QM/MD studies on graphene growth from small islands on the Ni(111) surface

Menggai Jiao, Wei Song, Hu Jun Qian, Ying Wang, Zhijian Wu, Stephan Irle, Keiji Morokuma

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Quantum chemical molecular dynamics simulations of graphene growth from small island precursors in different carbon nucleation densities on the Ni(111) surface at high temperatures have been conducted. The results indicate that small islands are not static, i.e. lateral diffusion and vertical fluctuation are frequently observed. In the case of low carbon nucleation density, carbon atoms or small carbon patches diffuse and attach to the edge of the nuclei to expand the size of the growing carbon network. The growth of graphene precursors is accompanied by the corresponding changes in the bonding of nickel atoms with the precipitation of subsurface carbon atoms. This is because the carbon-carbon interaction is stronger than the nickel-carbon interaction. In the case of high carbon nucleation densities, the dominant ripening mechanism depends on different growth stages. In the initial stage, the coalescence of carbon islands takes place via the Smoluchowski ripening mechanism. In the later stage the Smoluchowski ripening process is damped owing to the higher diffusion barrier of larger clusters and the restriction of movement by self-assembled nickel step edges. The cross-linking mechanism eventually takes over by the coalescence of extended polyyne chains between graphene islands. In either case, the Ostwald ripening process is not found in our molecular dynamics simulations due to the stability of carbon-carbon bonds within the islands. These investigations should be instructive to the control of graphene growth in experiments.

Original languageEnglish
Pages (from-to)3067-3074
Number of pages8
JournalNanoscale
Volume8
Issue number5
DOIs
StatePublished - Feb 7 2016
Externally publishedYes

Funding

The authors thank the National Natural Science Foundation of China for financial support (Grant No. 21203174, 21503210) and the Natural Science Foundation of Jilin Province (No. 20130522141JH, 20150101012JC). We are grateful to the Computing Center of Jilin Province and the Performance Computing Center of Jilin University for essential support. We also acknowledge the financial support from the Department of Science and Technology of Sichuan Province.

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