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

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.

Automated summary

The study established a multiscale model to calculate the effective thermal conductivity of graphene and polyamide-6 composite, finding that randomly entangled graphene networks exhibit better thermal conductivity than composites with specific graphene alignment. The results suggest that maximizing composite thermal conductivity can be achieved by producing the optimal orientation distribution for graphene flakes without increasing graphene loading.