Multiparameter Machine Learning Quantifies Electronic Dominance in Pd-Catalyzed Formic Acid Dehydrogenation.
basic_science · Level V
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- Record sourced from PubMed, PMID 42274019.
- Also identified by DOI 10.1021/acs.nanolett.6c01270.
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Abstract
Given the limited efficiency of geometric optimization in enhancing formic acid dehydrogenation (FAD), advancing Pd-based catalysts requires deeper insight into electronic structural regulation. Here, we developed a catalytic system confined within metal-nitrogen-doped carbon supports (Pd@MNC) and applied machine learning to innovatively establish a multiparameter correlation model integrating intrinsic kinetic barriers (<i>E</i><sub><i>ads</i></sub>) with diverse descriptors. Unlike traditional single-factor analyses, our findings unravel the central role of electronic structure engineering (48% relative importance) over geometric tunability (12%) in regulating catalytic kinetics, with the d-band center offset (<i>ε</i><sub><i>d</i></sub>, 30%) and Pd(II) proportion (<i>ω</i><sub><i>Pd(II)</i></sub>, 18%) accounting for the electronic contribution. Validated experimentally via Co and Cr doping, this theory-based machine learning framework offers a predictive paradigm for rational catalyst design and activity trend. Ultimately, this multidimensional electronic regulation strategy elevates FAD performance while providing broad applicability for accelerating other critical Pd-catalyzed processes, such as Suzuki coupling and CO<sub>2</sub> reduction reactions.