4.7 Article

Mechanism of Graphene Formation via Detonation Synthesis: A DFTB Nanoreactor Approach

Journal

JOURNAL OF CHEMICAL THEORY AND COMPUTATION
Volume 15, Issue 6, Pages 3654-3665

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.9b00158

Keywords

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Funding

  1. National Natural Science Foundation of China [21603252, 21473229, 91545121]
  2. Shanxi Province Science Foundation for Youth [201601D021048]
  3. Shell Global Solutions International B.V
  4. Synfuels China, Co. Ltd.

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With the development of theoretical and computational chemistry, as well as high-performance computing, molecular simulation can now be used not only as a tool to explain the experimental results but also as a means for discovery or prediction. Quantum chemical nanoreactor is such a method which can automatically explore the chemical process based only on the basic mechanics without prior knowledge of the reactions. Here, we present a new method which combines the semiempirical quantum mechanical density functional tight-binding (DFTB) method with the nanoreactor molecular dynamic (NMD) method, and we simulated the reaction process of graphene synthesis via detonation at different oxygen/acetylene mole ratios. The formation of graphene is initiated by the breaking of acetylene (C2H2) molecules by collision into pieces such as H atoms, ethynyl (HC C center dot), and vinylidene (H2C=CO radicals. It is followed by the formation of long straight carbon chains coupled with a few branched carbon chains, which then turned into a 2-D framework made of carbon rings. Trace oxygen could modulate the size of the rings during graphene formation and promote the formation of regular graphene with fused six-membered rings as we see, but the addition of high oxygen content makes more C-containing species oxidized to small oxide molecules instead of polymerization. The calculation speed of the DFTB nanoreactor is greatly improved compared to the ab initio nanoreactor, which makes it a valuable option to simulate chemical processes of large sizes and long time scales and to help us uncover the unknown unknowns.

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