A Caltech Library Service

3D percolation modeling for predicting the thermal conductivity of graphene-polymer composites

Aryanfar, Asghar and Medlej, Sajed and Tarhini, Ali and Damadi, S. Reza and Tehrani B., Ali R. and Goddard III, William A. (2021) 3D percolation modeling for predicting the thermal conductivity of graphene-polymer composites. Computational Materials Science, 197 . Art. No. 110650. ISSN 0927-0256. doi:10.1016/j.commatsci.2021.110650.

[img] MS Excel (Supplementary data 1) - Supplemental Material
See Usage Policy.

[img] MS Excel (Supplementary data 2) - Supplemental Material
See Usage Policy.


Use this Persistent URL to link to this item:


Graphene-based polymer composites exhibit a microstructure formed by aggregates within a matrix with enhanced thermal conductivity. We develop a percolation-based computational method, based on the multiple runnings of the shortest path iteratively for ellipsoidal particles to predict the thermal conductivity of such composites across stochastically-developed channels. We analyzes the role of the shape and the aspect ratio of the flakes and we predict the onset of percolation based on the density and particle dimensions. Consequently, we complement and verify the conductivity trends via our experiments by inclusion of graphene aggregates and fabrication of graphene-polymer composites. The analytical development and the numerical simulations are successfully verified with the experiments, where the prediction could explain the role of larger set of particle geometry and density. Such percolation-based quantification is very useful for the effective utilization and optimization of the equivalent shape of the graphene flakes and their distribution across the composite during the preparation process and application.

Item Type:Article
Related URLs:
URLURL TypeDescription
Aryanfar, Asghar0000-0002-8890-077X
Goddard III, William A.0000-0003-0097-5716
Additional Information:© 2021 Elsevier. Received 5 April 2021, Revised 3 June 2021, Accepted 6 June 2021, Available online 20 June 2021. The authors would like to thank and recognize the support from Masri Institute at American University of Beirut, Award#103919 and the PhD support from the Maroun Samaan Faculty of Engineering and Architecture for the student Sajed Medlej. CRediT authorship contribution statement: Asghar Aryanfar: Conceptualization, Validation, Formal analysis, Investigation, Writing - original draft, Writing - review & editing, Visualization, Funding acquisition. Sajed Medlej: Methodology, Data curation, Writing - review & editing. S. Reza Damadi: Data curation, Software, Writing - review & editing. Ali R. Tehrani B.: Supervision, Project administration. William A. Goddard III: Supervision, Project administration. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Funding AgencyGrant Number
American University of Beirut103919
Subject Keywords:Composite polymer; Percolation; Elliptic fillers; Thermal conductivity
Other Numbering System:
Other Numbering System NameOther Numbering System ID
Record Number:CaltechAUTHORS:20210628-191053775
Persistent URL:
Official Citation:Asghar Aryanfar, Sajed Medlej, Ali Tarhini, S. Reza Damadi, Ali R. Tehrani B., William A. Goddard III, 3D percolation modeling for predicting the thermal conductivity of graphene-polymer composites, Computational Materials Science, Volume 197, 2021, 110650, ISSN 0927-0256,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109631
Deposited By: George Porter
Deposited On:28 Jun 2021 23:13
Last Modified:16 Nov 2021 19:37

Repository Staff Only: item control page