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.
Marcella, Nicholas, Lim, Jin Soo, Płonka, Anna M., Yan, George, Owen, Cameron J., van der Hoeven, Jessi E. S., Foucher, Alexandre C., Ngan, Hio Tong, Torrisi, Steven B., Marinkovic, Nebojsa S., Stach, Eric A., Weaver, Jason F., Aizenberg, Joanna, Sautet, Philippe, Kozinsky, Boris, & Frenkel, Anatoly I. (2022). Decoding reactive structures in dilute alloy catalysts. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-28366-w
Marcella, Nicholas, Lim, Jin Soo, Płonka, Anna M., et al., "Decoding reactive structures in dilute alloy catalysts," Nature Communications 13, no. 1 (2022), https://doi.org/10.1038/s41467-022-28366-w
@article{osti_1846156,
author = {Marcella, Nicholas and Lim, Jin Soo and Płonka, Anna M. and Yan, George and Owen, Cameron J. and van der Hoeven, Jessi E. S. and Foucher, Alexandre C. and Ngan, Hio Tong and Torrisi, Steven B. and Marinkovic, Nebojsa S. and others},
title = {Decoding reactive structures in dilute alloy catalysts},
annote = {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.},
doi = {10.1038/s41467-022-28366-w},
url = {https://www.osti.gov/biblio/1846156},
journal = {Nature Communications},
issn = {ISSN 2041-1723},
number = {1},
volume = {13},
place = {United States},
publisher = {Nature Publishing Group},
year = {2022},
month = {02}}
Personick, Michelle L.; Montemore, Matthew M.; Kaxiras, Efthimios
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 374, Issue 2061https://doi.org/10.1098/rsta.2015.0077