Hydride-doped Ag17Cu10 nanoclusters as high-performance electrocatalysts for CO2 reduction

The atomically precise metal electrocatalysts for driving CO2 reduction reactions are eagerly pursued as they are model systems to identify the active sites, understand the reaction mechanism, and further guide the exploration of efficient and practical metal nanocatalysts. Reported herein is a nano...

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Veröffentlicht in:iScience 2023-10, Vol.26 (10), p.107850-107850, Article 107850
Hauptverfasser: Sun, Xueli, Wang, Peng, Yan, Xiaodan, Guo, Huifang, Wang, Lin, Xu, Qinghua, Yan, Bingzheng, Li, Simin, He, Jinlu, Chen, Guangxu, Shen, Hui, Zheng, Nanfeng
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Sprache:eng
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Zusammenfassung:The atomically precise metal electrocatalysts for driving CO2 reduction reactions are eagerly pursued as they are model systems to identify the active sites, understand the reaction mechanism, and further guide the exploration of efficient and practical metal nanocatalysts. Reported herein is a nanocluster-based electrocatalyst for CO2 reduction, which features a clear geometric and electronic structure, and more importantly excellent performance. The nanocatalysts with the molecular formula of [Ag17Cu10(dppm)4(PhC≡C)20H4]3+ have been obtained in a facile way. The unique metal framework of the cluster, with silver, copper, and hydride included, and dedicated surface structure, with strong (dppm) and labile (alkynyl) ligands coordinated, endow the cluster with excellent performance in electrochemical CO2 reduction reaction to CO. With the atomically precise electrocatalysts in hand, not only high reactivity and selectivity (Faradaic efficiency for CO up to 91.6%) but also long-term stability (24 h), are achieved. [Display omitted] •dppmCuBH4 as a reductant for the facile synthesis of metal nanoclusters•Ag17Cu10H4 nanoclusters as high-performance electrocatalysts for CO2 reduction•Hydrides are key in controlling electrocatalytic performance•Metal nanoclusters as model systems for studying electrocatalytic mechanism Catalysis; Electrochemical energy conversion; Computational chemistry; Materials science
ISSN:2589-0042
2589-0042
DOI:10.1016/j.isci.2023.107850