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Abstract The enhanced safety, superior energy, and power density of rechargeable metal‐air batteries make them ideal energy storage systems for application in energy grids and electric vehicles. However, the absence of a cost‐effective and stable bifunctional catalyst that can replace expensive platinum (Pt)‐based catalyst to promote oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) at the air cathode hinders their broader adaptation. Here, it is demonstrated that Tin (Sn) doped β‐gallium oxide (β‐Ga2O3) in the bulk form can efficiently catalyze ORR and OER and, hence, be applied as the cathode in Zn‐air batteries. The Sn‐doped β‐Ga2O3sample with 15% Sn (Snx=0.15‐Ga2O3) displayed exceptional catalytic activity for a bulk, non‐noble metal‐based catalyst. When used as a cathode, the excellent electrocatalytic bifunctional activity of Snx=0.15‐Ga2O3leads to a prototype Zn‐air battery with a high‐power density of 138 mW cm−2and improved cycling stability compared to devices with benchmark Pt‐based cathode. The combined experimental and theoretical exploration revealed that the Lewis acid sites in β‐Ga2O3aid in regulating the electron density distribution on the Sn‐doped sites, optimize the adsorption energies of reaction intermediates, and facilitate the formation of critical reaction intermediate (O*), leading to enhanced electrocatalytic activity.
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Deep reaction network exploration at a heterogeneous catalytic interface
Abstract Characterizing the reaction energies and barriers of reaction networks is central to catalyst development. However, heterogeneous catalytic surfaces pose several unique challenges to automatic reaction network characterization, including large sizes and open-ended reactant sets, that make ad hoc network construction the current state-of-the-art. Here, we show how automated network exploration algorithms can be adapted to the constraints of heterogeneous systems using ethylene oligomerization on silica-supported single-site Ga 3+ as a model system. Using only graph-based rules for exploring the network and elementary constraints based on activation energy and size for identifying network terminations, a comprehensive reaction network is generated and validated against standard methods. The algorithm (re)discovers the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. These results demonstrate that automated reaction exploration algorithms are rapidly maturing towards general purpose capability for exploratory catalytic applications.
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- Award ID(s):
- 1647722
- PAR ID:
- 10431181
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41467-022-32514-7
