Machine Learning Approach to Enable Spectral Imaging Analysis for Particularly Complex Nanomaterial Systems
Author:
Affiliation:
1. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland21218, United States
2. Ralph O’Connor Sustainable Energy Institute, Johns Hopkins University, Baltimore, Maryland21218, United States
Funder
Advanced Research Projects Agency - Energy
Johns Hopkins University
American Chemical Society Petroleum Research Fund
Publisher
American Chemical Society (ACS)
Subject
General Physics and Astronomy,General Engineering,General Materials Science
Link
https://pubs.acs.org/doi/pdf/10.1021/acsnano.2c08884
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1. Modern Trends in Hyperspectral Image Analysis: A Review
2. Medical hyperspectral imaging: a review
3. Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review
4. Hyperspectral imaging for astronomy and space surviellance
5. Direct atomic-level insight into the active sites of a high-performance PGM-free ORR catalyst
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