Issue 8, 2021

Elliptic percolation model for predicting the electrical conductivity of graphene–polymer composites

Abstract

Graphene-based polymers exhibit a conductive microstructure formed by aggregates in a matrix which drastically enhances their transmitting properties. We develop a new numerical framework for predicting the electrical conductivity based on continuum percolation theory in a two dimensional stochastically-generated medium. We analyze the role of the flake shape and its aspect ratio and consequently predict the onset of percolation based on the particle density and the domain scale. Simultaneously, we have performed experiments and have achieved very high electrical conductivity for such composites compared to other film fabrication techniques, which have verified the results of computing the homogenized electrical conductivity. As well, the proximity to and a comparison with other analytical models and other experimental techniques are presented. The numerical model can predict the composite transmitting conductivity in a larger range of particle geometry. Such quantification is exceedingly useful for effective utilization and optimization of graphene filler densities and their spatial distribution during manufacturing.

Graphical abstract: Elliptic percolation model for predicting the electrical conductivity of graphene–polymer composites

Associated articles

Supplementary files

Article information

Article type
Paper
Submitted
01 Nov 2020
Accepted
21 Dec 2020
First published
22 Dec 2020

Soft Matter, 2021,17, 2081-2089

Elliptic percolation model for predicting the electrical conductivity of graphene–polymer composites

A. Aryanfar, S. Medlej, A. Tarhini and A. R. Tehrani B, Soft Matter, 2021, 17, 2081 DOI: 10.1039/D0SM01950J

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