Abstract 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 microstructural parameters on the effective electrical conductivity of PGNs. Moreover, the dependence of the percolation 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.

中文翻译：

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通过开发考虑界面、隧道效应和几何效应的分析模型来预测聚合物-石墨烯纳米复合材料的电导率

摘要 在本文中，我们开发了一种基于平均场理论的分析模型，以预测聚合物-石墨烯纳米复合材料 (PGNs) 的有效电导率。石墨烯层被认为是随机分布在聚合物中的矩形纳米片。石墨烯几何形状、界面层特性和石墨烯表面之间的隧道效应已被考虑在内，以分析微观结构参数对 PGN 有效电导率的影响。此外，还评估了 PGN 的渗透阈值对石墨烯直径、石墨烯层厚度、隧道距离、势垒高度、界面厚度和石墨烯电导率的依赖性。同时考虑这些参数，该模型可以很好地描述 PGN 的电气行为。我们模型的预测与 PGN 有效电导率的最常引用的实验值非常一致。我们的调查可用于新材料的设计和优化，而不仅仅是预测和分析现有材料。