Data-driven insights into protonic-ceramic fuel cell and electrolysis performance
Cell reproducibility remains a significant challenge for emerging proton-conducting ceramic electrochemical fuel cell and electrolyzer technologies. This study investigates the factors contributing to cell-to-cell performance variation. Gaussian process and random forest regressor machine learning m...
| Main Authors: | , , , , , , , , , , |
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| Format: | Journal Article |
| Published: |
2025
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| Online Access: | http://hdl.handle.net/20.500.11937/97513 |