Multiscale modeling and maximizing the thermal conductivity of Polyamide-6 reinforced by highly entangled graphene flakes

Abstract

In this work, we have established a multiscale model to accurately calculate the effective thermal conductivity of the composite of graphene and polyamide-6 (PA-6) and use this model to search for the optimal orientation distribution of the graphene flakes to maximize the composite thermal conductivity. Compared with the direct results of large-scale molecular dynamics simulations on the validation case, our model shows 1% relative error for the effective thermal conductance of the standalone graphene network, and 4% for the overall composite thermal conductivity. Counterintuitively, our model predicts that, for the percolation-dominated composite structure, randomly entangled graphene network produces superior thermal conductivity, compared to the composite structure with certain graphene alignment. Our results show that, without increasing graphene loading, the composite thermal conductivity can be maximized by simply producing the optimal orientation distribution for the graphene flakes.