Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Influences of Graphene Morphology and Contact Distance Between Nanosheets on the Effective Conductivity of Polymer Nanocomposites Publisher



Zare Y1 ; Gharib N2 ; Rhee KY3
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. College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait
  3. 3. Department of Mechanical Engineering (BK21 Four), College of Engineering, Kyung Hee University, Yongin, South Korea

Source: Journal of Materials Research and Technology Published:2023


Abstract

Herein, the contact distance and effective tunneling conductivity in graphene polymer nanocomposites are expressed assuming the properties of graphene stack and the resistances of all components by graphene dimensions, interphase depth, contact resistance and filler morphology (stacked and well-dispersed nanosheets). In the case of incomplete filler dispersion in the matrix, the volume share, aspect ratio and conduction of stacks are suggested. Also, the contact distance is presented based on a power law description by percolation onset and effective filler amount supposing the properties of stacks. The effects of all parameters on the contact distance and effective conductivity are plotted at various ranges of factors. Undoubtedly, the reasonable impacts of all factors on the contact distance and effective conductivity justify the suggested equations. A higher filler amount, more filler dispersion, lower number of nanosheets in stacks, higher aspect ratio of filler (thinner and larger nanosheets), deeper interphase and larger distance between nanosheets in stacks produce a shorter contact distance, bigger network and less total resistance causing more effective conductivity. © 2023
Other Related Docs
23. Predicting of Electrical Conductivity for Polymer-Mxene Nanocomposites, Journal of Materials Research and Technology (2024)
27. From Nano to Macro in Graphene-Polymer Nanocomposites: A New Methodology for Conductivity Prediction, Colloids and Surfaces A: Physicochemical and Engineering Aspects (2024)