2024-10-09 オークリッジ国立研究所(ORNL)
＜関連情報＞
- https://www.ornl.gov/news/new-technique-could-unlock-potential-quantum-materials
- https://www.science.org/doi/10.1126/sciadv.adn5899
ダイナミックSTEM-EELSによる電子ビーム変換中の単一原子と欠陥の測定 Dynamic STEM-EELS for single-atom and defect measurement during electron beam transformations
Kevin M. Roccapriore, Riccardo Torsi, Joshua Robinson, Sergei Kalinin, and Maxim Ziatdinov
Science Advances Published:17 Jul 2024
DOI:https://doi.org/10.1126/sciadv.adn5899
Abstract
This study introduces the integration of dynamic computer vision–enabled imaging with electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). This approach involves real-time discovery and analysis of atomic structures as they form, allowing us to observe the evolution of material properties at the atomic level, capturing transient states traditional techniques often miss. Rapid object detection and action system enhances the efficiency and accuracy of STEM-EELS by autonomously identifying and targeting only areas of interest. This machine learning (ML)–based approach differs from classical ML in that it must be executed on the fly, not using static data. We apply this technology to V-doped MoS2, uncovering insights into defect formation and evolution under electron beam exposure. This approach opens uncharted avenues for exploring and characterizing materials in dynamic states, offering a pathway to increase our understanding of dynamic phenomena in materials under thermal, chemical, and beam stimuli.
