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Exploring cost-effective catalysts with high catalytic performance and long-term stability has always been a general concern for environment protection and energy conversion. Here, Au nanoparticles (NPs) embedded CuOx-CeO2 core/shell nanospheres (Au@CuOx-CeO2 CSNs) have been successfully prepared through a versatile one-pot method at ambient conditions. The spontaneous auto-redox reaction between HAuCl4 and Ce(OH)3 in aqueous solution triggered the self-assembly growth of micro-/nanostructural Au@CuOx-CeO2 CSNs. Meanwhile, the CuOx clusters in Au@CuOx-CeO2 CSNs are capable of improving the anti-sintering ability of Au NPs and providing synergistic catalysis benefits. As a result, the confined Au NPs exhibited extraordinary thermal stability even at a harsh thermal condition up to 700 °C. In addition, before and after the severe calcination process, Au@CuOx-CeO2 CSNs can exhibit enhanced catalytic activity and excellent recyclability towards the hydrogenation of p-nitrophenol compared to previously reported nanocatalysts. The synergistic catalysis path between Au/CuOx/CeO2 triphasic interfaces was revealed by density functional theory (DFT) calculations. The CuOx clusters around the embedded Au NPs can provide moderate adsorption strength of p-nitrophenol, while the adjacent CeO2-supported Au NPs can facilitate the hydrogen dissociation to form H* species, which contributes to achieve the efficient reduction of p-nitrophenol. This study opens up new possibilities for developing high-efficient and sintering-resistant micro-/nanostructural nanocatalysts by exploiting multiphasic systems.

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Publication history
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Acknowledgements

Publication history

Received: 19 February 2020
Revised: 24 March 2020
Accepted: 10 April 2020
Published: 05 August 2020
Issue date: August 2020

Copyright

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020

Acknowledgements

The authors are grateful for the financial support of the National Natural Science Foundation of China (Nos. 21590791, 21771005, 21931001, and 21927901), and Ministry of Science and Technology (MOST) of China (Nos. 2014CB643803, 2017YFA0205101, and 2017YFA0205104). The computational work was supported by the High-performance Computing Platform of Peking University. K. W. specifically thanks the National Postdoctoral Program for Innovative Talents under grant no. BX20190005, and the China Postdoctoral Science Foundation (No. 2019M660293).

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