Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel
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s41586-023-06393-x.pdf
Description
Published version
Size
5.89 MB
Format
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Checksum (MD5)
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Author(s)
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
Date Issued
September 14, 2023
Journal
Nature
Publisher
Springer Science and Business Media LLC
Citation
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).
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Mathematics
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DOI of Published Version
https://doi.org/10.1038/s41586-023-06393-x
