JOURNAL ARTICLE

Controllable Assembly of Highly Oxidized Cobalt on Graphdiyne Surface for Efficient Conversion of Nitrogen into Nitric Acid.

  • Published In: Angewandte Chemie International Edition, 2024, v. 63, n. 9. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zheng, Xuchen; Wu, Han; Gao, Yang; Chen, Siao; Xue, Yurui; Li, Yuliang 3 of 3

Abstract

The manufacture of nitric acid (HNO3) consumes large amounts of energy and causes serious environmental pollution. Electrochemical synthesis is regarded as a key way to eliminate carbon emissions from the chemicals industry. The selective electrosynthesis of HNO3 from nitrogen was achieved by controllable assembly of cobalt metal on graphdiyne surface using a powerful tool of electrochemistry at ambient conditions. As an advanced material, graphdiyne (GDY) has a large conjugated structure on its surface and is rich in sp‐C triple bond skeleton, which can achieve strong interaction with metal atoms, resulting in incomplete charge transfer between graphdiyne and cobalt atoms. The experimental and theoretical calculation results show that the highly oxidized cobalt on graphdiyne (HOCo/GDY) can selectively and efficiently activate and convert the nitrogen into the key intermediate *NO, which promotes the efficient overall conversion performance of nitrogen to nitric acid. Thus, the highest nitric acid yield (192.0 μg h−1 mg−1) and Faradaic efficiency (21.5 %) were achieved at low potentials. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Angewandte Chemie International Edition. 2024/02, Vol. 63, Issue 9, p1
  • Document Type:Article
  • Subject Area:Chemistry
  • Publication Date:2024
  • ISSN:1433-7851
  • DOI:10.1002/anie.202316723
  • Accession Number:175520419
  • Copyright Statement:Copyright of Angewandte Chemie International Edition is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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