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

Area of Science:

• Materials Science
• Chemical Engineering
• Data Science

Background:

• Quantifying reaction rates at heterogeneous, unstable interfaces is challenging but vital for applications like batteries and electrocatalysts.
• Operando microscopy yields rich image data, but lacks data-driven methods to extract underlying physics due to complex couplings.
• Existing methods struggle with the intricate interplay of reaction kinetics, surface chemistry, and phase separation.

Purpose of the Study:

• To develop a data-driven method for learning heterogeneous reaction kinetics from in situ microscopy images.
• To extract the free-energy landscape and reaction kinetics of materials with complex interfaces.
• To non-destructively characterize and optimize heterogeneous reactive surfaces.

Main Methods:

• Utilized in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles.
• Combined a large STXM image dataset with a thermodynamically consistent electrochemical phase-field model.
• Employed partial differential equation (PDE)-constrained optimization and uncertainty quantification.

Main Results:

• Successfully learned heterogeneous reaction kinetics from STXM images, correlating closely with carbon-coating thickness.
• Extracted the free-energy landscape and reaction kinetics, showing consistency with theoretical models.
• Achieved a mean discrepancy of <7% across 180,000 pixels, comparable to experimental noise.

Conclusions:

• The developed method enables learning nonequilibrium material properties beyond traditional experimental reach.
• Offers a novel, non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces.
• Advances the understanding and engineering of materials for energy storage and catalysis.