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

Asghar Aryanfar*, Sajed Medlej, Ali Tarhini, Ali R. Tehrani B

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2081-2089
Number of pages9
JournalSoft Matter
Volume17
Issue number8
DOIs
Publication statusPublished - 28 Feb 2021
MoE publication typeA1 Journal article-refereed

Fingerprint

Dive into the research topics of 'Elliptic percolation model for predicting the electrical conductivity of graphene-polymer composites'. Together they form a unique fingerprint.

Cite this