Domain coarsening in polycrystalline graphene

Author:

Zihua Liu, Debabrata Panja, Gerard T. Barkema

Keyword:

Condensed Matter, Disordered Systems and Neural Networks, Disordered Systems and Neural Networks (cond-mat.dis-nn), Statistical Mechanics (cond-mat.stat-mech)

journal:

date:

2023-11-07 16:00:00

Abstract

Graphene is a two-dimensional carbon material which exhibits exceptional properties, making it highly suitable for a wide range of applications. Practical graphene fabrication often yields a polycrystalline structure with many inherent defects, which significantly influence its performance. In this study, we utilize a Monte Carlo approach based on the optimized Wooten, Winer and Weaire (WWW) algorithm to simulate the crystalline domain coarsening process of polycrystalline graphene. Our sample configurations show excellent agreement with experimental data. We conduct statistical analyses of the bond and angle distribution, temporal evolution of the defect distribution, and spatial correlation of the lattice orientation that follows a stretched exponential distribution. Furthermore, we thoroughly investigate the diffusion behavior of defects and find that the changes in domain size follow a power-law distribution. We briefly discuss the possible connections of these results to (and differences from) domain growth processes in other statistical models, such as the Ising dynamics. We also examine the impact of buckling of polycrystalline graphene on the crystallization rate under substrate effects. Our findings may offer valuable guidance and insights for both theoretical investigations and experimental advancements.

PDF: Domain coarsening in polycrystalline graphene.pdf