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Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel

Author

Listed:
  • Hongbo Zhao

    (Massachusetts Institute of Technology)

  • Haitao Dean Deng

    (Stanford University)

  • Alexander E. Cohen

    (Massachusetts Institute of Technology)

  • Jongwoo Lim

    (Stanford University)

  • Yiyang Li

    (Stanford University)

  • Dimitrios Fraggedakis

    (Massachusetts Institute of Technology)

  • Benben Jiang

    (Massachusetts Institute of Technology)

  • Brian D. Storey

    (Toyota Research Institute)

  • William C. Chueh

    (Stanford University
    SLAC National Accelerator Laboratory)

  • Richard D. Braatz

    (Massachusetts Institute of Technology)

  • Martin Z. Bazant

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

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 datasets3–6, 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 (

Suggested Citation

  • Hongbo Zhao & Haitao Dean Deng & Alexander E. Cohen & Jongwoo Lim & Yiyang Li & Dimitrios Fraggedakis & Benben Jiang & Brian D. Storey & William C. Chueh & Richard D. Braatz & Martin Z. Bazant, 2023. "Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel," Nature, Nature, vol. 621(7978), pages 289-294, September.
  • Handle: RePEc:nat:nature:v:621:y:2023:i:7978:d:10.1038_s41586-023-06393-x
    DOI: 10.1038/s41586-023-06393-x
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    Cited by:

    1. Chuanlai Liu & Franz Roters & Dierk Raabe, 2024. "Role of grain-level chemo-mechanics in composite cathode degradation of solid-state lithium batteries," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    2. Qitao Chen & Baodong Mao & Yanhong Liu & Yunjie Zhou & Hui Huang & Song Wang & Longhua Li & Wei-Cheng Yan & Weidong Shi & Zhenhui Kang, 2024. "Designing 2D carbon dot nanoreactors for alcohol oxidation coupled with hydrogen evolution," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Shobhan Dhir & John Cattermull & Ben Jagger & Maximilian Schart & Lorenz F. Olbrich & Yifan Chen & Junyi Zhao & Krishnakanth Sada & Andrew Goodwin & Mauro Pasta, 2024. "Characterisation and modelling of potassium-ion batteries," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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