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Decoding reactive structures in dilute alloy catalysts

Author

Listed:
  • 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
    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
    Harvard University)

  • Philippe Sautet

    (University of California, Los Angeles
    University of California, Los Angeles)

  • Boris Kozinsky

    (Harvard University
    Robert Bosch LLC, Research and Technology Center)

  • Anatoly I. Frenkel

    (Stony Brook University
    Brookhaven National Laboratory)

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.

Suggested Citation

  • 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 , 2022. "Decoding reactive structures in dilute alloy catalysts," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28366-w
    DOI: 10.1038/s41467-022-28366-w
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    References listed on IDEAS

    as
    1. Kangyin Dong & Xiucheng Dong & Qingzhe Jiang, 2020. "How renewable energy consumption lower global CO2 emissions? Evidence from countries with different income levels," The World Economy, Wiley Blackwell, vol. 43(6), pages 1665-1698, June.
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    Cited by:

    1. Cameron J. Owen & Yu Xie & Anders Johansson & Lixin Sun & Boris Kozinsky, 2024. "Low-index mesoscopic surface reconstructions of Au surfaces using Bayesian force fields," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Prahlad K. Routh & Evgeniy Redekop & Sebastian Prodinger & Jessi E. S. Hoeven & Kang Rui Garrick Lim & Joanna Aizenberg & Maarten Nachtegaal & Adam H. Clark & Anatoly I. Frenkel, 2024. "Restructuring dynamics of surface species in bimetallic nanoparticles probed by modulation excitation spectroscopy," Nature Communications, Nature, vol. 15(1), pages 1-10, December.

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