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

Yasser Zare, Kyong Yop Rhee, Soo Jin Park

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)954-962
Number of pages9
JournalJOM
Volume75
Issue number3
DOIs
StatePublished - Mar 2023

Bibliographical note

Publisher Copyright:
© 2023, The Minerals, Metals & Materials Society.

Fingerprint

Dive into the research topics of 'Simulating Electrical Conductivity of Graphene-Filled System by Developing McLachlan Model Applicable to Breast Cancer Biosensors'. Together they form a unique fingerprint.

Cite this