Developing a high-efficiency predictive model for self-temperature-compensated piezoresistive properties of carbon nanotube/graphene nanoplatelet polymer-based nanocomposites

Abstract

The thermo-resistive and piezoresistive responses of carbon nanotube (CNT)/graphene nanoplatelet (GNP) polymer-based nanocomposites are analytically investigated. The 3D representative volume element is generated by the Monte Carlo approach to incorporate the random distribution of nanofillers. The Monte Carlo approach is paired with the percolation model to investigate the percolation behavior of the nanocomposite. The validity of the analytical model is verified by comparing the predicted results with the experimental data. The Poisson's ratio and height of barrier potential influence on the piezoresistivity of nanocomposite are studied. Analytical results determine the aspect ratio and influence of carbon nanotube degree of orientation on thermoresistivity of nanocomposite. The effects of intrinsic and physical properties of GNPs on resistivity change with temperature are investigated. It is found that nanocomposite filled with CNTs presented lower percolation threshold than those filled with GNPs. The results also revealed that the filler alignment caused a higher piezoresistivity. © 2022 Elsevier Ltd