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Oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) are three critical reactions for energy-related applications, such as water electrolyzers and metal-air batteries. Graphene-supported single-atom catalysts (SACs) have been widely explored; however, either experiments or density functional theory (DFT) computations cannot screen catalysts at high speed. Herein, based on DFT computations of 104 graphene-supported SACs (M@C3, M@C4, M@pyridine-N4, and M@pyrrole-N4), we built up machine learning (ML) models to describe the underlying pattern of easily obtainable physical properties and limiting potentials (errors = 0.013/0.005/0.020 V for ORR/OER/HER, respectively), and employed these models to predict the catalysis performance of 260 other graphene-supported SACs containing metal-NxCy active sites (M@NxCy). We recomputed the top catalysts recommended by ML towards ORR/OER/HER by DFT, which confirmed the reliability of our ML model, and identified two OER catalysts (Ir@pyridine-N3C1 and Ir@pyridine-N2C2) outperforming noble metal oxides, RuO2 and IrO2. The ML models quantitatively unveiled the significance of various descriptors and fast narrowed down the potential list of graphene-supported single-atom catalysts. This approach can be easily used to screen and design other SACs, and significantly accelerate the catalyst design for many other important reactions.
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This content will become publicly available on March 1, 2026
Mechanistic insights into oxygen reduction reaction on metal/perovskite catalysts: Interfacial interactions and reaction pathways
The oxygen reduction reaction (ORR) is a critical process in energy conversion systems, influencing the efficiency and performance of various devices such as fuel cells, batteries, and electrolyzers. Perovskite-supported metal materials (metal/perovskite) offer several advantages as ORR electrocatalysts, including strong metal-support interactions, oxygen vacancy formation in the perovskite lattice, and synergistic triple-phase boundary (TPB) activity at the interface. Despite their significance, the mechanistic understanding of ORR on metal/perovskite catalysts remains incomplete, particularly at metal/perovskite interfaces. This study investigates ORR on BaZrO3 (BZO) perovskite-supported metal clusters (Pt or Ag) using density functional theory (DFT) to unravel critical insights into charge redistribution at the metal/BZO interface. Energy profiles for elemental steps along two different ORR pathways—oxygen adsorption on the metal cluster surface and direct oxygen adsorption at the TPB—were calculated to explore the effects of different active sites. The results provide a deeper understanding of ORR on metal/perovskite catalysts, emphasizing the role of interfacial interactions and pathway-dependent reaction mechanisms. This work paves the way for guiding the design of high-performance electrocatalysts for ORR in terms of composition, interface design, and local environment modification for a broad range of energy applications.
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- Award ID(s):
- 2503035
- PAR ID:
- 10575423
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Solid State Ionics
- Volume:
- 421
- ISSN:
- 0167-2738
- Page Range / eLocation ID:
- 116808
- Subject(s) / Keyword(s):
- Oxygen reduction reaction Metal/perovskite catalysts Interfacial interactions Charge redistribution DFT
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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