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In Situ Scanning Transmission Electron Microscopy Study of MoS(2) Formation on Graphene with a Deep-Learning Framework

[Image: see text] Atomic-scale information is essential for understanding and designing unique structures and properties of two-dimensional (2D) materials. Recent developments in in situ transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) enable research to pr...

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Detalles Bibliográficos
Autores principales: Lee, Yeongdong, Lee, Jongyeong, Chung, Handolsam, Kim, Jaemin, Lee, Zonghoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8388093/
https://www.ncbi.nlm.nih.gov/pubmed/34471766
http://dx.doi.org/10.1021/acsomega.1c03002
Descripción
Sumario:[Image: see text] Atomic-scale information is essential for understanding and designing unique structures and properties of two-dimensional (2D) materials. Recent developments in in situ transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) enable research to provide abundant insights into the growth of nanomaterials. In this study, 2D MoS(2) is synthesized on a suspended graphene substrate inside a TEM column through thermolysis of the ammonium tetrathiomolybdate (NH(4))(2)MoS(4) precursor at 500 °C. To avoid misinterpretation of the in situ STEM images, a deep-learning framework, DeepSTEM, is developed. The DeepSTEM framework successfully reconstructs an object function in atomic-resolution STEM imaging for accurate determination of the atomic structure and dynamic analysis. In situ STEM imaging with DeepSTEM enables observation of the edge configuration, formation, and reknitting progress of MoS(2) clusters with the formation of a mirror twin boundary. The synthesized MoS(2)/graphene heterostructure shows various twist angles, as revealed by atomic-resolution TEM. This deep-learning framework-assisted in situ STEM imaging provides atomic information for in-depth studies on the growth and structure of 2D materials and shows the potential use of deep-learning techniques in 2D material research.