Issue 43, 2019

Proton irradiation of graphene: insights from atomistic modeling

Abstract

Various types of topological defects are produced during proton irradiation, which are crucial in functionalizing graphene, but the mechanisms of the defect generation process and the structure change are still elusive. Herein, we investigated the graphene defect generation probabilities and defect structures under proton irradiation using both ab initio and classical molecular dynamics simulations. As the proton energy increases from 0.1 keV to 100 keV, defect structures transition from single vacancy and Frenkel pairs to a rich variety of topological defects with the possibility of ejecting multiple atoms. We show that, relatively good agreement on defect generation probabilities can be reached between the two simulation approaches at a proton energy of 1 and 10 keV. However, at 0.1 keV, the single vacancy generation probability differs significantly in two methods due to the difference in the energy required to form single vacancy. Using the classical molecular dynamics simulation, we also studied the evolution of different types of defects and the dependence of their probabilities of occurrence on the proton energy and incident angle. The correlation between the impact positions and defect types allows for the convoluted relationship between the defect probabilities, geometric parameters, and proton energy to be elucidated. We show that the proton energy and incident angle can be used to effectively tune the generation probabilities of different types of defects. Our results provide insights into the controlled defect engineering through ion irradiation, which will be useful for the development of functionalized graphene and graphene electronics.

Graphical abstract: Proton irradiation of graphene: insights from atomistic modeling

Supplementary files

Article information

Article type
Paper
Submitted
30 Jul 2019
Accepted
18 Oct 2019
First published
22 Oct 2019

Nanoscale, 2019,11, 20754-20765

Proton irradiation of graphene: insights from atomistic modeling

T. Shi, Q. Peng, Z. Bai, F. Gao and I. Jovanovic, Nanoscale, 2019, 11, 20754 DOI: 10.1039/C9NR06502D

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