Characterizing thermal conduction in polycrystalline graphene

TL;DR

This study combines theoretical modeling and molecular dynamics simulations to understand how microstructural features like grain size and boundary misalignment affect thermal conductivity in polycrystalline graphene.

Contribution

It introduces a comprehensive approach integrating microstructural characterization, simulations, and an effective medium model to predict thermal transport in polycrystalline graphene.

Findings

Thermal conductivity increases with grain size.

Mismatch angle and dislocation density reduce thermal conductivity.

Grain boundary effects weaken the temperature dependence of thermal conductivity.

Abstract

Thermal conduction was explored and discussed through a combined theoretical and simulation approach in this work. The thermal conductivity k of polycrystalline graphene was calculated by molecular dynamics simulations based on a hexagonal patch model in close consistence with microstructural characterization in experiments. The effects of grain size, alignment, and temperature were identified with discussion on the microscopic phonon scattering mechanisms. The effective thermal conductivity is found to increase with the grain size and decrease with the mismatch angle and dislocation density at the grain boundaries. The 1/T temperature dependence of k is significantly weakened in the polycrystals. The effect of grain boundaries in modifying thermal transport properties of graphene was characterized by their effective width and thermal conductivity as an individual phase, which was later included in a predictive effective medium model that showed degraded reduction in thermal conductivity for grain larger than a few microns.