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Cited by 899

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Enhanced Electron Penetration through an Ultrathin Graphene Layer for Highly Efficient Catalysis of the Hydrogen Evolution Reaction

Deng, Jiao, Ren, Pengju, Deng, Dehui, Bao, Xinhe·2015·Angewandte Chemie

Abstract

Major challenges encountered when trying to replace precious‐metal‐based electrocatalysts of the hydrogen evolution reaction (HER) in acidic media are related to the low efficiency and stability of non‐precious‐metal compounds. Therefore, new concepts and strategies have to be devised to develop electrocatalysts that are based on earth‐abundant materials. Herein, we report a hierarchical architecture that consists of ultrathin graphene shells (only 1–3 layers) that encapsulate a uniform CoNi nanoalloy to enhance its HER performance in acidic media. The optimized catalyst exhibits high stability and activity with an onset overpotential of almost zero versus the reversible hydrogen electrode (RHE) and an overpotential of only 142 mV at 10 mA cm−2, which is quite close to that of commercial 40 % Pt/C catalysts. Density functional theory (DFT) calculations indicate that the ultrathin graphene shells strongly promote electron penetration from the CoNi nanoalloy to the graphene surface. With nitrogen dopants, they synergistically increase the electron density on the graphene surface, which results in superior HER activity on the graphene shells.

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Show all 10 references

Research works printed but not linked: 34

  1. 10.1126/science.1103197 · DOI · Paper page
  2. 10.1149/1.2427170 · DOI
  3. 10.1016/S0013-4686(02)00329-8 · DOI · Paper page
  4. 10.1073/pnas.1316792110 · DOI · Paper page
  5. 10.1002/adma.201302685 · DOI · Paper page
  6. 10.1039/C4RA05614K · DOI
  7. 10.1002/anie.201207111 · DOI · Paper page
  8. 10.1002/ange.201207111 · DOI · Paper page
  9. 10.1002/anie.201402315 · DOI · Paper page
  10. 10.1002/ange.201402315 · DOI
  11. 10.1016/j.electacta.2009.01.064 · DOI · Paper page
  12. 10.1038/ncomms5695 · DOI · Paper page
  13. 10.1002/ange.201402646 · DOI · Paper page
  14. 10.1002/anie.201007987 · DOI · Paper page
  15. 10.1002/ange.201007987 · DOI · Paper page
  16. 10.1039/c2ee03250c · DOI · Paper page
  17. 10.1038/nmat3313 · DOI · Paper page
  18. 10.1002/anie.201204958 · DOI · Paper page
  19. 10.1002/ange.201204958 · DOI · Paper page
  20. 10.1039/c3ta13759g · DOI · Paper page
  21. 10.1002/anie.201400358 · DOI · Paper page
  22. 10.1002/ange.201400358 · DOI
  23. 10.1038/ncomms2944 · DOI · Paper page
  24. 10.1039/C4EE00370E · DOI · Paper page
  25. 10.1002/ange.201311111 · DOI · Paper page
  26. 10.1002/anie.201400388 · DOI · Paper page
  27. 10.1002/ange.201400388 · DOI
  28. 10.1021/cs500523k · DOI · Paper page
  29. 10.1103/PhysRevB.75.235433 · DOI · Paper page
  30. 10.1021/jp312363x · DOI
  31. 10.1002/anie.201207918 · DOI · Paper page
  32. 10.1002/ange.201207918 · DOI
  33. 10.1149/1.2428530 · DOI · Paper page
  34. 10.1149/1.1856988 · DOI · Paper page

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Metadata

Authors
Deng, J., Ren, P., Deng, D., & Bao, X.
Venue
Angewandte Chemie
Publisher
Wiley
Published
2015-01-07
Volume
127
Issue
7
Pages
2128-2132
ISSN
  • 0044-8249 (ISSN-L)
  • 1521-3757
OpenAlex
W4246118671
Cited by
899
References
10
FWCI
40.37
Type
journal article
Language
de
Open access
closed
Funders
  • National Natural Science Foundation of China (21321002, 21033009, 21303191)
  • Strategic Priority Research Program of the Chinese Academy of Sciences (XDA09030100)

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CoChat. (2026). Citation check: Enhanced Electron Penetration through an Ultrathin Graphene Layer for Highly Efficient Catalysis of the Hydrogen Evolution Reaction. https://app.cochat.ai/papers/10.1002/ange.201409524
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Deng, J., Ren, P., Deng, D., & Bao, X. (2015). Enhanced Electron Penetration through an Ultrathin Graphene Layer for Highly Efficient Catalysis of the Hydrogen Evolution Reaction. Angewandte Chemie. https://doi.org/10.1002/ange.201409524

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