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

Autor: Zhao H; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA., Deng HD; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA., Cohen AE; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA., Lim J; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA., Li Y; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA., Fraggedakis D; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA., Jiang B; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA., Storey BD; Toyota Research Institute, Cambridge, MA, USA., Chueh WC; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.; Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA., Braatz RD; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA., Bazant MZ; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. bazant@mit.edu.; Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA. bazant@mit.edu.
Jazyk: angličtina
Zdroj: Nature [Nature] 2023 Sep; Vol. 621 (7978), pp. 289-294. Date of Electronic Publication: 2023 Sep 13.
DOI: 10.1038/s41586-023-06393-x
Abstrakt: Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries 1 and electrocatalysts 2 . Experimental characterizations of such materials by operando microscopy produce rich image datasets 3-6 , 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 separation 7 . 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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