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Electric-Field Assisted Modulation of Surface Thermochemistry

preprint
submitted on 15.04.2020 and posted on 17.04.2020 by Manish Shetty, Matthew Ardagh, Yutong Pang, Omar Abdelrahman, Paul Dauenhauer

Conventional catalyst design has enhanced reactivity and product selectivity through control of surface thermochemistry by tunable surface composition and the surrounding environment (e.g., pore structure). In this work, the prospect for electric field towards controlling product selectivity and reaction networks on the Pt(111) surface was evaluated with periodic density functional theory (DFT) calculations in concert with machine learning (ML) algorithms. Linear scaling relationships (LSRs) for adsorption energies of surface species in electric field were shown to: (i) be distinct as compared to zero-field LSRs across metals, and (ii) linearly correlate with adsorption energies of H* rather than the binding element. The slope of LSRs linearly correlated with the zero-field dipole moment. A random forest ML regression algorithm predicted field-dependent adsorption energies with a mean absolute error (0.12 eV) comparable to DFT. Overall, this study identifies the path forward for electric field-assisted catalysis, specifically towards catalyst poisoning, product selectivity, and control of reaction pathways.

Funding

Catalysis Center for Energy Innovation(CCEI)

Basic Energy Sciences

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History

Email Address of Submitting Author

hauer@umn.edu

Institution

University of Minnesota

Country

United States of America

ORCID For Submitting Author

0000-0001-5810-1953

Declaration of Conflict of Interest

No conflict of interest

Version Notes

Original Submission - Version 1.0

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