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The electrosynthesis of hydrogen peroxide (H2O2) from oxygen reduction reaction (ORR) via a two-electron pathway provides an appealing alternative to the energy-intensive anthraquinone route; however, the development of ORR with high selectivity and durability for H2O2 production is still challenging. Herein, we demonstrate an active and stable catalyst, composing of highly dispersed Ag nanoclusters on N-doped hollow carbon spheres (NC-Ag/NHCS), which can effectively reduce O2 molecules into H2O2 with a selectivity of 89%–91% in a potential range from 0.2 to 0.7 V (vs. reversible hydrogen electrode (RHE)) in acidic media. Strikingly, NC-Ag/NHCS achieve a mass activity of 27.1 A·g−1 and a yield rate of 408 mmol·gcat.−1·h−1 at 0.7 V, both of which are comparable with the best-reported results. Furthermore, NC-Ag/NHCS enable catalyzing H2O2 production with a stable current density over 48-h electrolysis and only about 9.8% loss in selectivity after 10,000 cycles. Theoretical analyses indicate that Ag nanoclusters can contribute more electrons to favor the protonation of adsorbed O2, thus leading to a high H2O2 selectivity. This work confirms the great potential of metal nanocluster-based materials for H2O2 electrosynthesis under ambient conditions.

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Publication history
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Acknowledgements

Publication history

Received: 27 December 2021
Revised: 29 January 2022
Accepted: 29 January 2022
Published: 10 March 2022
Issue date: July 2022

Copyright

© Tsinghua University Press 2022

Acknowledgements

Acknowledgements

This study was financially supported by the National Natural Science Foundation of China (Nos. 22075211, 21601136, 51971157, 62005173, and 51621003), Guangdong Province Higher Vocational Colleges & Schools Pearl River Scholar Funded Scheme (2016), Guangdong Third Generation Semiconductor Engineering Technology Development Center (No. 2020GCZX007), Science, Technology, and Innovation Commission of Shenzhen Municipality (No. RCBS20200714114818140), China Postdoctoral Science Foundation (No. 2019M663118), and School level scientific research project of Shenzhen Institute of information technology (No. PT2019E002).

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