Multifactorial predicting of conductivity in polymer nanocomposites with graphene: Insights into imperfect interphase conduction

Abstract Many papers have investigated the conductivity of nanocomposites containing a perfect interphase. Herein, an imperfect interphase is considered, and the conduction transference from graphene to the medium in the graphene polymer system denoted as Y is expressed by the efficient dimensions of the interphase and graphene nanosheets. Also, the real inverse aspect ratio, real filler portion, percolation threshold, and the proportion of percolated graphene in the nets are suggested by Y . Besides, an appropriate model for nanocomposite conductivity is established, assuming the mentioned issues and the contact region among neighboring nanosheets. The novel model is examined by various tested data. In addition, the characters of the main terms in Y and the conductivity of nanocomposites are explained. The calculations of the developed model display good arrangement with the tested data, while the original model underestimates the nanocomposite conductivity. Strong and large interfacial/interphase regions, big and slim nanosheets, and dense interphase harvest a high conductivity in nanocomposites. Additionally, a great extent of Y and a small inverse aspect ratio increase the efficiency of nanosheets, which results in high nanocomposite conductivity. The highest Y is obtained by the thinnest graphene nanosheets and the thickest interphase, enhancing the conductivity of nanocomposites. Highlights Conduction transference is expressed by the thicknesses of interphase and graphene. A simple model is proposed for nanocomposite's conductivity by conduction transference. The calculations of the novel model display good agreements with the tested data of samples. A strong and big interface/interphase produces a high conduction transfer. A high amount of conduction transferring increases the graphene efficiency and conductivity.