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Computational Investigation of Nanoscale Electrocatalysts for Clean Energy Conversion

Citation

Chen, Yalu (2021) Computational Investigation of Nanoscale Electrocatalysts for Clean Energy Conversion. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/tgw8-c485. https://resolver.caltech.edu/CaltechTHESIS:12152020-221422639

Abstract

Electrocatalysis provides a practical solution to the increasing global energy demand while maintaining a sustainable environment. Recently nanoscale catalysts (nanoparticles, nanowires, and dealloyed surfaces) have been shown to have experimentally far superior performance than metallic crystals at sustainable energy conversion. However, the surface feature of these improved catalysts is still unknown, as the detection of the active sites directly from experiment has not been possible.

In this thesis work, we discuss using the quantum mechanics based muitiscale simulations and machine learning to understand the nature of these superior materials. We first studied jagged Pt nanowire (J-PtNW), which was shown to have performance at oxygen reduction reactions (ORR) 50 times better than Pt/C. We used multiscale simulations (reactive force field, and density functional theory) to explain this remarkably accelerated ORR activity from an atomistic perspective. Next, we looked into the irregular gold surfaces and copper surfaces (nanoparticles and dealloyed surfaces), which showed dramatically improved performance at CO2 reduction reactions (CO2RR) and CO reduction reactions (CORR). We developed the strategy to combine the reactive force field, density functional theory, and machine learning to identify the active sites responsible for their improved performance. This approach provided the possibility to understand the highly irregular and disordered surface, which is impossible with surface science experiments or with quantum mechanics. The identification of the active sites provides insights into new design concepts (alloys, NP, NW, and electrolytes such as ionic liquids) aimed at increasing product selectivity and rates simultaneously with reducing energy requirements.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:computation; electroctalysis; clean energy conversion
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Materials Science
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Goddard, William A., III
Thesis Committee:
  • Gray, Harry B. (chair)
  • Fultz, Brent T.
  • Atwater, Harry A.
  • Goddard, William A., III
Defense Date:14 December 2020
Non-Caltech Author Email:chenyalu19940202 (AT) gmail.com
Funders:
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-18-1-2155
Department of Energy (DOE)DE-SC0004993
Record Number:CaltechTHESIS:12152020-221422639
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:12152020-221422639
DOI:10.7907/tgw8-c485
Related URLs:
URLURL TypeDescription
https://doi.org/10.1021/jacs.9b13218DOIArticle adapted for Ch. 2
https://doi.org/10.1021/jacs.9b04956DOIArticle adapted for Ch. 3
https://doi.org/doi/abs/10.1021/acsenergylett.8b01933DOIArticle adapted for Ch. 4
ORCID:
AuthorORCID
Chen, Yalu0000-0002-0589-845X
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14032
Collection:CaltechTHESIS
Deposited By: Yalu Chen
Deposited On:05 Jan 2021 19:27
Last Modified:16 Jan 2021 01:30

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