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Autobifunctional Mechanism of Jagged Pt Nanowires for Hydrogen Evolution Kinetics via End-to-End Simulation

Gu, Geun Ho and Lim, Juhyung and Wan, Chengzhang and Cheng, Tao and Pu, Heting and Kim, Sungwon and Noh, Juhwan and Choi, Changhyeok and Kim, Juhwan and Goddard, William A., III and Duan, Xiangfeng and Jung, Yousung (2021) Autobifunctional Mechanism of Jagged Pt Nanowires for Hydrogen Evolution Kinetics via End-to-End Simulation. Journal of the American Chemical Society, 143 (14). pp. 5355-5363. ISSN 0002-7863. doi:10.1021/jacs.0c11261. https://resolver.caltech.edu/CaltechAUTHORS:20210318-120121922

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Abstract

The extraordinary mass activity of jagged Pt nanowires can substantially improve the economics of the hydrogen evolution reaction (HER). However, it is a great challenge to fully unveil the HER kinetics driven by the jagged Pt nanowires with their multiscale morphology. Herein we present an end-to-end framework that combines experiment, machine learning, and multiscale advances of the past decade to elucidate the HER kinetics catalyzed by jagged Pt nanowires under alkaline conditions. The bifunctional catalysis conventionally refers to the synergistic increase in activity by the combination of two different catalysts. We report that monometals, such as jagged Pt nanowires, can exhibit bifunctional characteristics owing to its complex surface morphology, where one site prefers electrochemical proton adsorption and another is responsible for activation, resulting in a 4-fold increase in the activity. We find that the conventional design guideline that the sites with a 0 eV Gibbs free energy of adsorption are optimal for HER does not hold under alkaline conditions, and rather, an energy between −0.2 and 0.0 eV is shown to be optimal. At the reaction temperatures, the high activity arises from low-coordination-number (≤7) Pt atoms exposed by the jagged surface. Our current demonstration raises an emerging prospect to understand highly complex kinetic phenomena on the nanoscale in full by implementing end-to-end multiscale strategies.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1021/jacs.0c11261DOIArticle
https://github.com/kaist-amsg/JPtNW_Manuscript2021Related ItemData
ORCID:
AuthorORCID
Cheng, Tao0000-0003-4830-177X
Noh, Juhwan0000-0003-1183-9955
Goddard, William A., III0000-0003-0097-5716
Duan, Xiangfeng0000-0002-4321-6288
Jung, Yousung0000-0003-2615-8394
Additional Information:© 2021 American Chemical Society. Received: October 26, 2020; Publication Date: March 17, 2021. G.G. is grateful to Dr. Marcel P. Núñez for the useful discussion of the kinetic Monte Carlo simulation. Y.J. acknowledges financial support from the National Research Foundation of Korea (NRF-2017R1A2B3010176 and NRF-2020M3E6A1044370). X.D. gratefully acknowledges support from the National Science Foundation under grant no. NSF 1800580. W.A.G. thanks ONR (N00014-18-1-2155) and NSF (CBET-2005250) for support. T.C. acknowledges financial support from the National Natural Science Foundation of China (Grant No. 2190030094) and the Natural Science Foundation of Jiangsu Province (Grant No. SBK20190810). All data used to generate the results in this paper can be found at https://github.com/kaist-amsg/JPtNW_Manuscript2021. The repository includes the binding energy data for training machine-learning models, ACSF model parameters, and the structures of CEP DFT calculations. The authors declare no competing financial interest.
Funders:
Funding AgencyGrant Number
National Research Foundation of KoreaNRF-2017R1A2B3010176
National Research Foundation of KoreaNRF-2020M3E6A1044370
NSFCHE-1800580
Office of Naval Research (ONR)N00014-18-1-2155
NSFCBET-2005250
National Natural Science Foundation of China2190030094
Natural Science Foundation of Jiangsu ProvinceSBK20190810
Issue or Number:14
DOI:10.1021/jacs.0c11261
Record Number:CaltechAUTHORS:20210318-120121922
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210318-120121922
Official Citation:Autobifunctional Mechanism of Jagged Pt Nanowires for Hydrogen Evolution Kinetics via End-to-End Simulation. Geun Ho Gu, Juhyung Lim, Chengzhang Wan, Tao Cheng, Heting Pu, Sungwon Kim, Juhwan Noh, Changhyeok Choi, Juhwan Kim, William A. Goddard, Xiangfeng Duan, and Yousung Jung. Journal of the American Chemical Society 2021 143 (14), 5355-5363; DOI: 10.1021/jacs.0c11261
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108485
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:19 Mar 2021 02:18
Last Modified:16 Apr 2021 22:43

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