The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights
Open accessAbstract
The electrochemical nitrate reduction reaction (NO 3 RR) 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 metal d-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 *NO 3 is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO 3 RR to ammonia with a Faradaic efficiency of 92.5% at −0.5 V RHE and a yield rate of 6.25 mol h−1 g−1 at −0.6 V RHE. This study provides machine-learned design rules besides the d-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.
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Cited by
Recent Advances in Electrocatalysts for Efficient Nitrate Reduction to Ammonia
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Metadata
- Authors
- Gao, Q., Pillai, H. S., Huang, Y., Liu, S., Mu, Q., Han, X., Yan, Z., Zhou, H., He, Q., Xin, H., & Zhu, H.
- Venue
- Nature Communications
- Published
- 2022-04-29
- Volume
- 13
- Issue
- 1
- Pages
- 2338-2338
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W4225114738
- Cited by
- 420
- References
- 61
- FWCI
- 15.50
- Type
- journal article
- Language
- en
- Open access
- gold
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- Gao, Q., Pillai, H. S., Huang, Y., Liu, S., Mu, Q., Han, X., Yan, Z., Zhou, H., He, Q., Xin, H., & Zhu, H. (2022). Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights. Nature Communications. https://doi.org/10.1038/s41467-022-29926-w
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