Prediction of electrical conductivity of polymer-graphene nanocomposites by developing an analytical model considering interphase, tunneling and geometry effects

In this paper, we have developed an analytical model based on the mean-field theory, to predict the effective electrical conductivity of polymer-graphene nanocomposites (PGNs). The graphene layers are considered as rectangular nanoplatelets randomly distributed in the polymer. Graphene geometry, interphase layer properties, and tunneling effect between graphene surfaces have been taken into account to analyze the effect of micro -structural parameters on the effective electrical conductivity of PGNs. Moreover, the dependence of the perco-lation threshold of PGNs on the graphene diameter, graphene layer thickness, tunneling distance, electrical potential barrier height, interphase thickness, and graphene conductivity have been evaluated. Considering these parameters simultaneously, the model can describe the electrical behavior of the PGNs well. Predictions from our model are in good agreement with the most cited experimental values of the effective electrical conductivity of PGNs. Our investigation can be used for the design and optimization of new materials, beyond just predicting and analyzing the existing ones.