Deciphering electrocatalysts with multimodal operando approaches
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Abstract
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Research context
OpenAlex topics: Machine Learning in Materials Science · Electrocatalysts for Energy Conversion · Advanced Memory and Neural Computing
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References (10)
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1. Roldán Cuenya, B. & Bañares, M. A. Chem. Rev. 124, 8011–8013 (2024).10.1021/acs.chemrev.4c00184 ↗
2. Frey, H., Beck, A., Huang, X., van Bokhoven, J. A. & Willinger, M. G. Science 376, 982–987 (2022).10.1126/science.abm3371 ↗
3. Zhang, Q. et al. Nature 630, 643–647 (2024).10.1038/s41586-024-07479-w ↗
4. Yang, Y. et al. Nature 614, 262–269 (2023).10.1038/s41586-022-05540-0 ↗
5. Amirbeigiarab, R. et al. Nat. Catal. 6, 837–846 (2023).10.1038/s41929-023-01009-z ↗
6. Zheng, W., Lee, D. & Zheng, H. MRS Bull. 49, 205–213 (2024).10.1557/s43577-024-00661-5 ↗
7. Yoon, A. et al. Nat. Mater. https://doi.org/10.1038/s41563-024-02084-8 (2025).10.1038/s41563-024-02084-8 ↗
8. National Science and Technology Council Materials Genome Initiative Strategic Plan (US Government, 2021).
9. A Revolution in Digital Manufacturing: Integrating Machines, Robotics, Artificial Intelligence, and Forming Technologies (The Minerals, Metals & Materials Society, 2024).
10. Wilkinson, M. D. et al. Sci. Data 3, 160018 (2016).10.1038/sdata.2016.18 ↗