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The electrochemical reduction of nitrate to ammonia is of interest as an energy/environmentally friendly source of ammonia for agriculture and energy applications and as a route toward groundwater purification. We report in situ photoemission data, electrochemical results, and density functional theory calculations that demonstrate vanadium oxide—prepared by ambient exposure of V metal, with a distribution of surface V3+and V4+oxidation states—specifically adsorbs and reduces nitrate to ammonia at pH 3.2 at cathodic potentials. Negligible cathodic activity in the absence of NO3−indicates high selectivity with respect to non-nitrate reduction processes. In situ photoemission data indicate that nitrate adsorption and reduction to adsorbed NO2is a key step in the reduction process. NO3RR activity is also observed at pH 7, albeit at a much slower rate. The results indicate that intermediate (non-d0) oxidation states are important for both molecular nitrogen and nitrate reduction to ammonia.
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Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights
Abstract The electrochemical nitrate reduction reaction (NO3RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metald-states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO3is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO3RR to ammonia with a Faradaic efficiency of 92.5% at −0.5 VRHEand a yield rate of 6.25 mol h−1g−1at −0.6 VRHE. This study provides machine-learned design rules besides thed-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.
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- PAR ID:
- 10366619
- Publisher / Repository:
- Nature Publishing Group
- 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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