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: dxiang01@hotmail.com; Yuntao Li: yuntaoli@swpu.edu.cn)

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