Energetics and kinetics of vacancies in monolayer graphene boron nitride heterostructures

TL;DR

This study uses first-principle calculations to analyze how vacancies behave energetically and kinetically at interfaces in monolayer graphene-boron nitride heterostructures, providing data crucial for defect engineering in nanoelectronic devices.

Contribution

It offers new insights into vacancy energetics and migration at interfaces in GPBN heterostructures, highlighting the importance of interfacial effects and diffusion parameters for device design.

Findings

Interfaces are preferred sites for vacancy segregation.

Vacancy migration barriers are significantly affected by interfaces.

Dissimilar diffusion prefactors influence vacancy diffusion coefficients.

Abstract

Graphene and boron nitride (GPBN) heterostructures provide a viable way to realize tunable bandgap, promising new opportunities in graphene-based nanoelectronic and optoelectronic devices. In the present study, we investigated the interplay between vacancies and graphene/h-BN interfaces in monolayer GPBN heterostructures. The energetics and kinetics of monovacancies and divacancies in monolayer GPBN heterostructures were examined using first-principle calculations. The interfaces were shown to be preferential locations for vacancy segregation. Meanwhile the kinetics of vacancies was found to be noticeably modified at interfaces, evidenced by the Minimum Energy Paths (MEPs) and associated migration barriers calculations. The role of interfacial bonding configurations, energy states and polarization on the formation and diffusion of vacancies were discussed. Additionally we demonstrated that it is important to recognize the dissimilarities in the diffusion prefactor for different vacancies for accurate determination of the vacancy diffusion coefficient. Our results provide essential data for the modeling of vacancies in GPBN heterostructures, and important insights towards the precise engineering of defects, interfaces and quantum domains in the design of GPBN-based devices.