Toughness and Strength of Nanocrystalline Graphene

TL;DR

This paper develops a statistical theory to understand the variability in toughness and strength of nanocrystalline graphene, linking nano-scale defects and grain size to mechanical properties.

Contribution

It introduces the first statistical model of toughness in polycrystalline graphene, explaining how nano-scale defects influence its mechanical variability.

Findings

Statistical variation in toughness and strength explained by 'weakest-link' theory.

Grain size significantly affects the mechanical properties of nanocrystalline graphene.

Framework applicable to interpreting experimental results in 2D materials.

Abstract

Pristine monocrystalline graphene is claimed to be the strongest material known with remarkable mechanical and electrical properties. However, graphene made with scalable fabrication techniques is polycrystalline and contains inherent nano-scale line and point defects - grain boundaries and grain-boundary triple junctions - that lead to significant statistical fluctuations in toughness and strength. These fluctuations become particularly pronounced for nanocrystalline graphene where the density of defects is high. Here we use large-scale simulation and continuum modeling to show that the statistical variation in toughness and strength can be understood with 'weakest-link' statistics. We develop the first statistical theory of toughness in polycrystalline graphene, and elucidate the nano-scale origins of the grain-size dependence of its strength and toughness. Our results should lead to more reliable graphene device design, and provide a framework to interpret experimental results in a broad class of 2D materials.