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.

在这项研究中，我们提出了一个模型，用于估计基于石墨烯的样品的电导率，考虑相间深度、网络中的填料部分、网络效率、隧穿过程（作为石墨烯的量子效应）和表面能聚合物和纳米颗粒。该模型考虑了石墨烯纳米片的数量、尺寸、传导和渗透开始对电导率的影响。使用实验数据和参数检查评估所提出的模型。所提出模型的输出与实验结果显示出理想的一致性。结果表明，相间深度、网络效率、聚合物表面能和石墨烯纵横比直接控制着电导率，通过更细的隧道、更低的渗透开始、