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Subgroup discovery points to the prominent role of charge transfer in breaking nitrogen scaling relations at single-atom catalysts on VS₂

Title data

Li, Haobo ; Liu, Yunxia ; Chen, Ke ; Margraf, Johannes T. ; Li, Youyong ; Reuter, Karsten:
Subgroup discovery points to the prominent role of charge transfer in breaking nitrogen scaling relations at single-atom catalysts on VS₂.
In: ACS Catalysis. Vol. 11 (2021) Issue 13 . - pp. 7906-7914.
ISSN 2155-5435
DOI: https://doi.org/10.1021/acscatal.1c01324

Abstract in another language

The electrochemical nitrogen reduction reaction (NRR) is a much sought-after low-energy alternative to Haber–Bosch ammonia synthesis. Single-atom catalysts (SACs) promise to break scaling relations between adsorption energies of key NRR reaction intermediates that severely limit the performance of extended catalysts. Here, we perform a computational screening study of transition metal (TM) SACs supported on vanadium disulfide (VS2) and indeed obtain strongly broken scaling relations. A data-driven analysis by means of outlier detection and subgroup discovery reveals that this breaking is restricted to early TMs, while detailed electronic structure analysis rationalizes it in terms of strong charge transfer to the underlying support. This charge transfer selectively weakens *N and *NH adsorption and leads to promising NRR descriptors for SACs formed of earlier TMs like Ta that would conventionally not be associated with nitrogen reduction.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: subgroup discovery; density functional theory; computational screening; single-atom catalysts; electrochemical nitrogen reduction
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik - Univ.-Prof. Dr. Johannes Theo Margraf
Result of work at the UBT: No
DDC Subjects: 500 Science > 540 Chemistry
Date Deposited: 13 Nov 2023 13:01
Last Modified: 13 Nov 2023 13:01
URI: https://eref.uni-bayreuth.de/id/eprint/87682