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Effect of phase transitions on the electrical properties of polymer/carbon
nanotube and polymer/graphene nanoplatelet composites with different
conductive network structures
Dong Xianga*, Lei Wanga, Yuhao Tanga, Chunxia Zhaoa, Eileen Harkin-Jonesb,
Yuntao Lia,c,*
aSchool of Materials Science and Engineering, Southwest Petroleum University,
Chengdu 610500, ChinabSchool of Engineering, University of Ulster, Jordanstown BT37 0QB, UKcState Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest
Petroleum University, Chengdu 610500, China
(Dong Xiang: [email protected]; Yuntao Li: [email protected])
Abstract: In this study, multi-walled carbon nanotube (MWCNTs) and graphene
nanoplatelet (GNPs) filled high density polyethylene (HDPE) composites with
dispersed and segregated network structures were prepared by solution-assisted
mixing. Simultaneous DC conductivity and differential scanning calorimetry (DSC)
was used to measure electrical conductivity during the composite thermal phase
transitions. It was found that the conductive network is deformed during melting and
rebuilt again during annealing due to the re-agglomeration of nanofillers. The re-
building of the structure is significantly affected by the original network structure and
by the shape and loading of the nanofillers. Both deformation and reorganisation of
the network lead to a drastic changes in the conductivity of composites. The
crystallization process also affects the conductive network to some extent and the
subsequent volume shrinkage of the polymeric matrix after crystallization results in a
further decrease in the resistivity of HDPE/GNP composites. Classical electrical
percolation theory combined with a kinetic equation is used to describe the
conductivity recovery of composites during annealing, and the results are found to be
in good agreement with the experimental data.
Keywords: Carbon nanotubes; Graphene nanoplatelets; Polymer composites;
Network structure; Phase transition.
1. Introduction
Polymer composites reinforced with carbon nanofillers have attracted extensive
attention from academia and industry in recent years.1–3 They are increasingly finding
applications in automotive, aerospace and electronics industries for strain sensor,
antistatic protection, electromagnetic interference (EMI) shielding and so on.4–7
Carbon nanofillers, such as carbon nanotubes (CNTs) and graphene nanoplatelets
(GNPs) are widely used for conductive polymer composites (CPCs) due to their
excellent conductivity and high aspect ratio. A low electrical percolation threshold
can be achieved for polymer/CNT and polymer/GNP composites, particularly when a
segregated conductive network structure is achieved in the polymer matrix.8,9 By way
of example, Lin et al.10 reported a CPC with a segregated structure containing
graphene as the conductive component in a natural rubber (NR) matrix with a
percolation value as low as 0.4 vol% (an 8-fold reduction compared with a
homogeneously dispersed structure).
In order to process CPCs into a desired final product, they usually experience
some shear force and some phase transition processes such as melting and
crystallization. The deformation or reorganisation of conductive networks in polymers
during such processing could significantly alter the electrical conductivity of the final
product compared to that of the original unprocessed material. Electrical conduction
in CPCs is generally described by a classic electrical percolation theory,11 while the
dynamic process of conductive network evolution is usually ignored. Some previous
studies12–14 carried out real-time tracing of the dependence of electrical properties on
processing and phase transitions of CPCs to better understand dynamic percolation
behaviour and conductive network formation. Hilarius al.15 investigated the influence
of shear deformation on the electrical conductivity of MWCNTs and carbon black
(CB) filled polycarbonate composites by time-resolved combined rheological and
electrical measurements. The results indicated that shear deformation led to a
significant decrease in the conductivity of composites. However, a time-dependant
recovery of conductivity was observed in the quiescent melt after shear due to the
rearrangement of conductive network. Zhang et al.,16 using in-situ electrical
measurements, investigated dynamic percolation in highly oriented conductive
networks formed with MWCNTs and CB during annealing. It was found that the
introduction of CB accelerated dynamic percolation and reduced the activation energy
of such processes. Some literature also reported the effect of crystallization on the
electrical properties of carbon nanotube filled polymer composites. Alig et al.17 found
that the conductivity of polypropylene/MWCNT composites decreased by over one
order of magnitude during crystallization due to the reduction of the amount of
amorphous phase, and a similar phenomenon was also observed by Deng et al.18 It is
well known that the electrical properties of CPCs strongly depend on the dimensional
characteristics of conductive nanofillers and the conductive network structures.19 It is
not clear however how the particle shape and size and thus network structure affect
the conductivity evolution of CPCs during phase transitions. To the authors’ best
knowledge, no such studies have been reported on to date.
This paper presents an approach for in-situ characterisation of the electrical
conductivity of HDPE/CNT and HDPE/GNP composites with dispersed and
segregated network structures during melting and crystallization by combining
differential scanning calorimetry (DSC) with electrical conductivity measurements.
This system can simultaneously monitor the electrical and thermal properties of the
polymer nanocomposites during the entire thermal process. This study provides an
insight into the dynamic percolation behaviour of CPCs and thus important
information for their processing and for designing composites with well controlled
properties.
2. Experimental
2.1 Materials
A commercial high density polyethylene in powder form (HDPE, 888LIB), with
a melt flow rate of 0.2 g 10 min-1, was obtained from Mitsui Chemicals, Inc. (Japan).
Multi-wall carbon nanotubes (NC7000) with an average diameter of 9.5 nm and an
average length of 1.5 μm were purchased from Nanocyl S.A. (Belgium). Graphene
nanoplatelets (xGNP-15) with an average diameter of 15 μm and an average thickness
of 6 ~ 8 nm were sourced from XG Science (USA). O-dichlorobenzene (o-DCB) was
used to dissolve the polymer matrix and disperse the nanofillers.
2.2 Preparation
The HDPE/CNT and HDPE/GNP composites with different conductive network
structures were prepared with the following steps illustrated in Fig.1. Initially, the
MWCNTs or GNPs with specified concentrations were ultrasonicated in ethyl alcohol
and o-DCB at 100 W, 40 kHz for 2 h at room temperature respectively. For a
segregated conductive network structure, the HDPE powder was added into the
nanofiller/ethyl alcohol suspension and stirred for 1 h (Fig.1a). For a dispersed
conductive network structure, the HDPE powder was firstly dissolved in o-DCB with
stirring at 130 ℃. Subsequently, the above nanofiller/o-DCB suspension was added
and fast stirred for 1 h (Fig.1b). The HDPE/nanofiller/solvent mixtures prepared by
the two different routes were filtered using a Buchner funnel and dried in a vacuum
oven at 80 ℃ for 24 h to obtain HDPE/nanofiller blends. Finally, the dried blends
were compression moulded at 170 ℃ under 10 MPa for 10 minutes to produce sheets
(76 × 76 × 1 mm3). These composite samples were labelled as CS-x, CD-x, GS-x and
GD-x, respectively. Here, “C” and “G” in the sample labels represent the types of
nanofillers, namely MWCNTs (C) and GNPs (G). “S” and “D” refer to the segregated
(S) and dispersed (D) conductive networks, and “x” indicates the weight fraction of
nanofillers (wt%) in the polymer matrix. For example, CS-1 and GD-3 and so forth,
representing a segregated network structure formed by 1 wt% MWCNTs and a
dispersed network structure formed by 3 wt% GNPs, respectively.
2.3 Characterization
The dispersion of nanofillers in HDPE matrix and the morphology of
nanocomposites were investigated by scanning electron microscopy (SEM, ZEISS
EV0 MA15) with an operating voltage of 20 kV. All samples for SEM observation
were permanganic etched20 for 2 h with ultrasonication at 100 W to remove the
amorphous phase of HDPE in order to observe the nanofillers more clearly. The
etched samples were then gold sputtered prior to imaging. X-ray diffraction (XRD)
analysis was performed using a DX-2700 diffractometer. Cu-Kα radiation with a
wavelength of 0.154 nm was used. Data were recorded from 2 to 60° with a scanning
speed of 0.02 ° min-1. Raman spectroscopy was conducted using a micro-Raman
spectrometer (IDRaman micro IM-52) with 785 nm laser excitation. The size of the
laser spot on the surface of samples was less than 1 mm in diameter and the wave
number ranged from 200 to 2000 cm-1 with 4 cm-1 resolution.
Direct current (DC) electrical conductivity (σ DC) was measured by two-point
method combined with a picoammeter (Keithley 6485) and a DC voltage source
(Tektronix PWS4323) at a constant voltage of 3 V for the strip-shaped nanocomposite
samples with a dimension of 50 × 10 × 1 mm3. Silver paint was used to minimise the
contact resistance between the samples and electrodes, and the distance between the
electrodes is 30 mm (Fig.2a). Electrical conductivity can be calculated using the
formula: σ DC=L /(R ×S ); where R is the electrical resistance of sample, L and S are
the length and cross-sectional area of sample respectively.
The in-situ electrical characterisation of the composites during phase transition
was achieved by combining a DSC (METTLER TOLEDO DSC823) and the above
mentioned picoammeter and DC voltage source, as shown in Fig.2b. The sample (2 ×
3 × 1 mm3, Fig.2c) taken from the compression moulded sheet was placed into the
DSC after the electrodes were fixed at the ends of sample using silver paste. A voltage
of 3 V was also applied on the sample during DSC-electrical measurements. The DSC
simultaneously measured the thermal behaviour of the sample under an inert nitrogen
atmosphere. The samples were firstly heated by DSC from 40 to 150 ℃ at a heating
rate of 10 ℃ min-1, then held for 5 minutes, followed by a cooling process from 150
℃ to 40 ℃ at a cooling rate of 10 ℃ min-1. All the electrical and thermal data were
collected by specialized software. In this work, the enthalpy of fusion21 of 100%
HDPE crystal (ΔH m
° ) was 293 J g-1 and the degree of crystallinity (XC) was calculated
using Eq.1:
XC=ΔHm
(1−M C ) ΔHm° × 100 %(1)
where ΔH m is the heat of fusion of sample; M C is the weight fraction of nanofillers.
3. Results and discussion
3.1 Electrical conductivity
First of all, the initial electrical properties of all the prepared composites were
investigated. According to classical electrical percolation theory, the conductive
nanofillers provide a large number of conductive pathways in an insulating polymer
matrix resulting in a great increase in electrical conductivity when the nanofiller
loading reaches a critical concentration.11,22 Fig.3 shows the volume conductivity of
composites with different conductive network structures as a function of nanofiller
loadings. It can be observed in Fig.3 that all the composite systems exhibit a clear
percolation behaviour. However, their conductivities clearly differ depending on the
network structures of the composite (to be shown in the SEM images in the next
section).
The effect of the conductive network structure on the conductivity of
composites was further quantitatively analysed according to the scaling law of
percolation threshold,23 which can be expressed by Eq.2.
σ DC∝( p−pc)μ(2)
where σ DC is the conductivity of the nanocomposites. p and pc are the filler weight
fraction and critical concentration, respectively. The critical exponent μ is a parameter
which depends on the dimensionality of the conductive network. It follows a power-
law dependence of approximately 1.6 ~ 2 in a three dimensional, and 1 ~ 1.3 in a two
dimensional system.11,24 Here the values of pc and μ critical exponent determined by a
least square fitting of experimental data are also shown in Fig.3.
The fitting results show that the HDPE/MWCNT composites with a segregated
structure have a very low critical concentration ( pc= 0.1 wt%). A critical exponent of
1.702 reveals that a typical 3D conductive network is formed by the nanotubes in this
system. For the HDPE/MWCNT composites with a dispersed structure, a higher
critical concentration ( pc= 0.5 wt%) is obtained, which indicates that the nanotubes
are less efficient in forming conductive pathways in this structure. The conductivity of
HDPE/MWCNT composites with a segregated structure is approximately 5 orders of
magnitude higher than that with a dispersed structure at the same MWCNT loading of
0.5 wt% (Fig.3a). It should be noted that the HDPE/MWCNT composite with a
dispersed structure has a high critical exponent (μ = 3.883), indicating μ may not be
universal in some practical systems. For tunnelling percolation systems, μ becomes
material dependant and can be higher than 2 due to the specific distributions of both
conducting and insulating phases.25 For the GNPs filled composite systems, a
segregated structure also contributes to a higher efficiency in forming continuous
conductive network compared to a dispersed structure. The pc values are 2.0 wt% and
6.0 wt% for the HDPE/GNP composites with a segregated and dispersed structure
respectively, as shown in Fig.3b. Furthermore, the dimensionality of the HDPE/GNP
systems is also affected by the original network structures. The HDPE/GNP system
with a dispersed structure is apt to form a 3D conductive network ( μ= 1.693), while
that with a segregated structure has a typical 2D network (μ= 1.049). In published
literature, Du et al.26 also reported a low μ value of 1.08 for segregated
HDPE/graphene nanocomposites. In general, one can see that the HDPE/GNP
systems have much higher critical concentrations compared to the HDPE/MWCNT
systems, regardless of the network structures. The poorer enhancement in the
conductivity of GNP nanocomposites may be due to difficulty in achieving
entanglements for the 2D nanoparticles. Moreover, there are many types of contact
between GNPs in a polymer matrix, such as plane-to-plane, edge-to-edge and edge-to-
plane, in which only the plane-to-plane contact facilitates charge transfer.27
3.2 Structures and dispersion
The original network structures and nanofiller morphologies in the
HDPE/MWCNT and HDPE/GNP composites were investigated using SEM, as shown
in Fig.4. The segregated structures consisting of 1 wt% MWCNTs and 3, 4 wt%
GNPs can be clearly seen in Fig.4a, Fig.4d and Fig.4e respectively where numerous
nanofillers are localised at the boundaries of polymer phases to promote the delivery
of electron charge. Fig4.b and Fig4.c show that individual and agglomerated
nanotubes are uniformly dispersed in the matrix. A denser conductive network is
formed when the MWCNT addition is increased from 1 to 3 wt%, thus the
conductivity of the CD-3 composite increased by 3 orders of magnitude compared to
CD-1, as shown in Fig.3a. The GNPs and GNP agglomerates are homogeneously
distributed in the 12 wt% HDPE/GNP composite (GD-12) in Fig.4f, but its volume
conductivity (Fig.3b) is only 1 order of magnitude higher compared to the segregated
HDPE/GNP composite containing 4 wt% GNPs (Fig. 4e).
X-ray diffraction was performed on the neat HDPE and nanocomposites in
order to investigate whether the different nanofiller morphologies and network
structures affect the crystalline structure of the polymer. The XRD patterns of the
materials are shown in Fig.5. All the HDPE and nanocomposite samples exhibit two
intensive diffraction peaks at 21.8° and 24.0° related to the typical orthorhombic unit
cell structure of HDPE in (110) and (200) planes, and two weak peaks at 30.1° and
36.3° corresponding to the (210) and (020) reflection planes respectively.28 Compared
to neat HDPE, the peaks in (110) and (200) for the MWCNTs and GNPs filled
nanocomposties become more intensive without any shifting. This indicates that the
crystalline structure is not influenced by the introduction of nanofillers, but they may
contribute to the perfection of polymer crystallites due to a nucleation effect. This is
supported by the increased average crystallite sizes in the (110) and (200) planes
(denoted by L110 and L200, respectively), as shown in Table 1. The crystallinity (X XRD)
of the nanocomposites calculated from the XRD results also slight increases except
for the GD-12. The decrease in the crystallinity of GD-12 can be attributed to the
excessive GNP addition hindering the motion of macromolecules. A similar situation
was also reported for PE/MWCNT composites where the crystallinity decreased by
5% with the addition of 10 wt% MWCNTs compared to the unfilled polymer.29
It can be observed in Fig.5a that a broad diffraction peak at 25.6°, derived from
the ordered arrangement of concentric cylinders of graphitic carbon for the
MWCNTs,30 is absent from the XRD patterns of the HDPE/MWCNT composites
regardless of the network structures, which is probably due to the large relative
intensity of the diffraction peaks between HDPE and MWCNTs.29 However, the
diffraction peak of GNPs at 26° corresponding to the (002) plane of the graphene
sheets31 can be still observed in both the segregated and randomly dispersed
HDPE/GNP composites in Fig.5b, which is more intensive for the GD-12 due to the
presence of more agglomerates at a high GNP loading.
Raman spectroscopy has become an important approach for the characterization
of polymer nanocomposites in order to assess nanofiller dispersion and
polymer/nanofiller interactions. Fig.6 shows the Raman spectra of the HDPE,
nanofillers and nanocomposites with different network structures. It can be seen in
Fig.6 that the four characteristic peaks (marked in the dashed box) of HDPE at
wavenumbers of 1063, 1130, 1298 and 1440 cm-1 are weakened and even disappear
with the addition of MWCNTs and GNPs due to the interactions between the
nanofillers and HDPE matrix resulting in an interference effect.32 The D band (1306
and 1360 cm-1 for MWCNTs and GNPs, respectively) derived from the disordered
graphite structures and the G band (1598 and 1584 cm-1 for MWCNTs and GNPs,
respectively) derived from the in-plane vibration of C-C bonds of the MWCNTs and
GNPs are gradually enhanced with increasing nanofiller loadings.29,31 However, the
exceptional intensity of D and G bands for the CS-1 which is even higher than that for
the CD-3 can probably be attributed to the segregated structure producing a localised
high concentration of nanotubes. In addition, the G bands of the HDPE/MWCNT and
HDPE/GNP composites up-shift by about 14 cm-1 and 8 cm-1 respectively due to the
compressive forces associated with the polymer chains and/or crystallites on the
nanofillers.33 This provides additional evidence for the interactions between the
polymer and nanofillers in the composites.
3.3 In-situ electrical characterization and thermal properties
The electrical and thermal properties of some representative composites with
different network structures and different nanofiller shapes and loadings were
simultaneously measured by the DSC-electrical conductivity meter system during
phase transitions, as shown in Fig. 7a-b. The temperature profile used in the tests is
shown in Fig.1b7c. Prior to the onset of melting, the resistance change ratio (∆ R /R0,
Eq.3) of the HDPE/MWCNT and HDPE/GNP composites increases gradually due to
volume expansion of the polymer matrix.
∆ RR0
=R−R0
R0 (3)
where R and R0 are the real-time and initial electrical resistance, respectively. The
increase in ∆ R /R0 for the CD-1, which is in the percolation region, is larger
compared to the CS-1 and CD-3 (Fig.7a). This reveals a less robust conductive
network for the CD-1 which results in a higher sensitivity to the increase in
temperature. A slight decrease (or a plateau, marked by an arrow in Fig. 7a) in ∆ R /R0
for the HDPE/MWCNT composites during the onset and peak of melting can be
attributed to the motion of nanotubes to regenerate some interconnected contacts,18
while this is absent for the HDPE/GNP composites probably due to the lower mobility
of the 2-dimensional GNPs.
Subsequently, the ∆ R /R0 of both the MWCNTs and GNPs filled composites
drastically increases up to the end of the melting stage as a result of the enhanced
network deformation, particularly for the HDPE/GNP composites. This indicates that
the conductive network formed by GNPs is more readily damaged due to lack of
entanglements or interlacing compared to that of the 1-dimensional nanotubes. The
∆ R /R0 of the GS-3, GS-4 and CD-1 samples, with a relatively lower initial
conductivity, increase by about 5000%, 400% and 100% respectively at the end of the
melting stage. Interestingly, the ∆ R /R0 for the CS-1 is lower than that for the CD-3
during melting, although the CS-1 has a lower initial conductivity (see Fig.3a and
Fig.7a). A similar situation occurs for the GS-4 and GD-12 (see Fig.3b and Fig.7b).
This demonstrates that a segregated network structure is more stable than the
dispersed structure as a consequence of the smaller distances between the nanofillers
localised at the boundaries of the polymer phases which facilitates electron transport
and tunnelling.10
After the completion of melting, ∆ R /R0 significantly decreases for the
HDPE/MWCNT and HDPE/GNP composites and levels off until the onset of
crystallization due to the reorganisation of the conductive network during annealing.34
Furthermore, the resistivity of the HDPE/MWCNT composites in this stage is even
lower than their initial resistivity (∆ R /R0< 0, Fig.7a), which implies that some new
conductive pathways have formed. However, the conductive network in the
HDPE/GNP composites cannot be fully recovered due to the low mobility of GNPs.
In addition, it can be observed in Fig.7a-b that the crystallization process inhibits
conductive network formation. Consequently, ∆ R /R0 increases to some extent for
the composites during cystallization, particularly for the HDPE/MWCNT composites.
Then the volume shrinkage of the polymeric matrix after crystallization results in
another decrease in the resistivity of the HDPE/GNP composites due to the GNP
network densification,35 while it does not show a significant effect on that of the
HDPE/MWCNT composites. It should be noted that the electrical properties of all the
samples return to their initial level after the thermal cycle, but the degree of
conductive network deformation and recovery that they have experienced during
phase transitions is highly dependent on the original network structure and on the
shapes and loadings of nanofillers in the nanocomposites.
The thermal properties of the HDPE/MWCNT and HDPE/GNP composites
were also obtained from the DSC-electrical conductivity meter system. Fig.8 shows
the DSC curves for the nanocomposites during melting and crystallization, and the
related thermal parameters are also listed in Table.1. It can be seen that the melting
behaviour and melting temperature (T m) of the HDPE is not affected by the difference
in network structure and the introduction of nanofillers (Fig.8a and Table 1), while the
crystallization temperature (T c) of the nanocomposites is slightly increased by about 4
~ 6 ℃ due to the nucleation effect of the nanofillers (Fig.8b and Table 1). The higher
T c for the CD-1 and CD-3 can be attributed to a better dispersion of nanotubes which
provides more nucleating sites. Although the trend in crystallinity from DSC (X DSC) is
in accord with that from XRD in Table 1, the changes in X DSC are not significant
considering the experimental error associated with DSC.
3.4 Modelling of electrical conductivity recovery
As discussed above, the deformation of the composite conductive network
during melting results in a drastic drop in electrical conductivity. During annealing,
however, conductivity demonstrates a clear recovery with time, particularly for those
composites with nanofiller loadings close to the percolation region. This recovery in
conductivity is often considered as an agglomerating of nanofillers generating
conductive spherical agglomerates, and these agglomerates occupy a much larger
effective volume than fully dispersed nanofillers. At a certain concentration, the
interconnected agglomerates form conductive pathways in the polymer matrix, which
is sometimes referred as dynamic percolation.36,37
For modelling of the agglomerating process of nanofillers, a second order
kinetics method, first proposed by Heinrich et al.38 for describing the formation of
filler networks in elastomers, was applied. The deduced kinetic equation (Eq.4) gives
a time dependent volume concentration of growing agglomerates, PA ( t ). For
establishing the relationship between the direct current conductivity (σ DC) of the
composite and PA (t ), the classical electrical percolation theory considering the
effective volume concentration of the nanofiller agglomerates was used, as expressed
in Eq.5.11
PA ( t )=PA 0+(PA ∞−PA 0)(1−1
1+4 kt (PA ∞−P A 0)) (4)
σ DC=σ0 A(PA−Pc
'
1−Pc' )
μ
, PA>PC (5)
where PA0 and PA ∞ are the starting and final (t→∞) effective volume concentration of
the agglomerates respectively, k is the reaction rate constant; σ 0 A is the conductivity
of nanofiller agglomerates, and the values are approximately set as 1 × 10-2 and 5 ×
10-3 S cm-1 for MWCNTs and GNPs respectively. The much lower values of σ 0 A
compared to individual MWCNT and GNP are due to the volume occupied by the
agglomerates containing a considerable amount of polymer chains.39 Pc
' is the critical
concentration of agglomerates, which is fixed at 20 vol%.40 Parameter μ is determined
according to the fitted values in Fig.3.
Fig.9 shows that the conductivity of the HDPE/MWCNT and HDPE/GNP
composites during annealing agrees well with the theoretical model. The related
fitting parameters for modelling are shown in Table 2. One can see in Table 2 that the
PA 0 values of all the measured samples are higher than the
Pc' , indicating that there
are sufficient conductive agglomerates in the matrix to partially retain the
conductivity at the end of melting. In addition, the PA ∞ further increases for all the
composites on the basis of PA 0, which demonstrates that more nanofillers have
entered the agglomerates spontaneously during annealing due to the van der Waals
forces between the nanofillers to construct a more robust conductive network in the
HDPE matrix and improve the conductivity of the composite. In Fig.9b, the
conductivity of the GS-3 increased by more than one order of magnitude after
annealing. Also, it can be found from Table 2 and Fig.9 that the composites with a
lower PA0 (closer to
Pc' ) correspond to a more evident increase or recovery in
conductivity. Furthermore, the PA 0 values of the composites are affected by the
conductive network structure, and the loadings and shapes of nanofillers. By way of
example, the conductivity of the CD-1 with the lowest PA 0 value of 21.3 vol%
increased by 386% from 7.2 × 10-3 Sm-1 to 3.5 × 10-2 Sm-1 during annealing (Fig. 9a).
By contrast, the conductivity only increased by 93% for the CS-1 with a PA 0 much
higher than the Pc
' value (Fig. 9a). It also indicates that a dispersed structure would
lead to a clearer conductivity recovery process. The CD-1 and GS-3 with lower
nanofiller loadings than the CD-3 and GS-4 respectively, present a lower PA 0 and
more obvious conductivity recovery (Fig. 9b). It demonstrates that conductivity
recovery is evident at lower nanofiller loadings. 2-dimensional GNPs appear to result
in a lower PA 0 and thus stronger conductivity recovery compared to 1-dimensional
MWCNTs. This can be clearly seen in Table 2 and Fig.9 for the GS-3 and CS-1
despite of GS-3 containing more nanofillers. In general, one can see that a dispersed
structure, lower nanofiller loading and 2-dimensional GNPs result in a stronger
conductivity recovery.
4. Conclusions
The effect of phase transitions on the electrical properties of HDPE/MWCNT
and HDPE/GNP composites with different conductive network structures was
investigated by in-situ DSC-electrical measurements. It was found that the
construction of a segregated conductive network structure greatly reduces the
percolation thresholds for both MWCNTs and GNPs filled composites. The
conductive network is deformed and rebuilt during melting and annealing
respectively, a process which is significantly affected by the original network
structure in the polymer and the shape and loading of the nanofillers. The
crystallization process has an inhibitory effect on the conductive network formed by
MWCNTs and GNPs and the volume shrinkage of the polymeric matrix after
crystallization further reduces the resistivity of the HDPE/GNP composites. The
recovery of conductivity in the composites during annealing is considered as a re-
agglomeration process of nanofillers which reforms the conductive pathways in the
polymer melt. The classical electrical percolation theory combining a derived kinetic
equation can reasonably describe the time dependent conductivity recovery of the
composites, and the fitted results from modelling are in good agreement with the
experimental data. This work helps to provide a better understanding of the electrical
conductivity evolution of CPCs during phase transitions and can provide some
valuable guidance for their processing.
Acknowledgments
The authors would like to thank the financial support from SWPU open
experiment program (KSZ16106), Science & Technology Department of Sichuan
Province (2017HH0086, 2017JY0152) and Education Department of Sichuan
Province (17ZB0462).
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Pictures
Fig.1. Schematic diagram for preparation of the HDPE/MWCNT composites with a segregated
network structure (a) and HDPE/GNP composites with a dispersed network structure (b).
Fig.2. Schematic diagram for the DSC-electrical measurement (a) and photographs of specimen
for DC electrical conductivity (b) and DSC-electrical measurements (c).
Fig.3. Electrical conductivity of the MWCNTs (a) and GNPs (b) filled composites with dispersed
and segregated structures versus nanofiller loadings.
Fig.4. SEM images of the HDPE/MWCNT and HDPE/GNP composites with different network
structures: CS-1 (a), CD-1 (b), CD-3 (c), GS-3 (d), GS-4 (e), GD-12 (f).
Fig.5. XRD patterns of the HDPE/MWCNT (a) and HDPE/GNP (b) composites with different
conductive network structures and nanofiller loadings.
Fig.6. Raman spectra of the HDPE/MWCNT (a) and HDPE/GNP composites (b) with different
conductive network structures.
Fig.7. In-situ electrical measurement results for the HDPE/MWCNT (a) and HDPE/GNP (b)
composites during phase transitions and the temperature profile used in tests (c).
Fig.8. DSC curves of the HDPE/MWCNT and HDPE/GNP composites in the melting (a) and
crystallization (b) processes.
Fig.9. Fitting results for the conductivity recovery modelling of HDPE/MWCNT (a) and
HDPE/GNP (b) nanocomposites during annealing.
Tables
Table 1. Summary of the related parameters for the nanocomposites from DSC and XRD.
Sample T m(℃) T c (℃) X DSC (%)%) X XRD (%) L110(nm) L200(nm)
HDPE 129.6 108.2 43.0 54.5 20.1 16.9
CS-1 128.2 111.8 44.5 56.9 21.2 17.8
CD-1 128.5 113.2 45.3 56.8 20.4 17.5
CD-3 128.3 114.5 46.2 61.6 21.1 18.0
GS-3 128.6 111.9 44.0 56.5 20.4 16.4
GS-4 129.0 111.9 45.9 59.9 20.7 17.0
GD-12 128.9 112.2 42.3 48.0 21.7 18.0
Table 2. Summary of the fitted parameters for conductivity recovery modelling.
Sample σ 0 A (S cm-1) PA 0 (vol
%)(()(vol
Pc' (vol%) PA ∞ (vol%) k (s-1)
CS-1 1 × 10-2 35.1 20 43.9 1.8 × 10-4
CD-1 1 × 10-2 21.3 20 22.4 9.8 × 10-4
CD-3 1 × 10-2 25.3 20 27.2 1.2 × 10-4
GS-3 5 × 10-3 20.2 20 25.8 1.3 × 10-4
GS-4 5 × 10-3 33.5 20 54.6 1.6 × 10-4
GD-12 5 × 10-3 22.6 20 26.1 5.6 × 10-4