actually involved in the
reaction because the tellurium metal cannot react.
Several improvements were made on the formulas to avoid the above problems.
Tellurium metal particles were replaced by 100% soluble tellurium chloride salt (TeCl4,
99%, Acros Organics) in case any particles were brought into the FSS. However, the
reduction of TeCl4 requires four times the amount of NaBH4 compared with tellurium
powder, yet the reaction is faster and is not limited to the surface reaction. The
88
concentration of the tellurium ions was reduced to 0.5mM compared to the 7.5mM in the
original batch method110. The low concentration of QDs not only reduces the risk of tube
blockage caused by oxidized tellurium powder, but also may suggest a potential
condition to obtain high photoluminescence of the QDs113 because the excessive
tellurium ion damage the thermodynamically favorable structure.
Briefly, the TeCl4 was first weighed and dissolved in DI water with droplets of 1M
NaOH solution. Then, the solution was sealed in a reagent bottle and bubbled with
nitrogen until the whole process was finished. An hour later, NaBH4 was weighed and
dissolved with water (pH 9.3) and quickly injected into the reagent bottle. The whole
bottle was then warmed by a hot plate set at 80°C with a stirrer to accelerate the
reduction. Any generated hydrogen gases were removed by nitrogen and a fume hood
so that the risk of explosion was eliminated. After the reaction finished, the solution was
cooled down to room temperature and then was ready to use. On the other hand, CdCl2
was dissolved in DI water with N-acetylcysteine (NAC, 99%) in another reagent bottle
with pH adjusted to 9. The formation of the coordination compound at an alkaline pH is
required to avoid Cd(OH)2 precipitates114. A 1:1 molar ratio was found to give the lowest
ratio at which NAC and CdCl2 are completely dissolved at a pH of 9. The Cd reagent
bottle was also bubbled with nitrogen for one hour to prevent any oxygen penetration.
Instrument and design
Instruments. The FSS is composed of two piston pumps (Syrris Co.), a PTFE tube
(0.75mm ID), stainless-steel (SS) tubes (1/16” & 1/32” ID), and a backpressure regulator
(IDEX Co.). A Hitachi F-2000 fluorescence spectrophotometer was modified as an inline
detector for the emission spectra by the application of a flow quartz cuvette of 10mm
path length (NSG Precision Cells). All optical measurements were carried out at room
89
temperature under ambient conditions. The pH measurements were made by the AR60
pH meter (FisherSci). Transmission electron microscopy (TEM, JEOL 2010F) was used
to characterize the CdTe QDs. Labview 8.5 software was used to connect the pumps
and the fluorescence spectrophotometer for the purpose of online measurement and
flow rate control. Quantum yield was measured by a fluorometer (Horiba NanoLog)
Micro-reactor design and set-up. The capillary micro-reactor synthesis system is
shown in Figure 4-1. Two piston pumps were used to feed the precursor solutions
prepared earlier into the capillary PTFE tubing (ID=500μm) and also the SS tubing with a
designed flow rate. The nucleation and reaction take place in the heat zone of the tubes
(360μL for PTFE tubing; 250 μL for 1/32” SS tube; 2.4mL for 1/16” SS tube), which is
coiled and set in an oil bath with a constant temperature. Next, the solution was
immediately cooled though another coiled PTFE tubing (360μL) in the recycled water
bath to quench the reaction and also to avoid any potential damage inside the back
pressure regulator (9bar - 13bar) due to high temperatures. The fluorescence
spectrophotometer was connected in an inline manner through the use of a flow cuvette
(440uL) so that it could provide real-time data for further analysis.
Results and discussion
The reactor presented in Figure 4-1 enables an adjustable isothermal reaction
condition, which minimizes the emission wavelength fluctuation due to temperature
differences. Different residence times and precursor ratios can be achieved by changing
the flow rate of the precursors. By carefully tuning these parameters, the FSS is able to
produce the high quantum yield QDs with the λmax ranging from 500nm to 800nm. In the
following section, the results from the hydrothermal synthesis of CdTe QD at different
90
reaction conditions under a steady-state operation of the capillary micro-reactor system
are discussed.
Effect of reagent concentration on QDs
The traditional precursor ratio of [Cd]:[ligand]:[Te] was chosen as 1:2.4:0.5 for the
consumption of the tellurium using excessive cadmium115. However, the concentration of
the reagents is believed to have a direct influence on both QD’s growth and its PL
property, especially in water-based QDs116. The effects of reagent concentrations on
QD’s precise residence time and their PL property was explored by comparing QDs that
are emitted at 557nm made at different reagent concentrations, which is difficult to
achieve with batch synthesis.
Our experimental results indicated that the amounts of [Cd2+] as well as [NAC] can
strongly influence the PL properties of hydrothermally prepared CdTe QDs. As shown in
Figure 4-2, the QY of CdTe QD gradually increased from 20% and stabilized at 45% as
[Cd2+] increased from 2.5mM to 12.5mM at 170°C. Meanwhile, the increase of [NAC] has
an opposite effect on the QY, reducing the QY from 46% down to 20% as the [NAC]
increased from 12.5mM to 30mM. The effect of reagent concentrations on reaction times
required for 577nm QDs were contrary to that of QYs. The residence time was reduced
from 7.6s (2.5mM [Cd2+]) and 11s (30mM [NAC]) down to 3s for both 12.5mM [Cd2+] and
~15mM [NAC].
The variation of the QY results from the surface structure of QDs. Surface defects
which are controlled by the dynamic growth process of the QDs, are believed to be one
of the main reasons for low QY. With the equilibrium of dissolution and growth at the QD
surface, defects can be repaired by the Ostwald ripening phenomenon117. Bao et al.
revealed that QY can be gradually enhanced as time goes by without any treatment.118
91
An alternative way to reduce the surface defect is by forming a thin tellurium-poor layer
that covers the original defects, such as with a proper layer of organic ligands. The ligand
molecules can interact with the QDs’ surface via sulfur atoms and thereby supply sulfur
atoms into the crystal structure119. Borchert et al. showed that the highly-luminescent
CdTe QDs possess fewer tellurium atoms at the surface than QDs with low
luminescence.120 In our case, as the ratio of [Cd2+]:[Te2-] increases, the QD surface may
be enriched in cadmium atoms, thus providing more sites for ligand attachment. When
the QD surface becomes full of cadmium atoms, further increasing the [Cd2+]:[Te2-] ratio
cannot drive ligand attachment. Therefore, the QY growth trend slowed down as the ratio
increased from 10mM to 12.5mM.
On the other hand, the reduction of the residence time that is required for the same
emission wavelength QD should be considered through the kinetic process. Generally,
the nucleation and the growth speed are controlled by the concentration of the free
precursor, such as any free cadmium ions. More free cadmium ions lead to a faster
reaction. It has been demonstrated by Farideh et al.114 that cadmium can form
coordination compounds with NAC at near-neutral pH as well as a high pH value, which
is also the key to prepare cadmium precursors under an alkaline condition. They
illustrate that the free, reactive cadmium ions are only available at a very low
concentration from the reversible reaction of Cd(II) complexes. By either increasing the
[Cd2+] or decreasing [NAC], more free cadmium ions will be released into the solution.
This provides a potential site for nucleation and acceleration of crystal growth. Similar
results were observed in previous publications113,121.
92
Effect of reaction temperature on QDs
We explored the effect of the reaction temperature within a range of 115°C to
185°C. The residence time was constantly set at 5.8s to provide enough time for QD
growth at low temperatures. Figure 4-3(a) shows the normalized emission wavelength
spectrum, indicating that the emission wavelength and therefore the size of QDs
accelerated with the rising temperature. The relationship between temperature and λmax
is clearer in Figure 4-3(b), where λmax can be calculated with a given temperature using
the following equation:
y = 0.0309𝑥2 − 6.6238𝑥 + 866.64 (4-1)
Every degree rise in the temperature results in a λmax change of 1.6nm on average,
which is small enough for the precise control of the emission wavelength. The polynomial
trend line illustrates that the QD’s growth speed gets faster when the reaction
temperature is raised, which is consistent with other studies122.
Picking a suitable reaction temperature is a strategy depending on the target of the
process. A higher temperature reduces the reaction time and thus increases the yield
especially for near-infrared QDs. However, a low temperature is preferred since a slow
growth speed is believed to help reduce the surface defects through Ostwald ripening117.
Although lower reaction temperatures were reported118, the QDs synthesized at 115°C in
the FSS approach the minimum λmax ever reported123 by the hydrothermal method. On
the other hand, the maximum temperature in our apparatus is limited because of the
limitation of the back pressure regulator to 10 bar. In practice, 170°C to 180°C
temperatures were chosen by considering both reaction speed and PL properties. We
have observed 40-60% quantum yields from the QDs with a λmax between 510nm and
730nm synthesized in this temperature range.
93
Effect of residence time
The effect of the residence time is demonstrated in Figure 4-4a with a reaction
temperature of 180°C. Longer residence times resulted in longer wavelength emissions.
The λmax of CdTe QD ranges from 509nm to 641nm attributable to 25Å - 40Å particles124.
Figure 4-4b shows that the residence time has a logarithmic relationship with the
emission wavelength as defined in the following equation:
y = 78.283 ln(x) + 510.6 (4-2)
Moreover, by converting the emission wavelength calculated in Equation 4-2 using
the below equation, the band gap of the QDs can be calculated:
E =hc
λ (4-3)
Sotirios et al.125 calculated that the effective band gap energies for CdTe QD were
a function of the dot radius. Therefore, the residence time can be directly related to the
average radius of the QDs (Figure 4-5a). The linear plot of the cube of the average QD
radius (Figure 4-5b) is consistent with the Ostwald ripening growth mechanism, which
supports the hypothesis that the growth mechanism of QDs is mainly controlled by the
Ostwald ripening117.
Compared to conventional batch methods, the reaction time required for the same
emitted QD at the same reaction temperature is dramatically lower using the FSS.
According to previous reports, several studies required at least 30 minutes to reach the
minimum emission wavelength126. The hot-injection method reduced the reaction time to
the 2 minute scale94, which is still 10 times longer and is difficult to scale up. The
chemical aerosol flow method provides an alternative approach giving a comparable
94
reaction time at 200°C - 270°C, although further treatments such as coating and/or
bio-conjunction are limited.122
The reason for the reaction time variation may be due to the different thermal
conductivities between each system. For example, the traditional batch methods involve
the heating of both stainless steel jackets and PTFE vessels via an oven so that the
heating ratio is mainly controlled by the thermal conductivity of the air. While applying the
hot-injection method, the container and part of the solution are already heated. Thus, the
reaction time can be reduced substantially. The FSS takes the advantage of the
relatively high thermal conductivity of both the PTFE and SS tubes and also the thin
cross-section of the tubes, where the liquid can approach its target temperature almost
instantaneously so that no time is wasted on heating.
Since the flow rate is adjustable with a 0.02mL/min minimum step change, the
theoretical resolution for QD’s λmax is reduced to 0.5nm, which is the best resolution
achieved. A straightforward example is given in Figure 4-6 to demonstrate the ability of
tuning the emission wavelength of the aqueous CdTe QDs.
XRD characterization of CdTe QDs
XRD patterns of CdTe QDs obtained by flow synthesis at different residence times
are shown in Figure 4-7. The green QD pattern is consistent with the bulk CdTe
materials, which belongs to cubic (zinc blende) structure. However, the other patterns
from the yellow and red QDs reflect that the crystal structure of QDs shifted from the
cubic CdTe towards the cubic CdS as the residence time increased. Similar results in the
XRD pattern have been reported in the synthesis of CdTe using thiol-group ligand.89 This
phenomenon is consistent with the theory that a sulfur shell is generated from the thiol
group of a partially hydrolyzed ligand. It can be prevented by using DMF as the solvent127
95
or performing synthesis at comparatively low pH (5.6 - 5.9) in the presence of
2-mercaptoethylamine as the stabilizer89. This limits the incorporation of sulfur into the
growing CdTe QDs.
TEM characterization of CdTe QDs synthesized at 180°C
The CdTe QDs synthesized at 180°C were characterized by TEM as shown in
Figure 4-8. The distinguishable lattice planes reveal the high crystalline of QDs. It also
indicates that CdTe QDs produced by FSS had a narrow particle size distribution and
were well dispersed in the solution. The average size was around 2nm from the TEM
picture and was also consistent with the estimated mean particle size by using the
effective mass approximation.128
Thermal Control
As the understanding of the QD reaction goes deeper, the stability of the
temperature is discovered to be the key parameter for precise control of QD’s emission
wavelength because of the temperature sensitivity of the reaction. As discussed in the
previous chapter, a simple oil bath made by using a glass container and a hot plate with
1 - 2°C variation cannot support the requirement of manufacturing a 1nm resolution QD,
A 1°C temperature change contributes to a 1.6 nm EW change. Figure 4-14 gives an
example of how λmax is affected by the oscillation of the reaction temperature.
The new heating system was designed and composed with a heating mantel, PTFE
and PI board, external stirrer, a peristaltic pump, and a thermal couple for the precise
control of the reaction temperature. As shown in Figure 4-9, the temperature inside the
new heating system is determined by the interaction of the heating mantel and the
cooling water while the external stirrer ensures the adequate heat exchange. The
heating mantel is able to heat the oil bath at 4.5°C/min at its full power. The cooling water
96
is pumped by a digital peristaltic pump whose flow rate can be adjusted by Labview
software. Moreover, due to the excellent thermal conductivity of copper tubing, the
cooling water could remove heat by evaporation. In the following test, the flow rate of the
cooling water is limited so that it could reach the same cooling speed as heating
(-4.5°C/min). The SS reaction tube sits in the innermost space, which is adjacent to a
thermal couple in order to get the precise detection of the reaction temperature.
The new device was tested by three control algorithms for the purpose of a fast and
accurate control algorithm: the P controller, PI controller and the on-off controller. The
Good Gain method was applied to find out the suitable Kp for the P controller without the
need for specific knowledge about the new heating system. This method involves a
series of adjustments in altering the set point, with the Kp value increasing from 0 or 1
until the system response is acceptable. In a brief test, Kp was set as 20, 40, and 80,
respectively, as shown from Figure 4-10 to Figure 4-12. Both the phenomenon of
overshooting and oscillation were observed in all three cases, which is not desirable.
A detailed mathematical model was built for the PI controller in order for a smoother
temperature alteration. Figure 4-13 indicates the step change data for the manipulated
variable (MV) decrease from 40 down to 35, where K, τ, and D were calculated from the
tangent line:
𝐾 =∆𝑌
∆𝑈=
(170°C−141°C)
40− 5= 5.8° ,
𝜏 = 115𝑚𝑖𝑛, 𝐷 = 4𝑚𝑖𝑛,
In Z-N method, the calculation of the control parameters is shown below:
𝐺𝑢(𝑠) = 5.8
115𝑠+1 𝑒−4𝑠 (4-4)
97
From the Bode plot of Gu(s) (obtained from program AAS_ECH4323Noline.exe) at
phase lag φ= -180º, the log AR = -0.9 and the log Wco = -0.4.
𝐾𝑢 = 1
𝐴𝑅 = 7.9 (4-5)
𝑃𝑢 = 2𝜋
𝑊 = 15.78 (4-6)
𝐾𝑐 = 𝐾𝑢
2.2 = 3.6 (4-7)
𝜏𝐼 = 𝑃𝑢
1.2 = 13.15 (4-8)
In the control algorithm, the following rules were set up in case the temperature was
outside a reasonable control range:
1. If error>10, MV = 100.
2. If -10<error<10, 𝑀 = 𝑀 (𝑡 − 1) + 𝐾 (𝑒(𝑡) − 𝑒(𝑡 − 1) + ∆𝑡/𝜏𝐼𝑒(𝑡)). 3. If e<-10, MV=0, cooling water on.
Figure 4-15 illustrates the performance of the PI controller with the above setting at
different set points. Overshooting to at least 5°C is still observed as the temperature
increases, which is even worse compared to the P controller. A complex oscillation is
also observed for the set point: a 5 second clutter cycle with a 1.7°C amplitude, a smooth
14 second clutter cycle with a 6°C amplitude, and a 1 second clutter cycle with a 1.3°C
amplitude. The clutter cycle was obtained by the control rules, which forced the system
to cool down so that the original smooth cycle with larger amplitude was broken down
into several pieces. Lower Kc values were tested since it helps to reduce the oscillation,
theoretically. Figure 4-16 and Figure 4-17 show the performance of the heating system
with 1/2 Kc (1.8) and 1/4 Kc (0.9) with 𝜏𝐼 unaltered. Similar oscillations were observed
with similar wavelengths and amplitudes, which imply that more adjustment is needed on
the model. The large overshooting and subsequent oscillation may be due to the heat
capacity of the heating mantel and its temperature difference from the oil bath. As the
temperature increases, the heating mantel stays at a temperature much higher than the
98
set point and continues heating the oil bath even when powered off for a long time. On
the other hand, the oil bath can be rapidly cooled and thus can confuse the control
algorithm by unnecessarily increasing MV. Reducing the oscillation is possible by further
calibrating both Kc and 𝜏𝐼, but it is very likely to depart from the goal of fast tuning
temperature.
Lastly, the on-off controller was tested since it is a simple yet effective method for
these conditions. The fully open (MV=100) and fully closed (MV=-100) control algorithm
ensured that the fastest response to the set point change happened around the set point
despite a dead band of 0.5°C. This was done to reduce any potential overshooting due
to the large heat capacity of the heating system. The MV was set to 20% inside the
dead band so that the temperature could be fine-tuned and oscillation constrained to a
small range. In general, the following rules were applied for the on-off controller.
1. If error>0.5, MV=100 2. If 0.5>error>0, MV=20 3. If 0>error>-0.5, MV=-20 4. If -0.5>error, MV=-100
Figure 4-18(a) shows the performance of the on-off controller when increasing the
set point from 160°C to 170°C. With the heating mantel set at full power until the
temperature reached 169.5°C, an unavoidable overshoot of 1°C was observed for
around 60 seconds. This is similar to the P controller but comparatively better than the PI
controller. The following oscillation was expected due to the nature of the on-off
controller, yet in a much smaller temperature range ( 0.2°C). Despite the smaller
temperature deviation, the drawback of the on-off system is reflected by its high
frequency oscillation of MV (changed working state randomly from 1 to 9 seconds),
which would accelerate the wear and the tear of the system. Nevertheless, the on-off
99
controller did improve the precision of the QD system as shown in Figure 4-18(b). At
constant temperature (170°C) and flow rate (1.5mL/min per pump), the standard
deviation of the emission wavelength produced by the FSS was reduced to 0.69nm at
the mean value of 581nm. Such precision is already above the resolution of the
fluorometer and is considered to be sufficient for further application.
Process control
The size of the QDs and their PL properties can be adjusted by varying the
temperature as well as changing the residence time. It is apparent that temperature
cannot be rapidly and precisely controlled compared with tuning the pump flow rate.
Meanwhile, keeping temperature inside a suitable range is important: a high temperature
is preferred because of the relevant fast reaction speed, yet the temperature cannot be
too high to overload the pressure limitation of the system. Thus, the establishment of the
process control system was based on setting an appropriate temperature and tuning the
flow rate (i.e. residence time).
The following section introduces a first order plus time delay (FOPTD) model
structure for analyzing the emission wavelength of QDs produced by the FSS that is
based on step response data. Two different tuning methods were used to calculate the
control parameters for the closed loop system with the feedback controller. Those two
tuning methods are the Cohen-Coon and the Ziegler-Nichols method. Although the
reaction conditions were completely different from the silica model, as discussed in
Chapter 3, the same procedure was applied when building the feedback control model.
Graphical process identification from step responses
The FOPTD process is a proper assumption for the QD FSS because the typical
FOPTD feature can be observed from its step change response curve: a sigmoidal
100
response with no oscillations or inverse responses. A graphical identification procedure
was made to identify the three parameters K, τ and D for the FOPTD equation:
𝑦′(𝑡) = {0, 𝑡 < 𝐷
𝑘∆𝑢 [1 − 𝑒−𝑡−𝐷
𝜏 ] , 𝑡 ≥ 𝐷 (4-9)
Figure 4-20 shows the step responses of the FSS in different flow rate ranges. A
simple step change experiment is not enough to cover the whole working flow range
because the curve of the flow rate (MV) to emission wavelength (PV) might be
asymptotical as shown in Figure 4-19. This is based on the assumption that the
wavelength (QD particle size) is proportional to the reaction time. Therefore, four step
responses were made at 0.5mL/min, 1.5mL/min, 2.5mL/min and 3.5mL/min. This
provides coverage of most of the flow rate range as shown in Figure 4-20. A gradual
increase of the step change of 0.1mL/min at 0.5mL/min and 1.5mL/min, 0.2mL/min at
2.5mL/min, and 0.3mL/min at 3.5mL/min were done to ensure the detectable differences.
The Ks for each step change were calculated from the equation 𝐾 =∆𝑌
∆𝑈 and are shown
below:
𝐾0.5 =∆𝑌
∆𝑈=642−651
0.1= −90𝑛𝑚 ∙ 𝑚𝑖𝑛 𝑚𝑙⁄ (4-10)
𝐾1.5 =∆𝑌
∆𝑈=577−580.5
0.1= −35.7𝑛𝑚 ∙ 𝑚𝑖𝑛 𝑚𝑙⁄ (4-11)
𝐾2.5 =∆𝑌
∆𝑈=544−550.
0.2= −31.7𝑛𝑚 ∙ 𝑚𝑖𝑛 𝑚𝑙⁄ (4-12)
𝐾 .5 =∆𝑌
∆𝑈=519.5−525
0. = −18.3𝑛𝑚 ∙ 𝑚𝑖𝑛 𝑚𝑙⁄ (4-13)
By simulating the power trend line, where y represents K and x is the flow rate
(Figure 4-21a), we get
𝑦 = −54.404𝑥−0.744 (4-14)
Although K can be directly calculated from the step response curves, τ and D are
harder to determine because of the limitation of the discrete wavelength measurements.
101
Table 4-1 indicates the results of τ and D that are determined graphically from the step
response data, where τ is distributed around 0.4min and D becomes negative at a higher
flow rate. To explain the discrepancy in these parameters, a possible reason is that this
could have resulted from the higher interval of each wavelength measurement compared
to the time cost for each step change so that the real step response curves are
elongated.
Alternative methods were applied to simulate and estimate of τ and D using the
residence time. According to the definition of dead time, D should be equal to the
residence time that measures from the pump, where step change occurs, to the
fluorometer, where the wavelength change is observed. Therefore, the following
equation holds:
𝐷 = /𝑣 (4-15)
where V is the absolute volume of the FSS and v is the flow rate. Considering the
limitation of the measurement interval, D is restricted above 0.5min so that the tuning
program would not be improperly affected by the delayed responses. Figure 4-21c
shows the calculated D, which is discontinuous at the flow rate of 1.22mL/min and gives
a 0.5min dead time.
τ can be mathematically represented by the following steps. First, by denoting t with
𝜏 + 𝐷, the FOPTD function becomes:
𝑦′(𝜏 + 𝐷) = 𝐾∆𝑢(1 − 𝑒−1) = 0.63𝐾∆𝑢 (4-16)
𝜏 = 𝑡0.6 𝐾∆𝑢 − 𝐷 (4-17)
Next, assuming that the gradient of the response curve is proportional to the
residence time, we have:
102
𝑡0.6 𝐾∆𝑢 = 0.63𝑉
𝑣 (4-18)
where c is an unidentified coefficient.
Since 𝐷 =𝑉
𝑣, Equation 4-17 can be rewritten as:
𝜏 = 0.63𝑉
𝑣 −
𝑉
𝑣= 0.63
𝑉
𝑣( −
1
0.6 ) = 0.63
𝑉
𝑣 =
1.22𝑚𝑙
𝑥 𝑚𝑙/𝑚𝑖𝑛× 0.63 (4-19)
Three values of 1, 0.5 and 0.25, were chosen to optimize the coefficient c (Figure
4-21b), which is described in detail in the following chapter.
Cohen-Coon tuning method
The Cohen-Coon tuning method was applied with an uncertain τ due to the control
parameters’ direct and programmatically-convenient mathematical relationship (3-14 and
3-15). Figure 4-22 indicates that the effect of τ on Kc and τI was the same, where the
absolute value of Kc and τI increased with an increased τ. While the increase of Kc
would tune the system faster, a larger τI could reduce the stability of the system.
Figure 4-23 shows the results of the feedback control on the QD FSS by the PI
controller and the Cohen-Coon tuning parameter, as displayed in Figure 4-22 at the
0.5min interval. All experiments started with a 1.5mL/min flow rate, which produced the
650nm emission wavelength QD with a targeted 570nm emission wavelength for
comparison. Overshoots were observed in all three experiments and were antiparallel to
the Kc’s effect, which is contrary to observing an expected parallel and stronger
overshoot for a higher Kc. The maximum flow rate gradually decreased from c=0.25
(absolute value of Kc is lowest) to c=1 (absolute value of Kc is highest).
Further exploration of the control algorithm provided more details about the
overshoots. The discretized PI controller uses the following equation:
103
𝑀 (𝑡) = 𝑀 (𝑡 − 1) + 𝐾 [𝑒(𝑡) − 𝑒(𝑡 − 1) +∆𝑡
𝜏𝐼𝑒(𝑡)] (4-20)
where 𝑒(𝑡) − 𝑒(𝑡 − 1) represents the proportion of Kc and ∆𝑡
𝜏𝐼𝑒(𝑡) represents the
proportion of 𝜏𝐼.
Figure 4-24 shows the variation of both parts in the tuning process, which indicates
that the tuning process was mainly controlled by ∆𝑡
𝜏𝐼𝑒(𝑡) as the Kc proportion increases
the oscillation amplitude. Since the overshoots were contributed by ∆𝑡
𝜏𝐼𝑒(𝑡), the increase
of τI from c=0.25 to c=1 resulted in a decrease of the maximum for the overshoots.
Although the overshoot was restrained at c=1, the oscillation became unacceptable due
to the increasing Kc proportion and its interactivity with a delayed τI effect.
The best performance belongs to c=0.25, which has the best stability once the set
point is reached. It takes 7min to reach the set point with three attenuated peaks.
However, further decreasing the coefficient c will not speed up the tuning process
because the overshoot will be further amplified by the increasing τI and thus the
oscillation will be enlarged.
Ziegler–Nichols tuning method
An alternative, yet robust and popular method, the Ziegler-Nichols tuning method
was also tested in case it could provide a smoother tuning. The open loop transfer
function was determined by plugging the K, τ and D values gathered at the 0.1mL/min
flow rate mark from Figure 4-21 (c=0.5) into the below equation:
𝐺𝑢′(𝑠) =|𝐾|
𝜏𝑠+1𝑒−𝐷𝑠 (4-21)
The Bode Plots of Equation 4-21 were taken by the program named
FrespAsthaASS_ECH4323NoLine.exe and the log AR and log ω were recorded at
104
phase lag φ= -180º. By applying Equations 3-16, 3-17, 3-18 and 3-19, the complete
series of Kc and 𝜏𝐼 values covering the whole flow rate were calculated as shown in
Figure 4-25.
Both parameters can be broken into two parts at 1.22mL/min flow rate due to the
turning point of D. Kc can be represented by the following equations:
𝐾 (𝑥) = {0.0016𝑥2 − 0.011𝑥 − 0.0012, 𝑥 < 1.22
8 × 10−5𝑥2 − 0.0047𝑥 − 0.0066, 𝑥 ≥ 1.22 (4-22)
𝜏𝐼 can be represented by the following equation:
𝜏𝐼(𝑥) = {1.2873𝑥−1, 𝑥 < 1.22
0.0081𝑥2 − 0.0894𝑥 + 1.1336, 𝑥 ≥ 1.22 (4-23)
where x is the flow rate.
The Kc and 𝜏𝐼 were then plugged into Equation 4-20 for the tuning calculation.
Figure 4-26 shows the tuning result by the Ziegler-Nichols method at three different
set points. The tuning curves were smooth but slower compared to Cohen-Coon method,
taking about 10min to 14min to reach the set point. The parameter generated by the
Ziegler-Nichols method was much higher than that derived from the Cohen-Coon
method (1.5 - 3 times for |𝐾 | and 3.8 - 7.5 times for 𝜏𝐼), which indicates that the tuning
process was less affected by 𝜏𝐼. However, the analysis of the tuning equation (Figure
4-27) illustrates that the tuning mainly followed the trend of ∆𝑡
𝜏𝐼𝑒(𝑡).
Core-shell QD in FSS
The coating technique for CdTe/CdS QDs in the hydrothermal method relies on the
controlled reaction of S2- with Cd2+, where the S2- ion could come from either the NAC
ligand118,129 or Na2S130,131. While the ligands provide the S2- by self-degradation and
surface reactions that link themselves with the QDs, they cannot shift the wavelengths
105
more towards the region of red emission due to the functionality of the ligands. The
ligands maintain a level of stability that is insufficient to supply an adequate source of S2-
to coat the QDs. On the other hand, Na2S remains a sufficient and direct source of S2-
ions. The only disadvantage of Na2S comes from the CdS reaction, where the reaction
speed is so fast that a single crystal of CdS instead of CdS shell can form in the solution
unless the speed at which Na2S is added is highly limited.
A third and alternative method was developed during the course of this research to
supply the S2- ions in order to bypass the complication of the flow system when Na2S is
applied. This method combines the ability of precisely controlling the residence time (or
reaction time) and the nature of sodium thiosulfate, which will slowly degrade in the
acidic environment. The sodium thiosulfate decomposes at pH<7, as shown below:
𝑆2𝑂 2− + 2𝐻+⇔ 𝐻2𝑂 + 𝑆 ↓ +𝑆𝑂2 (4-24)
When mixed with the Cd2+ ion, three thiosulfate compounds can be formed
according to the concentration of 𝑆2𝑂 2−: 𝑆2𝑂 , [ (𝑆2𝑂 )2]
2− and [ (𝑆2𝑂 ) ]4−. All
three compounds degrade slowly under UV or acidic environments at room temperature,
but the two coordination compounds have a lower photostability132. The overall reaction
can be written as follows:
[ (𝑆2𝑂 )𝑥]2(𝑥−1)− + 𝐻2𝑂 → 𝑆 + 𝑆𝑂4
2− + (𝑥 − 1)𝑆2𝑂 2− + 2𝐻+ (4-25)
While it takes hours to form the CdS precipitates at room temperature, the reaction
is accelerated dramatically when the temperature is increased. Combining this with the
reaction time control from the FSS, the controlled CdS coating process becomes
possible.
106
Materials and method
The raw QD solution was prepared using the same method mentioned earlier in the
section describing the conversion of the batch method to the FSS method. Briefly, the Te
precursor and the Cd precursor were prepared as follows:
Te precursor: 125mg of TeCl4 was dissolved in 500mL of DI water by drop addition of 1M sodium hydroxide until the solution became clear. The solution was then sealed and bubbled by N2. After 30 min, 250mg of NaBH4 was added and the solution was heated to 80°C until it became colorless again.
Cd precursor: 2.292g of CdCl2 and 2.448g of NAC were dissolved with 500mL of DI water and the pH was adjusted to 9. The solution was ready after 30min of N2 bubbling.
Several QD raw solutions with different emission wavelengths were collected in
advance.
In the preliminary test by the batch method, 10mL of QD raw solution was mixed
with 0.1g of sodium thiosulfate. Afterwards, the pH of the solution was adjusted to 5
using 3.3wt% HCl. The resulting solution was sealed and then heated at 90°C to observe
the wavelength shift. During the heating, the samples were quickly taken from the glass
vial, quenched, and then measured for their fluorescence by the Hitachi F2000
spectrophotometer.
In the FSS, the reagents for the core-shell QD reaction were separated into three
parts and pumped respectively: the raw QD solution, which contains the unreacted Cd2+
ion and the core QD; the diluted HCl solution; and the sodium thiosulfate solution.
Following the reaction and the online fluorescence detection, the products were collected
with excess NaOH to quench the reaction. Furthermore, the products were centrifuged
and washed by DI water to remove the unreacted sodium thiosulfate. Precise emission
107
spectrums were measured offline by a Horiba Nanolog UV/NIR spectrophotometer,
which gives better accuracy at wavelengths above 650nm.
Results and Discussion
A preliminary test was done prior to tuning the reaction in the FSS. By recycling the
unreacted Cd2+ in the raw QD solution, the addition of any extra chemicals was limited to
diluted HCl solution and sodium thiosulfate. While the unreacted Cd2+ ion was
approximately 12mM, 0.1g of sodium thiosulfate supplied a 5:1 molar ratio to the Cd2+
ion so that all the remaining Cd2+ would be in the form of coordination compounds. The
presence of excess sodium thiosulfate could help accelerate the reaction in order to fit
the time requirement for the FSS. The control group was also induced by adjusting the
pH of the raw QD solution to 5 without adding any excess sodium thiosulfate. As shown
in Table 4-2, the control group has a 2nm absolute shift from the raw solution during the
heating, which may be caused by the pH changes133. On the other hand, the sample with
the sodium thiosulfate experienced a red shift of the emission wavelength at the speed
around 2.4nm/min and finally become agglomerated due to the overreaction. The
stationary emission wavelength of the control group proved that the growth of the QDs
stopped after it was produced by the FSS because all the free Te2- ions were blocked by
the dissolved O2 in the solution. Therefore, the red shift of the emission wavelength only
results from the degradation of sodium thiosulfate.
Further explorations were carried out on the FSS in order to stop the reaction at the
target wavelength. Compared to the batch test, a more accelerated reaction was
preferred to fit the residence time range of the FSS. Table 4-3 shows that the red shift of
the emission wavelength is affected by reaction time, temperature, molar ratio, and the
pH. The red shift of the emission wavelength enlarged as the residence time increased.
108
However, the tuning of the residence time shows the limitation of the maximum red shift
at about 70nm (Figure 4-28) before precipitation. This is consistent with other literature
involving hydrothermal synthesized core-shell CdTe/Cds QDs134. A further shift was
limited by the small difference between the conduction bands of the CdTe core and the
CdS shell (about 0.1eV)131. On the other hand, the organometallic method gives a
maximum red shift of 120nm for the CdTe/CdS system135. The difference in the
maximum red shift between these two methods may be attributed to the increase of the
CdS concentration gradient towards the surface in the hydrothermal method136-138.
The results also indicated that the reaction temperature and the pH are two critical
parameters. The increase in temperature from 90°C to 170°C reduced the reaction time
from tens of minutes (batch) to seconds. Although high temperatures such as 160°C
dramatically accelerate the reaction, it is difficult to approach the maximum red-shift
because the boundary residence time between the coating and the precipitation are
quite blurred. Conversely, the lower temperature with a much milder reaction provides a
sufficiently large time zone for tuning without the risk of tube blockage. The pH also has
the same function as temperature in that it catalyzes the reaction. However, it is more
difficult to monitor during the process since degradation of sodium thiosulfate produces
H+ ions throughout the reaction.
109
Table 4-1. Calculated Step change data for K τ D
Flow rate, mL/min
K τ, min
D, min
0.5 90 0.447761 1.302238806 1.5 40 0.402985 0.342014925
2.5 30 0.447761 0.322238806
3.5 20 0.223881 -11.5238806
Table 4-2. Preliminary batch test of coating effect by sodium thiosulfate
Time, min Emission wavelength, nm (Control group)
Emission wavelength, nm (Core-shell group)
0 624 624 10 626 641 18 626 668 26 626 Agglomerate
Table 4-3. Residence time and temperature effect on CdS coating
Residence time, s
Flow rate ratio Cd2+: 2 2−
Temperature, °C
Emission wavelength, nm
72 2:1:1 1:5 120 682
96 2:1:1 1:5 120 693
144 2:1:1 1:5 120 697
144 2:1:1 1:5 130 precipitation
7.5 2:1:1 1:5 150 670
7.5 2:1:1 1:5 160 681
9.375 2:1:1 1:5 160 686
10 2:1:1 1:5 160 688
15 2:1:1 1:5 160 698
30 2:1:1 1:5 160 precipitation
15 2:1:1 1:5 170 715
73 2:0.5:0.5 1:2.5 130 663
72 1:0.25:0.75 1:2.5 130 722
96 1:0.25:0.75 1:2.5 130 precipitation
110
Figure 4-1. Flow system for QD synthesis. 1. piston pump; 2. oil/heating bath; 3. condenser/cooling bath; 4. back pressure regulator; 5. fluorometer; 6. sample collector; 7. data acquisition (and proposed feedback control).
111
Figure 4-2. Concentration effect of [Cd2+] and [NAC] on QD’s reaction speed and QY. All the QD were adjusted to the same emission wavelength for comparison. The basic condition is [Cd2+] = 12.5mM, [Te2-] = 0.5mM, [NAC] = 15mM. Reaction temperature was set to be 170°C.
0%
10%
20%
30%
40%
50%
60%
0
1
2
3
4
5
6
7
8
9
10
12.5 10 7.5 5 2.5
Qu
an
tum
yie
ld
Recati
on
tim
e,
seco
nd
[Cd2+], mM
Residence time QY%
0%
10%
20%
30%
40%
50%
60%
0
2
4
6
8
10
12
14
12.5 16 19.5 23 26.5 30
Qta
ntu
m y
ield
Reacti
on
tim
e,
seco
nd
[NAC], mM
RT QY%
112
Figure 4-3. (a) Normalized emission spectra for QDs synthesized at different temperatures, with a constant residence time of 5.8s showing the tunability of emission wavelength (excitation 350 nm). Temperature were 115°C, 125°C, 135°C, 145°C, 155°C, 165°C, 175°C, 185°C from left to right respectively. (b) Relationship between λmax and temperature for QDs synthesized at different temperatures.
0
0.2
0.4
0.6
0.8
1
450 475 500 525 550 575 600 625 650 675
A.U
.
Wavelength, nm
115℃
125℃
135℃
145℃
155℃
165℃
175℃
185℃
y = 0.014x2 - 2.5857x + 629.91
500
525
550
575
600
625
650
100 150 200
λm
ax, n
m
Temperature, °C
113
Figure 4-4. (a) Normalized emission spectra for QDs synthesized with different residence time at the constant temperature of 170°C (excitation 350 nm). (b) Relationship between λmax and residence time for aqueous QDs synthesized with different residence times.
0
0.2
0.4
0.6
0.8
1
450 475 500 525 550 575 600 625 650 675 700
A.U
.
Wavelength, nm
5.88s
3.92s
2.35s
2.35s
1.96s
1.63s
1.37s
1.18s
0.98s
y = 75.283ln(x) + 510.6
500
520
540
560
580
600
620
640
0 1 2 3 4 5 6 7
λm
ax, n
m
Residence time, second
114
Figure 4-5. (a) The calculated QD average radius as the function of residence time. (b)
the plot of cube of average QD radius as a function of residence time.
0
0.5
1
1.5
2
2.5
3
0 1 2 3 4 5 6 7
QD
av
era
ge r
ad
ius,
nm
Residence time, second
0
5
10
15
20
25
0 1 2 3 4 5 6 7
<R
>3
Residence time, second
115
Figure 4-6. Images of QDs prepared via continuous flow under room light (left) and under
UV excitation (right). QDs were synthesized at 180 °C by decreasing the residence time at constant intervals to obtain emission wavelengths ranging from 530 to 730 nm.
Visible illumination UV illumination
116
Figure 4-7. XRD patterns of the CdTe QD by flow synthesis at different residence time.
CdS
CdTe
Red
Yellow
Green
117
Figure 4-8. TEM image of QD produced under 180°C with a residence time of 3.5 seconds.
5nm
118
Figure 4-9. Sketch of heating system
Thermal couple External stir
SS tube Cooling water PTFE stand
119
Figure 4-10. The temperature response of new heating system with increase set point. (
Kp=20, P controller)
0
20
40
60
80
100
120
140
0 500 1000 1500 2000 2500 3000 3500
Tem
pera
ture
,°C
Time, second
125
127
129
131
133
135
0 500 1000 1500 2000 2500 3000 3500
Tem
pera
ture
,°C
Time, second
-80
-60
-40
-20
0
20
40
60
80
100
120
0 500 1000 1500 2000 2500 3000 3500
Man
ipu
late
d V
ari
ab
le(M
V)
Time, second
120
Figure 4-11. The temperature response of new heating system with increase set point. (
Kp=40, P controller)
125
130
135
140
145
150
0 200 400 600 800 1000 1200 1400 1600 1800
Tem
pera
ture
,°C
Time, second
142
143
144
145
146
147
148
0 200 400 600 800 1000 1200 1400 1600 1800
Tem
pera
ture
,°C
Time, second
-100
-50
0
50
100
150
0 200 400 600 800 1000 1200 1400 1600 1800
Man
ipu
late
d V
ari
ab
le(M
V)
Time, second
121
Figure 4-12. The temperature response of new heating system with decrease set point.
(Kp=80, P controller)
170
175
180
185
190
195
200
205
0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400
Tem
pera
ture
,°C
Time(second)
199
199.5
200
200.5
201
600 800 1000 1200 1400 1600 1800 2000 2200 2400
Tem
pera
ture
, °C
Time(second)
-110
-60
-10
40
90
0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400
Man
ipu
late
d V
ari
ab
le(M
V)
Time(second)
122
Figure 4-13. Step change data for heating system from MV 40 to 35
140
145
150
155
160
165
170
175
0 50 100 150 200 250 300 350
Tem
pera
ture
, °C
Time, minute
123
Figure 4-14. QD emission wavelength disturbed by temperature deviation
611
612
613
614
615
616
617
618
0 200 400 600 800 1000 1200 1400 1600
Em
issio
n w
av
ele
ng
th, n
m
Time, second
169
169.5
170
170.5
171
0 200 400 600 800 1000 1200 1400 1600
Tem
pera
ture
, °C
Time, second
124
Figure 4-15. Performance of heating system with PI controller (Kc= 3.6, τI=13.15, set point at 120 / 135 / 145)
115
117
119
121
123
125
0 10 20 30 40 50 60 70
Tem
pera
ture
, °C
Time,second
-150
-100
-50
0
50
100
150
0 10 20 30 40 50 60 70
MV
Time,second
125
Figure 4-15. Continued
115
120
125
130
135
140
145
0 20 40 60 80 100 120 140
Tem
pera
ture
,°C
Time,second
0
20
40
60
80
100
120
0 20 40 60 80 100 120 140
MV
Time,second
126
Figure 4-15. Continued
140
142
144
146
148
150
0 10 20 30 40 50 60 70
Te
mp
era
ture
,°C
Time,second
-120
-100
-80
-60
-40
-20
0
0 10 20 30 40 50 60 70
MV
Time,second
127
Figure 4-16. Performance of heating system with PI controller (Kc= 1.8, τI=13.15, set point at 120)
115
117
119
121
123
125
0 20 40 60 80 100 120 140
Te
mp
era
ture
,°C
Time,min
-150
-100
-50
0
50
100
150
0 20 40 60 80 100 120 140
MV
Time,min
128
Figure 4-17. Performance of heating system with PI controller (Kc= 0.9, τI=13.15, set point at 130)
115
120
125
130
135
0 50 100 150 200 250
Te
mp
era
ture
,°C
Time,min
-150
-100
-50
0
50
100
150
0 50 100 150 200 250
MV
Time,min
129
Figure 4-18. (a)The performance of heating system with on-off controller and (b)its effect
on stabilizing the QD emission wavelength.
169
169.2
169.4
169.6
169.8
170
170.2
170.4
170.6
170.8
171
0 200 400 600 800 1000 1200 1400
Tem
pera
ture
, °C
Time, second
-150
-100
-50
0
50
100
150
0 200 400 600 800 1000 1200 1400
MV
Time, second
130
Figure 4-18. Continued.
169.7
169.8
169.9
170
170.1
170.2
0 500 1000 1500 2000 2500 3000
Tem
pera
ture
,°C
Time,s
-40
-20
0
20
40
60
80
100
120
0 500 1000 1500 2000 2500 3000
MV
Time,s
578
579
580
581
582
583
584
500 1000 1500 2000 2500 3000 3500
Em
issio
n p
eak,
nm
Time,second
131
Figure 4-19. The potential relationship between flow rate, reaction time and emission
wavelength
Reacti
on
tim
e,
seco
nd
Flo
w r
ate
, m
l/m
in
Emission wavelength, nm
132
Figure 4-20. Step change from 0.5 to 0.6mL/min, 1.5 to 1.6 mL/min, 2.5 to 2.7 mL/min,
3.5 to 3.8 mL/min
640641642643644645646647648649650651652653654
0 1 2 3 4 5 6 7 8
Em
issio
n w
av
ele
ng
th, n
m
Time, minute
576
577
578
579
580
581
582
583
0 1 2 3 4 5 6 7 8
Wav
ele
ng
th, n
m
Time, minute
133
Figure 4-20. Continued
542
543
544
545
546
547
548
549
550
551
552
553
554
0 1 2 3 4 5 6 7 8
Wav
ele
ng
th, n
m
Time, minute
518
519
520
521
522
523
524
525
526
0 1 2 3 4 5 6
Wav
ele
ng
th, n
m
Time, minute
134
Figure 4-21. K,τ,D calculated and simulated from step change data.
-140
-120
-100
-80
-60
-40
-20
0
0 1 2 3 4 5 6 7
K
flow rate,ml/min
y = -54.404x-0.744
0
0.2
0.4
0.6
0.8
1
1.2
1.4
0 1 2 3 4 5 6 7
Tau
Flow rate, ml/min
Tau1
tau0.5
Tau0.25
0
0.5
1
1.5
2
2.5
0 1 2 3 4 5 6 7
D
Flow rate, ml/min
135
Figure 4-22. C-C method Kc and 𝜏𝐼.
-0.015
-0.01
-0.005
0
0 1 2 3 4 5 6 7
Kc
Flow rate,ml/min
COHEN-COON method Kc
tau1
Tau0.5
tau0.25
0
0.5
1
1.5
2
0 1 2 3 4 5 6 7
Ti
Flow rate,ml/min
COHEN-COON method Ti
tau1
tau0.5
tau0.25
136
Figure 4-23. C-C method tuning (Feedback control for Stainless steel tubing with c=(a)1,
(b) 0.5, (c) 0.25).
540
550
560
570
580
590
600
610
620
630
640
650
660
670
0 2 4 6 8 10 12
wav
ele
ng
th, n
m
Time, min
0
1
2
3
4
5
6
0 2 4 6 8 10 12
To
tal fl
ow
rate
, m
l/m
in
Time, min
137
Figure 4-23. Continued.
540
560
580
600
620
640
660
680
0 2 4 6 8 10 12 14 16
wav
ele
ng
th, n
m
Time, min
0
1
2
3
4
5
6
0 2 4 6 8 10 12 14 16
To
tal fl
ow
rate
, m
l/m
in
Time, min
138
Figure 4-23. Continued.
540
560
580
600
620
640
660
680
0 2 4 6 8 10 12 14
wav
ele
ng
th, n
m
Time, min
0
1
2
3
4
5
6
0 2 4 6 8 10 12 14
To
tal fl
ow
rate
, m
l/m
in
Time, min
139
Figure 4-24. The weight of Kc and τI in tuning program for c=0.25(a), 0.5(b) and 1.0 (c).
-300
-250
-200
-150
-100
-50
0
50
100
150
0 2 4 6 8 10 12 14
Time, min
(e-et-1)
dt/taui*et
e(t)-e(t-1)+dt/Taui*e(t)
-200
-150
-100
-50
0
50
100
0 2 4 6 8 10 12 14
Time, min
e(t)-e(t-1)
dt/Taui*e(t)
e(t)-e(t-1)+dt/Taui*e(t)
140
Figure 4-24. Continued.
-120
-100
-80
-60
-40
-20
0
20
40
60
80
0 2 4 6 8 10
Time, minute
e(t)-e(t-1)
dt/Taui*e(t)
e(t)-e(t-1)+dt/Taui*e(t)
141
Figure 4-25. Z-N method Kc 𝜏𝐼 .
y = 0.0016x2 - 0.011x - 0.0012
y = 8E-05x2 - 0.0047x - 0.0066
-0.04
-0.035
-0.03
-0.025
-0.02
-0.015
-0.01
-0.005
0
0 1 2 3 4 5 6 7
Kc
Flow rate,ml/min
y = 1.2873x-1
y = 0.0081x2 - 0.0894x + 1.1336 0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 1 2 3 4 5 6 7
τI
flow rate,ml/min
142
Figure 4-26. Z-N method tuning (530nm(a), 580nm(b), 637nm(c).
520
530
540
550
560
570
580
590
0 2 4 6 8 10 12 14
Wav
ele
ng
th, n
m
time, min
0.6
1.6
2.6
3.6
4.6
5.6
6.6
7.6
8.6
0 2 4 6 8 10 12 14
To
tal fl
ow
rate
,ml/m
in
time, min
143
Figure 4-26. Continued.
570
580
590
600
610
620
630
640
650
0 2 4 6 8 10 12 14 16 18
Wav
ele
ng
th, n
m
time, min
0.6
1.1
1.6
2.1
2.6
3.1
0 2 4 6 8 10 12 14 16 18
To
tal fl
ow
rate
,ml/m
in
time, min
144
Figure 4-26. Continued.
580
590
600
610
620
630
640
650
0 5 10 15 20 25
Wav
ele
ng
th, n
m
time, min
0.6
0.8
1
1.2
1.4
1.6
1.8
0 5 10 15 20 25
To
tal fl
ow
rate
,ml/m
in
time, min
145
Figure 4-27. The weight of Kc and τI in tuning program for set point=530.
-30
-25
-20
-15
-10
-5
0
5
10
15
0 2 4 6 8 10 12 14
Time, minute
(e-et-1)
dt/Taui*e(t)
e(t)-e(t-1)+dt/Taui*e(t)
146
Figure 4-28. Red shift of emission wavelength from the coating of CdS shell at 120°C.
0
0.2
0.4
0.6
0.8
1
1.2
500 550 600 650 700 750 800
A.U
Wavelength, nm
FR=0.75
FR=1.52
FR=2
Raw QD
147
5CHAPTER 5 CONCLUSION AND FUTURE WORK
SUMMARY
In this research, we demonstrated the application of the flow synthesis system and
the process control in nano particle manufacturing by modeling the Stober silica particle
synthesis process. The FSS was built with multiple improvements to solve the unique
problems that are associated with nano particle production with the Stober process. In
addition, both size map based and the feedback controls were investigated for the
Stober process for its improvement. Each of these controls demonstrated both
advantages and disadvantages during the model establishment and the adjustment of
process controls. The size map based control works well for the Stober model given a
known range of tuning parameters while the feedback control requires less information
from the process but gives better suitability because a similar output is achieved.
Case studies about dye doped silica particles and QDs indicate the potential of the
FSS in the nano particle field. The addition of dye molecules in the precursor molecule
TEOS enables the synthesis of silica particles with adjustable sizes using multiple dyes
which span the entire visible light region. The CdTe QDs synthesized by the
hydrothermal method were successfully converted into the FSS. The QDs synthesized
by the FSS emitted wavelengths ranging in the visible and NIR regions, from roughly 500
- 800nm with an offset of 2nm and a 40% - 60% quantum yield.
The advantage of the FSS allows for precise and quantitative studies on the effects
of reagent concentration, reaction temperature, and residence time. The results
indicated that high Cd2+ concentration and low ligand concentration were preferred for
preparing high quality QDs. The FSS also compressed the reaction time from hours to
148
seconds with no adverse effect on the QDs and the PL properties. Two feedback tuning
methods were applied on the residence time to control the emission wavelength of the
QDs, which are able to reach the set point at around 10min.
A specialized CdS coating method for CdTe was developed for the FSS by
controlling the degradation speed of Na2S. This method was able to create a CdS shell
that shifted the emission wavelength towards the red regions of the original QDs up to a
maximum of 70nm. The concentration of Na2S, temperature, and residence time can be
used as control parameters.
CONCLUSION
This study confirms the feasibility of applying the flow synthesis system in
nanoparticle manufacturing. The following tips would be useful to convert a batch
synthesis into the FSS.
1. The reaction should be studied by batch before any attempts in FSS so that agglomerations and large particles can be avoided in the selected reaction condition ranges.
2. After the determination of reaction conditions, the reagents should be separated into groups where they can keep unreactive.
3. The FSS is a good tool to optimize the reaction conditions. The required reaction temperature and reaction time may vary a lot due to the high thermal transfer rate of the tube reactor.
4. The online/inline detectors may be transformed from benchtop instruments by automatic sampling.
FUTURE WORK
In this research, the promising application of the FSS in the nano-particle
manufacturing field was reported. The research can be further extended in various ways.
Possible directions that directly related to this study are reported as follows:
149
Stober silica process. The addition of a passive mixer or micro-scale mixer may
improve the stability of the products and also shorten the residence time by mixing the
reagents faster and giving better uniformity.
Also, the temperature can be considered as a control parameter in order to
accelerate the reaction time for the Stober silica process, which should reduce the tube
blockage by intensifying the Brownian motion. The technique barrier is the lack of an
instrument that can do sonication and precise temperature control simultaneously.
CdTe QDs. More studies is necessary for the synthesis of core/shell structure
CdTe/CdS quantum dots. The effects from different control parameters can be compared
and optimized for the yield and long term system reliability.
A more complex system can be developed for the core/shell QDs. The quantum
yield can be online detected by the combination of absorption and emission spectrums.
The synthesis of core QDs and the coating process can be integrated into one flow
system. The relationship between quantum yield and shell thickness can be studied so
that a control algorithm may be designed to produce the QDs with desired emission
spectrum and highest quantum yield by controlling both the core QDs’ size and shell
thickness.
Besides the future steps that directly related to the above two synthesis, the FSS is
also promising in many other colloidal processes taking advantage of fast tuning
parameters and hydrothermal ability.
150
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BIOGRAPHICAL SKETCH
Jiaqing Zhou was born in Shanghai, P.R.China in 1984. He obtained his bachelor
degree in Materials Science and Engineering in July 2006 in Tongji University. He then
worked for half a year in as an engineer in Shanghai SBS Zipper Science & Technology
Co. Ltd. Later he continued his education at University of Florida beginning in 2007 and
joined Dr. Kevin Powers’ group. He received his Ph.D. from the University of Florida in
the summer of 2012.