Computational modeling of electrically conductive networks formed by graphene nanoplatelet-carbon nanotube hybrid particles

Handle URI:
http://hdl.handle.net/10754/627050
Title:
Computational modeling of electrically conductive networks formed by graphene nanoplatelet-carbon nanotube hybrid particles
Authors:
Mora Cordova, Angel; Han, Fei ( 0000-0002-8050-3657 ) ; Lubineau, Gilles ( 0000-0002-7370-6093 )
Abstract:
One strategy to ensure that nanofiller networks in a polymer composite percolate at low volume fractions is to promote segregation. In a segregated structure, the concentration of nanofillers is kept low in some regions of the sample. In turn, the concentration in remaining regions is much higher than the average concentration of the sample. This selective placement of the nanofillers ensures percolation at low average concentration. One original strategy to promote segregation is by tuning the shape of the nanofillers. We use a computational approach to study the conductive networks formed by hybrid particles obtained by growing carbon nanotubes (CNTs) on graphene nanoplatelets (GNPs). The objective of this study is (1) to show that the higher electrical conductivity of these composites is due to the hybrid particles forming a segregated structure and (2) to understand which parameters defining the hybrid particles determine the efficiency of the segregation. We construct a microstructure to observe the conducting paths and determine whether a segregated structure has indeed been formed inside the composite. A measure of efficiency is presented based on the fraction of nanofillers that contribute to the conductive network. Then, the efficiency of the hybrid-particle networks is compared to those of three other networks of carbon-based nanofillers in which no hybrid particles are used: only CNTs, only GNPs, and a mix of CNTs and GNPs. Finally, some parameters of the hybrid particle are studied: the CNT density on the GNPs, and the CNT and GNP geometries. We also present recommendations for the further improvement of a composite's conductivity based on these parameters.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Mechanical Engineering Program; COHMAS Laboratory, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Citation:
Mora A, Han F, lubineau gilles (2018) Computational modeling of electrically conductive networks formed by graphene nanoplatelet-carbon nanotube hybrid particles. Modelling and Simulation in Materials Science and Engineering. Available: http://dx.doi.org/10.1088/1361-651x/aaab7a.
Publisher:
IOP Publishing
Journal:
Modelling and Simulation in Materials Science and Engineering
Issue Date:
30-Jan-2018
DOI:
10.1088/1361-651x/aaab7a
Type:
Article
ISSN:
0965-0393; 1361-651X
Sponsors:
The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST).
Additional Links:
http://iopscience.iop.org/article/10.1088/1361-651X/aaab7a
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division; Mechanical Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorMora Cordova, Angelen
dc.contributor.authorHan, Feien
dc.contributor.authorLubineau, Gillesen
dc.date.accessioned2018-02-07T07:02:24Z-
dc.date.available2018-02-07T07:02:24Z-
dc.date.issued2018-01-30en
dc.identifier.citationMora A, Han F, lubineau gilles (2018) Computational modeling of electrically conductive networks formed by graphene nanoplatelet-carbon nanotube hybrid particles. Modelling and Simulation in Materials Science and Engineering. Available: http://dx.doi.org/10.1088/1361-651x/aaab7a.en
dc.identifier.issn0965-0393en
dc.identifier.issn1361-651Xen
dc.identifier.doi10.1088/1361-651x/aaab7aen
dc.identifier.urihttp://hdl.handle.net/10754/627050-
dc.description.abstractOne strategy to ensure that nanofiller networks in a polymer composite percolate at low volume fractions is to promote segregation. In a segregated structure, the concentration of nanofillers is kept low in some regions of the sample. In turn, the concentration in remaining regions is much higher than the average concentration of the sample. This selective placement of the nanofillers ensures percolation at low average concentration. One original strategy to promote segregation is by tuning the shape of the nanofillers. We use a computational approach to study the conductive networks formed by hybrid particles obtained by growing carbon nanotubes (CNTs) on graphene nanoplatelets (GNPs). The objective of this study is (1) to show that the higher electrical conductivity of these composites is due to the hybrid particles forming a segregated structure and (2) to understand which parameters defining the hybrid particles determine the efficiency of the segregation. We construct a microstructure to observe the conducting paths and determine whether a segregated structure has indeed been formed inside the composite. A measure of efficiency is presented based on the fraction of nanofillers that contribute to the conductive network. Then, the efficiency of the hybrid-particle networks is compared to those of three other networks of carbon-based nanofillers in which no hybrid particles are used: only CNTs, only GNPs, and a mix of CNTs and GNPs. Finally, some parameters of the hybrid particle are studied: the CNT density on the GNPs, and the CNT and GNP geometries. We also present recommendations for the further improvement of a composite's conductivity based on these parameters.en
dc.description.sponsorshipThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST).en
dc.publisherIOP Publishingen
dc.relation.urlhttp://iopscience.iop.org/article/10.1088/1361-651X/aaab7aen
dc.rightsThis is an author-created, un-copyedited version of an article accepted for publication/published in Modelling and Simulation in Materials Science and Engineering. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://doi.org/10.1088/1361-651x/aaab7aen
dc.subjectGraphene nanoplateleten
dc.subjectCarbon nanotubeen
dc.subjectPolymer compositesen
dc.subjectSegregated structureen
dc.subjectHybrid particleen
dc.subjectElectrical propertiesen
dc.titleComputational modeling of electrically conductive networks formed by graphene nanoplatelet-carbon nanotube hybrid particlesen
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentMechanical Engineering Programen
dc.contributor.departmentCOHMAS Laboratory, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabiaen
dc.identifier.journalModelling and Simulation in Materials Science and Engineeringen
dc.eprint.versionPost-printen
kaust.authorMora Cordova, Angelen
kaust.authorHan, Feien
kaust.authorLubineau, Gillesen
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