Toward Controlled Synthesis of 2D Crystals by CVD: Learning from the Real-Time Crystal Morphology Evolutions.
basic_science · Level V
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- Record sourced from PubMed, PMID 38349857.
- Also identified by DOI 10.1021/acs.nanolett.3c04016.
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Abstract
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 MoS<sub>2</sub> 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 MoS<sub>2</sub> 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.