Abstract
Graphene is a 2-dimensional allotrope of carbon with remarkable physicochemical properties. Currently, the most promising route for commercial synthesis of graphene for technological application is chemical vapor deposition (CVD). The optimization of this chemical process will potentially enable control over crucial properties, such as graphene quality and domain size. Such optimization requires a detailed atomistic understanding of how graphene nucleation and growth take place during CVD. This mechanism depends on a multitude of synthetic parameters: temperature, CVD pressure, catalyst type, facet and phase, feedstock type, and the presence of chemical etchants, to name only a few. In this feature article, we highlight recent quantum chemical simulations of chemical vapor deposition (CVD) graphene nucleation and growth. These simulations aim to systematically span this complex CVD "parameter space" toward providing the necessary understanding of graphene nucleation, to assist the optimization of CVD graphene growth.
| Original language | English |
|---|---|
| Pages (from-to) | 13851-13864 |
| Number of pages | 14 |
| Journal | Journal of Physical Chemistry C |
| Volume | 120 |
| Issue number | 26 |
| DOIs | |
| State | Published - Jul 7 2016 |
| Externally published | Yes |
Funding
AJP, SI, and KM acknowledge support from the Australian Research Council (ARC DP140102894) and the Japan Society for the Promotion of Science (Open Partnership 13039901- 000174). SI and AJP acknowledge support by the JSPS Sakura program for bilateral researcher exchange. This work was in part supported by two CREST (Core Research for Evolutional Science and Technology) grants to KM from JST. IM acknowledges an Australian Postgraduate Award. YW acknowledges the National Natural Science Foundation of China for financial support (Grant No. 21203174) and the Natural Science Foundation of Jilin Province (No.20130522141JH, 20150101012JC). YW is grateful to the Computing Center of Jilin Province and the Performance Computing Center of Jilin University for essential support. YW also acknowledges the financial support from the Department of Science and Technology of Sichuan Province. HBL acknowledges the Natural Science Foundation of Shandong Province, China (Grant No.ZR2014BQ015), and the National Natural Science Foundation of China (21403127). We are grateful for generous supercomputer time at the Institute for Molecular Science (IMS) in Okazaki, Japan, and at the National Computational Infrastructure (NCI), which is supported by the Australian Government.