Disclosing Pt/CeO2 vibrational and electronic properties by Machine Learning-augmented DRIFT/XAS

We propose combined operando DRIFTS/XAS studies on Pt/CeO₂ catalysts to correlate surface vibrational fingerprints with bulk electronic and structural changes during calcination, reduction, CO treatment, and CO oxidation. Machine learning will be applied to identify hidden correlations between IR and XAS features, enabling predictive models that transfer synchrotron-level insights to laboratory IR spectroscopy. This approach will deliver unprecedented mechanistic understanding and a broadly applicable framework for catalysis.