Abstract
In this study, we propose a model for the estimation of conductivity of graphene-based samples considering the roles of the interphase depth, filler portion in the nets, network efficiency, tunneling processes (as a quantum effects of graphene), and superficial energies of polymer and nanoparticles. This model considers the effects of the amount, dimensions, conduction, and percolation onset of graphene nanosheets on conductivity. The proposed model is evaluated using experimental data and parametric examinations. The outputs of the proposed model display a desirable agreement with experimental results. It is demonstrated that the interphase deepness, network efficiency, polymer surface energy, and graphene aspect ratio directly control the conductivity, and a superior conductivity is acquired by the slimmer tunnels, lower percolation onset, and lower filler surface energy.
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