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Efficient Discovery of Active, Selective, and Stable Catalysts for Electrochemical H2O2 Synthesis through Active Motif Screening
- Title
- Efficient Discovery of Active, Selective, and Stable Catalysts for Electrochemical H2O2 Synthesis through Active Motif Screening
- Authors
- Back, Seoin; Na, Jonggeol; Ulissi, Zachary W.
- Ewha Authors
- 나종걸
- SCOPUS Author ID
- 나종걸
- Issue Date
- 2021
- Journal Title
- ACS CATALYSIS
- ISSN
- 2155-5435
- Citation
- ACS CATALYSIS vol. 11, no. 5, pp. 2483 - 2491
- Keywords
- density functional theory calculations; high-throughput screening; hydrogen peroxide; intermetallic alloys; active motif screening; ensemble effect; ligand effect
- Publisher
- AMER CHEMICAL SOC
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- Electrochemical reduction of O-2 provides a clean and decentralized pathway to produce H2O2 compared to the current energy-intensive anthraquinone process. As the electrochemical reduction of O-2 proceeds via either a two-electron or a four-electron pathway, it is thus essential to control the selectivity as well as to maximize the catalytic activity. Siahrostami et al. [Nat. Mater. 2013, 12, 1137] demonstrated a novel approach to control the reaction pathway by optimizing an adsorption ensemble to tune adsorption sites of reaction intermediates, identified Pt-Hg catalysts from density functional theory (DFT) calculations, and experimentally validated this catalyst. Inspired by this concept, in this work, we apply a state-of-the-art high-throughput screening to develop an O-2 reduction catalyst for selective H2O2 production. Starting from the Materials Project database, we evaluate activity, selectivity, and electrochemical stability. To efficiently perform the screening, we introduce an active-motif-based approach, which pre-screens unpromising materials and performs DFT calculations only for promising materials, which significantly reduces the number of the required calculations. Finally, we discuss a strategy for efficient future high-throughput screening using a machine learning pipeline consisting of a nonlinear dimension reduction and a density-based clustering.
- DOI
- 10.1021/acscatal.0c05494
- Appears in Collections:
- 공과대학 > 화공신소재공학과 > Journal papers
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