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Navigating the Nanoworld: Automatic Feature Recognition

Published online by Cambridge University Press:  22 July 2022

Stephen J. Pennycook*
Affiliation:
Dept. of Materials Science and Engineering, University of Tennessee, Knoxville, TN, USA School of Physical Sciences and CAS Key Laboratory of Vacuum Sciences, University of Chinese Academy of Sciences, Beijing, China
Jiadong Dan
Affiliation:
Graduate School for Integrative Sciences and Engineering, National University of Singapore Department of Materials Science and Engineering, National University of Singapore NUS Centre for Bioimaging Sciences, National University of Singapore
Xiaoxu Zhao
Affiliation:
School of Materials Science and Engineering, Nanyang Technological University, Singapore
Shoucong Ning
Affiliation:
Department of Materials Science and Engineering, National University of Singapore
Wu Zhou
Affiliation:
School of Physical Sciences and CAS Key Laboratory of Vacuum Sciences, University of Chinese Academy of Sciences, Beijing, China
Qian He
Affiliation:
Department of Materials Science and Engineering, National University of Singapore
N. Duane Loh
Affiliation:
NUS Centre for Bioimaging Sciences, National University of Singapore Department of Physics, National University of Singapore Department of Biological Sciences, National University of Singapore
*
*Corresponding author: stephen.pennycook@cantab.net

Abstract

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Type
Beyond Visualization with In Situ and Operando TEM
Copyright
Copyright © Microscopy Society of America 2022

References

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Dan, J., et al. , Science Advances, accepted (2022).Google Scholar
S. J. P. acknowledges funding from Singapore Ministry of Education Tier 1 grant R-284-000-172-114, Tier 2 grant R-284-000-175-112. N.D.L acknowledges funding support from the Singapore National Research Foundation (grant number NRF-CRP16-2015-05) and a NUS Early Career award (A-0004744-00-00).Google Scholar