Multi-scale numerical simulations on piezoresistivity of CNT/polymer nanocomposites
© Hu et al.; licensee Springer. 2012
Received: 29 May 2012
Accepted: 30 June 2012
Published: 17 July 2012
In this work, we propose a comprehensive multi-scale three-dimensional (3D) resistor network numerical model to predict the piezoresistivity behavior of a nanocomposite material composed of an insulating polymer matrix and conductive carbon nanotubes (CNTs). This material is expected to be used as highly sensitive resistance-type strain sensors due to its high piezoresistivity defined as the resistance change ratio divided by the mechanical strain. In this multi-scale 3D numerical model, three main working mechanisms, which are well known to induce the piezoresistivity of strain sensors fabricated from nanocomposites, are for the first time considered systematically. They are (a) the change of the internal conductive network formed by the CNTs, (b) the tunneling effect among neighboring CNTs, and (c) the CNTs’ piezoresistivity. Comparisons between the present numerical results and our previous experimental ones were also performed to validate the present numerical model. The influence of the CNTs’ piezoresistivity on the total piezoresistivity of nanocomposite strain sensors is explored in detail and further compared with that of the other two mechanisms. It is found that the first two working mechanisms (i.e., the change of the internal conductive network and the tunneling effect) play a major role on the piezoresistivity of the nanocomposite strain sensors, whereas the contribution from the CNTs’ piezoresistivity is quite small. The present numerical results can provide valuable information for designing highly sensitive resistance-type strain sensors made from various nanocomposites composed of an insulating polymer matrix and conductive nanofillers.
Due to their excellent electrical properties, carbon nanotubes (CNTs) of high aspect ratio are predicted to open up a whole range of smart structural applications . In particular, it has been confirmed that the conductance of a CNT could be dramatically changed by introducing a mechanical strain as a consequence of structural change (e.g., chirality change ). Due to this piezoresistivity of CNTs, great interest has recently been aroused in building strain sensors not only using CNTs themselves (either single-walled carbon nanotube (SWCNT) or multi-walled carbon nanotube (MWCNT)) [3–5] but also incorporating them into an insulating polymer matrix [6–9], as summarized in a recent review paper . As compared to conventional metal-foil strain gauges, higher sensitivities have been observed in these novel sensors at least at a macro-scale [5–9]. Moreover, a linear piezoresistivity has been identified within very small strain ranges (e.g., 200 με  or 1,300 με ), whereas nonlinear piezoresistivity has also been reported for a quite large strain range (e.g., 6,000 με [6, 7]). In spite of these exciting results, the fundamental understanding of piezoresistivity behavior in a CNT/polymer nanocomposite is still lacking, largely due to less effort being put into theoretical and numerical investigations.
Based on a three-dimensional (3D) statistical resistor network model , we have previously considered the change of internal CNT conductive network and tunneling effect in exploring the piezoresistivity behavior of the above CNT/polymer nanocomposite strain sensors . The movement of CNTs in the polymer under a given strain is firstly predicted using a rigid-body fiber reorientation model. Then, by integrating this fiber reorientation model into the 3D resistor network model in an iterative way, the resistance change of the nanocomposites caused by the applied strain is estimated. Moreover, the predicted electrical conductivity and piezoresistivity of CNT/polymer nanocomposites are verified using our previous experimental data [6–8]. Furthermore, the influence of various parameters (i.e., cross-sectional area of tunnel current, height of barrier, alignment of CNTs, electrical conductivity, and nanofiller electrical conductivity) are systematically investigated . However, the effect of the piezoresistivity of CNTs themselves is not mentioned.
The change of the internal conductive network formed by the CNTs themselves.
The tunneling effects among neighboring CNTs.
The piezoresistivity of CNTs themselves.
To include the CNTs’ piezoresistivity effect in a multi-scale way, we firstly calculate the strain of an embedded CNT by applying a mechanical tensile strain on a micro-mechanics-based cylindrical representative volume element (RVE), which simulates the CNT deformation for the case of multiple one-directionally aligned CNTs in a polymer matrix. Then, we transform the strain of the CNT into that of an arbitrary-oriented CNT dispersed in macroscopic CNT/polymer nanocomposites. By employing this strain information and the piezoresistivity of CNTs themselves predicted from the first-principle computations at the atomistic scale [13, 14], we update the electrical conductance of the CNTs in the internal conductive network of the macroscopic nanocomposite sensors under the applied strain. By using this updated electrical conductance of CNTs and incorporating the internal network change of CNTs and tunneling effect simultaneously, we finally comprehensively evaluate the piezoresistivity of the nanocomposite strain sensors.
So far, the resistor network model has been well documented  to predict the electrical conductivity of some conductive composites. However, because of its high computational cost, the corresponding numerical studies have been limited [15, 16], even for that of conventional composites with short carbon fibers. There has been no work about its extension for predicting the piezoresistivity of CNT/polymer nanocomposites, except for our previous work . To maintain the integrity of the present work, our previously improved 3D resistor network model  is briefly described.
An improved 3D resistor network model without the effect of CNT’s piezoresistivity
where J is tunneling current density (see Figure 3), V is the electrical potential difference, e is the quantum of electricity, m is the mass of electron, h is Planck’s constant, d is the distance between CNTs, λ is the height of the barrier (for epoxy, 0.5 to 2.5 eV), and A is the cross-sectional area of the tunnel (the cross-sectional area of CNT is approximately used here).
Based on the well-known matrix representation for a resistor network  and Kirchhoff’s current law, the total electrical current I under an applied voltage can be estimated (see Figure 2). This is a large-scale linear system because the number of CNTs involved in the numerical model is very large, which ranges from several thousands, possibly up to several tens of thousands, depending on the aspect ratio of the CNTs. An iterative equation solver (i.e., the incomplete Cholesky conjugate gradient method) has been used to solve these linear equations for obtaining the total electrical current I. Then, the macroscopic electrical conductivity of the nanocomposites can be evaluated by Ohm’s law, which is used for predicting the resistance of the nanocomposites.
The reorientation of CNTs under the applied strain is simplified as a rigid-body movement due to a much higher Young’s modulus of CNTs compared with a polymer matrix and the very weak interface between CNTs and the matrix . Therefore, the change of CNT position and orientation caused by the mechanical strain can be evaluated using the 3D fiber reorientation model  based on an affine transformation (see Figure 3). Corresponding to an updated CNT distribution under a prescribed mechanical strain, a new CNT network can be formed by re-calculating the possible intersections between CNTs and tunneling resistances between CNTs within the cutoff distance.
The switch of the CNT intersections to a possible tunneling effect due to the breakup of CNT contacts and the update distance of pre-existing tunneling effects were modeled. Then, the resistance of a nanocomposite at a fixed strain level can be re-evaluated using the 3D resistor network in an iterative way to finally calculate the piezoresistivity of the nanocomposite.
Extension of the above model with considering the piezoresistivity of CNT
Parameters of the CNT and polymer
2, 3, 4, 5
Young’s modulus (GPa)
Young’s modulus (GPa)
Strain ratio α of CNT to polymer at various CNT loadings
Strain ratioα in Equation3
where lij is the distance of the two intersection points calculated from (xi, yi, zi) and (xj, yj, zj).
where ΔR is the resistance change of a SWCNT due to a strain ε θ , and R0 is the initial resistance of this SWCNT.
The samples of SWCNTs used to obtain the average piezoresistivity response in Equation 6 were (8,0), (8,1), (8,2), (8,8), etc., and their diameters were very small . For CNTs with large diameters, e.g., 50 nm in this work, the influence of diameter on ΔR/R0 can be neglected since Equation 6 only describes the resistance change caused by quantum effects due to CNT lattice deformation based on the Landauer formula, in which the size effect on ΔR/R0 is removed. Moreover, the influences of the changes of CNT’s length and cross-sectional area in classical electrical equations are neglected based on the following reasons: (1) they are very small under small strains, and (2) quantum phenomena are dominant at the atomistic level.
Furthermore, in Table 1, the Young’s modulus of the CNTs was taken as 1,000 GPa, which is slightly higher than most reported values for MWCNTs, e.g., 500 to 800 GPa. By using a lower Young’s modulus for a CNT, its strain should increase. However, by considering the weak piezoresistivity of CNTs in Equation 6 and low CNT loading, this strain increase cannot lead to remarkable piezoresistivity change of nanocomposite sensors.
where SCNT is the cross-sectional area of a CNT, and l is the CNT length. Note that in this work, the initial electrical conductivity σ0 is taken as 1 × 104 S/m.
By substituting Equations 10 and 11 into Equations 5 and 9, we can update the orientation angle of the CNT and the electrical conductance gij between i and j.
Note that besides update of the electrical conductivity of the CNT segment on the internal conductive network, we also update the internal CNT network by judging the loss or formation of contacting points, and update the tunneling effect by changing the distance among those neighboring CNTs, as done in our previous work .
Results and discussion
Computational conditions for present numerical simulation
Electrical conductivity (S/m)
1 × 104
CNT loading Wf (wt.%)
2, 3, 4, 5
Barrier height λ (eV)
Tunneling distance range (nm)
Unit cell size (μm3)
25 × 25 × 15
Note that the CNTs in the present numerical model are distributed uniformly at random without considering their possible aggregations. From our recent experimental results (NH et al., unpublished data), some moderate aggregations can lead to a sparse conductive network of CNTs, and then result in a higher sensor sensitivity. However, in this case, the stability and repeatability of nanocomposite sensors decrease especially for the case of low CNT loading. Moreover, we deal with the polymer matrix as a completely insulating one. Some polymers may be electrically conductive to a certain extent. In this case, this point may also influence the piezoresistivity of nanocomposite sensors. Although it is quite difficult to precisely estimate this influence, it can be speculated from the following two aspects. Firstly, by considering Ohm’s law, the resistance Rp of a polymer matrix only is proportional to L/A, where L and A are the length and cross-sectional area of a nanocomposite sensor. Then, Rp will change by ΔRp since both L and A vary when the sensor is subjected to a strain. Therefore, the total piezoresistivity of the nanocomposite sensor should increase when considering the contribution of ΔRp. Secondly, the working mechanism, i.e., the change of internal network of the CNTs due to strain, might be weakened since the electric current can still pass through the matrix at the breakup points of the CNT network. However, when the electrical conductivity of the conductive polymer is much lower than that of the CNTs, this influence might be small since electric current always smartly chooses the components of much higher conductivity, i.e., CNTs, to pass through.
In this work, we extend our previous 3D resistor network numerical model for predicting the piezoresistivity behaviors of strain sensors made from CNT/polymer nanocomposites. Besides the change of the internal conductive network formed by the conductive CNTs and the tunneling effect among neighboring CNTs, the contribution of the CNTs’ piezoresistivity was incorporated simultaneously. This modeling was realized by using a multi-scale technique in which we firstly compute the strain of a CNT in a RVE, then transform it into the strain of a randomly oriented CNT in the polymer matrix under a prescribed strain, and finally build up the relationship between the electrical conductivity change of the CNT using the above strain information and the CNTs’ piezoresistivity obtained from the first-principle computations . An iterative model, which combines the 3D resistor network model and the fiber reorientation model, was constructed to predict the total piezoresistivity of the nanocomposite sensors from the updated information including CNT position, the internal conductive network, the tunneling effect among those neighboring CNTs, and the electrical conductivity of CNTs. The computational results qualitatively agree with our previous experimental results very well. Moreover, the contribution of the piezoresistivity of CNTs on the total piezoresistivity of the nanocomposite sensors is comparatively small, compared with those from the change of the internal conductive network and tunneling effects. The present numerical results can provide some valuable information for designing the highly sensitive resistance-type strain sensors made from the nanocomposites containing CNTs or other nanofillers.
This work was partly supported by the Grand-in-Aid for Scientific Research (no. 22360044) from the Japanese Ministry of Education, Culture, Sports, Science and Technology to NH.
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