Localized electronic states at grain boundaries on the surface of graphene and graphite

TL;DR

This study investigates how grain boundaries in graphene and graphite influence local electronic states, revealing that their atomic structure causes distinct electronic resonances which affect material transport properties.

Contribution

The paper provides atomic-scale characterization of grain boundary electronic states using STM/STS and simulations, linking structure to electronic behavior in graphene and graphite.

Findings

Grain boundaries exhibit localized electronic resonances.

Resonance types depend on crystallite orientation.

Electronic features influence transport anisotropy.

Abstract

Recent advances in large-scale synthesis of graphene and other 2D materials have underscored the importance of local defects such as dislocations and grain boundaries (GBs), and especially their tendency to alter the electronic properties of the material. Understanding how the polycrystalline morphology affects the electronic properties is crucial for the development of applications such as flexible electronics, energy harvesting devices or sensors. We here report on atomic scale characterization of several GBs and on the structural-dependence of the localized electronic states in their vicinity. Using low temperature scanning tunneling microscopy (STM) and spectroscopy (STS), together with tight binding and ab initio numerical simulations we explore GBs on the surface of graphite and elucidate the interconnection between the local density of states (LDOS) and their atomic structure. We show that the electronic fingerprints of these GBs consist of pronounced resonances which, depending on the relative orientation of the adjacent crystallites, appear either on the electron side of the spectrum or as an electron-hole symmetric doublet close to the charge neutrality point. These two types of spectral features will impact very differently the transport properties allowing, in the asymmetric case to introduce transport anisotropy which could be utilized to design novel growth and fabrication strategies to control device performance.