TY - JOUR
T1 - Dynamic STEM-EELS for single-atom and defect measurement during electron beam transformations
AU - Roccapriore, Kevin M.
AU - Torsi, Riccardo
AU - Robinson, Joshua
AU - Kalinin, Sergei
AU - Ziatdinov, Maxim
N1 - Publisher Copyright:
© 2024 The Authors, some rights reserved.
PY - 2024/7/19
Y1 - 2024/7/19
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85199126362&partnerID=8YFLogxK
U2 - 10.1126/sciadv.adn5899
DO - 10.1126/sciadv.adn5899
M3 - Article
C2 - 39018401
AN - SCOPUS:85199126362
SN - 2375-2548
VL - 10
JO - Science Advances
JF - Science Advances
IS - 29
M1 - eadn5899
ER -