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Cited by 464

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Restoring the Nitrogen Cycle by Electrochemical Reduction of Nitrate: Progress and Prospects

Zeng, Yachao, Priest, Cameron, Wang, Guofeng, Wu, Gang·2020·Small Methods

Abstract

The increasing amount of anthropogenic nitrogen has resulted in alarming levels of nitrate in bodies of water and caused a cascade of damage to the environment and human health. Electrochemical nitrate reduction is an efficient strategy ancillary to biological nitrogen cycle management, which utilizes electrons as green reductants and rebalances the N 2 cycle. In this review, the fundamental principles and implementation of electrochemical nitrate reduction are described to lay the foundation for the eventual large‐scale application in the remediation of nitrate‐laden wastewaters. Factors that influence the activity and selectivity of electrochemical nitrate reduction are also discussed. Moreover, electrolytic cell design and construction are discussed via a rational choice of critical components and assembly. Finally, a roadmap for the development of highly selective nitrate reduction catalysts is proposed. This review attempts to provide a practical strategy for electrochemical nitrogen cycle management and aims to stimulate future research for final implementation.

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Metadata

Authors
Zeng, Y., Priest, C., Wang, G., & Wu, G.
Venue
Small Methods
Publisher
Wiley
Published
2020-09-29
Volume
4
Issue
12
ISSN
2366-9608 (ISSN-L)
OpenAlex
W3089344870
Cited by
464
References
4
FWCI
10.24
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Restoring the Nitrogen Cycle by Electrochemical Reduction of Nitrate: Progress and Prospects. https://app.cochat.ai/papers/10.1002/smtd.202000672
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Zeng, Y., Priest, C., Wang, G., & Wu, G. (2020). Restoring the Nitrogen Cycle by Electrochemical Reduction of Nitrate: Progress and Prospects. Small Methods. https://doi.org/10.1002/smtd.202000672

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.