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Publications

Prof. Zonghoon Lee’s Atomic-Scale Electron Microscopy Lab

Publications

Link to Google Scholar


Publications in Nature | Science | their sister journals


Nature, 629, 348-354,2024 /  Nature Communications, 14:4747, 2023 / Nature Communications, 13:4916, 2022 / Nature Communications, 13:2759, 2022 / Nature, 596, 519-524, 2021 Nature, 582, 511-514, 2020 / Nature Nanotechnology, 15, 289-295, 2020 / Nature Nanotechnology, 15, 59-66, 2020 / Science Advances, 6 (10), eaay4958, 2020 / Nature Electronics, 3, 207-215, 2020 / Nature Communications, 11 (1437), 2020 / Nature Energy, 3, 773-782, 2018 / Nature Communications, 8:1549, 2017 / Nature Communications, 6:8294, 2015 / Nature Communications, 6:7817, 2015 / Nature Communications, 5:3383, 2014 






- Selected as Journal Cover of July 2021


Abstract


 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)​2MoS​2 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. 

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2011

Prior to Joining UNIST, 2011

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