Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel
Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries1 and electrocatalysts2. Experimental characterizations of such materials by operando microscopy produce rich image datasets3...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2024
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| Online Access: | https://hdl.handle.net/1721.1/157426 |
| _version_ | 1824458004997603328 |
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| author | Zhao, Hongbo Deng, Haitao Dean Cohen, Alexander E Lim, Jongwoo Li, Yiyang Fraggedakis, Dimitrios Jiang, Benben Storey, Brian D Chueh, William C Braatz, Richard D Bazant, Martin Z |
| author2 | Massachusetts Institute of Technology. Department of Chemical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Chemical Engineering Zhao, Hongbo Deng, Haitao Dean Cohen, Alexander E Lim, Jongwoo Li, Yiyang Fraggedakis, Dimitrios Jiang, Benben Storey, Brian D Chueh, William C Braatz, Richard D Bazant, Martin Z |
| author_sort | Zhao, Hongbo |
| collection | MIT |
| description | Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries1 and electrocatalysts2. Experimental characterizations of such materials by operando microscopy produce rich image datasets3,4,5,6, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation7. Here we show that heterogeneous reaction kinetics can be learned from in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, partial differential equation (PDE)-constrained optimization and uncertainty quantification, we extract the free-energy landscape and reaction kinetics and verify their consistency with theoretical models. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through Auger electron microscopy (AEM). Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (<7%) and comparable with experimental noise. Our results open the possibility of learning nonequilibrium material properties beyond the reach of traditional experimental methods and offer a new non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces. |
| first_indexed | 2025-02-19T04:19:00Z |
| format | Article |
| id | mit-1721.1/157426 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:19:00Z |
| publishDate | 2024 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1574262024-10-26T03:54:10Z Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel Zhao, Hongbo Deng, Haitao Dean Cohen, Alexander E Lim, Jongwoo Li, Yiyang Fraggedakis, Dimitrios Jiang, Benben Storey, Brian D Chueh, William C Braatz, Richard D Bazant, Martin Z Massachusetts Institute of Technology. Department of Chemical Engineering Massachusetts Institute of Technology. Department of Mathematics Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries1 and electrocatalysts2. Experimental characterizations of such materials by operando microscopy produce rich image datasets3,4,5,6, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation7. Here we show that heterogeneous reaction kinetics can be learned from in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, partial differential equation (PDE)-constrained optimization and uncertainty quantification, we extract the free-energy landscape and reaction kinetics and verify their consistency with theoretical models. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through Auger electron microscopy (AEM). Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (<7%) and comparable with experimental noise. Our results open the possibility of learning nonequilibrium material properties beyond the reach of traditional experimental methods and offer a new non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces. 2024-10-25T18:26:08Z 2024-10-25T18:26:08Z 2023-09-14 2024-10-25T18:20:00Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/157426 Zhao, H., Deng, H.D., Cohen, A.E. et al. Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel. Nature 621, 289–294 (2023). en 10.1038/s41586-023-06393-x Nature Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Springer Science and Business Media LLC |
| spellingShingle | Zhao, Hongbo Deng, Haitao Dean Cohen, Alexander E Lim, Jongwoo Li, Yiyang Fraggedakis, Dimitrios Jiang, Benben Storey, Brian D Chueh, William C Braatz, Richard D Bazant, Martin Z Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel |
| title | Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel |
| title_full | Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel |
| title_fullStr | Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel |
| title_full_unstemmed | Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel |
| title_short | Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel |
| title_sort | learning heterogeneous reaction kinetics from x ray videos pixel by pixel |
| url | https://hdl.handle.net/1721.1/157426 |
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