化学气相沉积
形态学(生物学)
单层
材料科学
纳米技术
Crystal(编程语言)
特征(语言学)
晶体生长
人工智能
结晶学
生物系统
化学
计算机科学
地质学
程序设计语言
古生物学
语言学
哲学
生物
作者
Jing Zhang,Tianyu Zhai,Faizal Arifurrahman,Yuguo Wang,Andrew Hitt,Zelai He,Qing Ai,Yifeng Liu,Chen‐Yang Lin,Yifan Zhu,Ming Tang,Jun Lou
出处
期刊:Nano Letters
[American Chemical Society]
日期:2024-02-13
卷期号:24 (8): 2465-2472
标识
DOI:10.1021/acs.nanolett.3c04016
摘要
The rich morphology of 2D materials grown through chemical vapor deposition (CVD), is a distinctive feature. However, understanding the complex growth of 2D crystals under practical CVD conditions remains a challenge due to various intertwined factors. Real-time monitoring is crucial to providing essential data and enabling the use of advanced tools like machine learning for unraveling these complexities. In this study, we present a custom-built miniaturized CVD system capable of observing and recording 2D MoS2 crystal growth in real time. Image processing converts the real-time footage into digital data, and machine learning algorithms (ML) unveil the significant factors influencing growth. The machine learning model successfully predicts CVD growth parameters for synthesizing ultralarge monolayer MoS2 crystals. It also demonstrates the potential to reverse engineer CVD growth parameters by analyzing the as-grown 2D crystal morphology. This interdisciplinary approach can be integrated to enhance our understanding of controlled 2D crystal synthesis through CVD.
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