Fractal-Theory-Based Control of the Shape and Quality of CVD-Grown 2D Materials

被引:68
作者
Li, Junzhu [1 ,2 ]
Chen, Mingguang [1 ]
Zhang, Chenhui [1 ]
Dong, Haocong [2 ]
Lin, Weiyi [3 ]
Zhuang, Pingping [3 ]
Wen, Yan [1 ]
Tian, Bo [1 ,2 ]
Cai, Weiwei [3 ]
Zhang, Xixiang [1 ]
机构
[1] KAUST, Phys Sci & Engn Div, Thuwal 239556900, Saudi Arabia
[2] Eleven Dimens Nanomat Res Inst, Xiamen 361000, Fujian, Peoples R China
[3] Xiamen Univ, Dept Phys, State Key Lab Phys Chem, Xiamen 361005, Fujian, Peoples R China
基金
中国国家自然科学基金;
关键词
2D materials; CVD growth; fractal theory; shape control; DIFFUSION-LIMITED AGGREGATION; CHEMICAL-VAPOR-DEPOSITION; SINGLE-CRYSTAL GRAPHENE; FIELD; OXYGEN;
D O I
10.1002/adma.201902431
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The precise control of the shape and quality of 2D materials during chemical vapor deposition (CVD) processes remains a challenging task, due to a lack of understanding of their underlying growth mechanisms. The existence of a fractal-growth-based mechanism in the CVD synthesis of several 2D materials is revealed, to which a modified traditional fractal theory is applied in order to build a 2D diffusion-limited aggregation (2D-DLA) model based on an atomic-scale growth mechanism. The strength of this model is validated by the perfect correlation between theoretically simulated data, predicted by 2D-DLA, and experimental results obtained from the CVD synthesis of graphene, hexagonal boron nitride, and transition metal dichalcogenides. By applying the 2D-DLA model, it is also discovered that the single-domain net growth rate (SD-NGR) plays a crucial factor in governing the shape and quality of 2D-material crystals. By carefully tuning SD-NGR, various fractal-morphology high-quality single-crystal 2D materials are synthesized, achieving, for the first time, the precise control of 2D-material CVD growth. This work lays the theoretical foundation for the precise adjustment of the morphologies and physical properties of 2D materials, which is essential to the use of fractal-shaped nanomaterials for the fabrication of new-generation neural-network nanodevices.
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页数:7
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