Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Progressing of a Power Model for Electrical Conductivity of Graphene-Based Composites Publisher Pubmed



Zare Y1 ; Rhee KY2 ; Park SJ3
Authors
Show Affiliations
Authors Affiliations
  1. 1. Biomaterials and Tissue Engineering Research Group, Department of Interdisciplinary Technologies, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran
  2. 2. Department of Mechanical Engineering (BK21 Four), College of Engineering, Kyung Hee University, Yongin, South Korea
  3. 3. Department of Chemistry, Inha University, Incheon, 22212, South Korea

Source: Scientific Reports Published:2023


Abstract

This work presents a power equation for the conductivity of graphene-based polymer composites by the tunneling length, interphase deepness and filler size. The impressions of these factors on the effective concentration and percolation beginning of graphene nano-sheets in nanocomposites are also expressed. The developed equations for percolation beginning and conductivity are examined by the experimented data of some examples, which can guesstimate the interphase depth, tunneling size and percolation exponent. Besides, the impacts of numerous factors on the percolation beginning and conductivity are designed. The developed equation for percolation beginning shows the formation of thick interphase and large tunnels in the reported samples. So, disregarding of tunneling and interphase spaces in polymer graphene nanocomposites overpredicts the percolation beginning. Additionally, the developed model presents the acceptable calculations for the conductivity of samples. Among the mentioned parameters, the concentration and graphene conductivity in addition to the interphase depth induce the strongest effects on the conductivity of composites. © 2023, The Author(s).
Other Related Docs
25. From Nano to Macro in Graphene-Polymer Nanocomposites: A New Methodology for Conductivity Prediction, Colloids and Surfaces A: Physicochemical and Engineering Aspects (2024)
35. Predicting of Electrical Conductivity for Polymer-Mxene Nanocomposites, Journal of Materials Research and Technology (2024)