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

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.