Decoding reactive structures in dilute alloy catalysts

Nicholas Marcella, Jin Soo Lim, Anna M. Płonka, George Yan, Cameron J. Owen, Jessi E. S. Hoeven, Alexandre C. Foucher, Hio Tong Ngan, Steven B. Torrisi, Nebojsa S. Marinkovic, Eric A. Stach, Jason F. Weaver, Joanna Aizenberg, Philippe Sautet, Boris Kozinsky () and Anatoly I. Frenkel ()
Additional contact information
Nicholas Marcella: Stony Brook University
Jin Soo Lim: Harvard University
Anna M. Płonka: Stony Brook University
George Yan: University of California, Los Angeles
Cameron J. Owen: Harvard University
Jessi E. S. Hoeven: Harvard University
Alexandre C. Foucher: University of Pennsylvania
Hio Tong Ngan: University of California, Los Angeles
Steven B. Torrisi: Harvard University
Nebojsa S. Marinkovic: Columbia University
Eric A. Stach: University of Pennsylvania
Jason F. Weaver: University of Florida
Joanna Aizenberg: Harvard University
Philippe Sautet: University of California, Los Angeles
Boris Kozinsky: Harvard University
Anatoly I. Frenkel: Stony Brook University

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract Rational catalyst design is crucial toward achieving more energy-efficient and sustainable catalytic processes. Understanding and modeling catalytic reaction pathways and kinetics require atomic level knowledge of the active sites. These structures often change dynamically during reactions and are difficult to decipher. A prototypical example is the hydrogen-deuterium exchange reaction catalyzed by dilute Pd-in-Au alloy nanoparticles. From a combination of catalytic activity measurements, machine learning-enabled spectroscopic analysis, and first-principles based kinetic modeling, we demonstrate that the active species are surface Pd ensembles containing only a few (from 1 to 3) Pd atoms. These species simultaneously explain the observed X-ray spectra and equate the experimental and theoretical values of the apparent activation energy. Remarkably, we find that the catalytic activity can be tuned on demand by controlling the size of the Pd ensembles through catalyst pretreatment. Our data-driven multimodal approach enables decoding of reactive structures in complex and dynamic alloy catalysts.

Date: 2022
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DOI: 10.1038/s41467-022-28366-w

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