摘要
Recent experiments indicate that metal intercalation is a very effective method to manipulate the graphene-adatom interaction and control metal nanostructure formation on graphene. A key question is mass transport, i.e., how atoms deposited uniformly on graphene populate different areas depending on the local intercalation. Using first-principles calculations, we show that partially intercalated graphene, with a mixture of intercalated and pristine areas, can induce an alternating electric field because of the spatial variations in electron doping, and thus, an oscillatory electrostatic potential. This alternating field can change normal stochastic adatom diffusion to biased diffusion, leading to selective mass transport and consequent nucleation, on either the intercalated or pristine areas, depending on the charge state of the adatoms.
Recent experiments indicate that metal intercalation is a very effective method to manipulate the graphene-adatom interaction and control metal nanostructure formation on graphene. A key question is mass transport, i.e., how atoms deposited uniformly on graphene populate different areas depending on the local intercalation. Using first-principles calculations, we show that partially intercalated graphene, with a mixture of intercalated and pristine areas, can induce an alternating electric field because of the spatial variations in electron doping, and thus, an oscillatory electrostatic potential. This alternating field can change normal stochastic adatom diffusion to biased diffusion, leading to selective mass transport and consequent nucleation, on either the intercalated or pristine areas, depending on the charge state of the adatoms.
基金
Acknowledgements We thank Dr. Jim Evans for many useful discussions. Work at Ames Laboratory was supported by the U.S. Department of Energy, Basic Energy Sciences, Division of Materials Science and Engineering, including a grant of computer time at the National Energy Research Scientific Computing Centre （NERSC） in Berkeley, CA under Contract No. DE-AC02-07CH11358. X. J. L. also acknowledges the support by the National Natural Science Foundation of China （No. 11574044） and Science and Technology Department of Jilin Province （No. 20150520088JH）. H. Q. L. acknowledges support from National Natural Science Foundation of China （No. U1530401） and computational resource from the Beijing Computational Science Research Center.
