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Simulating Electrical Conductivity of Graphene-Filled System by Developing Mclachlan Model Applicable to Breast Cancer Biosensors Publisher



Zare Y1 ; Rhee KY2 ; Park SJ3
Authors

Source: JOM Published:2023


Abstract

In this study, the model proposed by McLachlan is extended to include the electrical conductivity of a graphene-filled system. The tunneling and interphase sections are considered for percolation inception and effective portion of the graphene nanosheets. The experimental data of percolation inception and conductivity for various examples are used to examine the model. Additionally, the effects of numerous factors, such as graphene size, graphene aspect ratio, interphase depth, and tunneling length, on the percolation inception, effective filler portion, and conductivity are studied. The levels of percolation inception and conductivity show good agreement with the estimates from the developed equations. Moreover, a thick interphase and low percolation inception improve conductivity. It was found that the concentration and thickness of the graphene nanosheets mainly affect conductivity. Hence, these advanced equations are expected to replace the conventional formulations for percolation inception and conductivity in nanocomposites. The developed model is applicable for breast cancer biosensors, since conductivity plays a key role in the efficacies of such biosensors. © 2023, The Minerals, Metals & Materials Society.
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