Unveiling pH‐Dependent Mechanistic Shifts in Cu2O‐Catalyzed Nitrate Electroreduction for Selective Ammonia Synthesis
Source abstract
Abstract
Abstract The electrocatalytic nitrate reduction reaction (NO3RR) offers a sustainable route for ammonia synthesis, yet its practical implementation requires catalysts adaptable to pH fluctuations inherent in industrial wastewater systems. Herein, Cu2O nanocubes as a model catalyst is synthesized and decoupled the pH‐dependent reaction mechanisms with systematic experimental investigations. In acid electrolytes, the catalyst exhibited 93.3% faradaic efficiency for NH3 with a yield rate of 34.6 mg h−1mgcat−1 at −0.7 V versus. RHE. In contrast, in alkaline and neutral electrolytes, NH3 synthesis is impeded by the formation of NO2‐ which served as the predominant by‐product. Through operando infrared spectroscopy analysis, proton availability as the pivotal regulator is identified: Acidic media facilitates the further conversion of *NO2 intermediates into NH3 via the NOH pathway, whereas H+‐deficient environments in neutral/alkaline conditions divert reaction flux through the NHO pathway. This mechanistic elucidation establishes proton concentration as a key parameter for steering nitrate‐to‐ammonia conversion efficiency.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Ammonia Synthesis and Nitrogen Reduction · Advanced Photocatalysis Techniques · Caching and Content Delivery
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Environmental and Chemical Engineering Shanghai University 99 Shangda Road Shanghai 200444 China
Department of Chemical Engineering Sichuan University Chengdu 610065 China
Funding
National Natural Science Foundation of China
References (31)
Show source-provided references
1. 10.1016/j.joule.2019.10.00610.1016/j.joule.2019.10.006 ↗
2. 10.1016/j.joule.2020.12.02510.1016/j.joule.2020.12.025 ↗
3. 10.1016/j.scitotenv.2020.14467410.1016/j.scitotenv.2020.144674 ↗
4. 10.1039/c2ee23062c10.1039/c2ee23062c ↗
5. 10.1016/j.jmst.2020.10.05610.1016/j.jmst.2020.10.056 ↗
6. 10.1016/j.apmt.2020.10062010.1016/j.apmt.2020.100620 ↗
7. 10.1038/s44160-023-00387-310.1038/s44160-023-00387-3 ↗
8. Yu J. · Appl. Catal. B Environ. Energy. 2025 · 368
9. 10.1021/acscatal.3c0143910.1021/acscatal.3c01439 ↗
10. 10.1038/s44160-024-00705-310.1038/s44160-024-00705-3 ↗
11. 10.1038/s41565-022-01121-410.1038/s41565-022-01121-4 ↗
12. 10.1002/smll.20200452610.1002/smll.202004526 ↗
13. 10.1021/acsenergylett.3c0122610.1021/acsenergylett.3c01226 ↗
14. 10.1002/anie.20180136910.1002/anie.201801369 ↗
15. 10.1038/s41467-022-28728-410.1038/s41467-022-28728-4 ↗
16. 10.1002/adma.20240039610.1002/adma.202400396 ↗
17. 10.1002/ange.20241303310.1002/ange.202413033 ↗
18. 10.1002/anie.20242526210.1002/anie.202425262 ↗
19. Wang Y. · J. Gong, Sci. China Chem. · 2023 · 66 · 913
20. 10.3390/catal1503019910.3390/catal15030199 ↗
21. 10.1016/j.cej.2025.16338010.1016/j.cej.2025.163380 ↗
22. 10.1016/j.surfin.2025.10667310.1016/j.surfin.2025.106673 ↗
23. 10.1016/j.apcatb.2019.01.04210.1016/j.apcatb.2019.01.042 ↗
24. 10.1007/s10854-018-0527-610.1007/s10854-018-0527-6 ↗
25. 10.1002/adfm.20220989010.1002/adfm.202209890 ↗
26. 10.1021/acsnano.2c0791110.1021/acsnano.2c07911 ↗
27. 10.1016/j.jcis.2022.09.01210.1016/j.jcis.2022.09.012 ↗
28. 10.1021/acsnano.3c0594610.1021/acsnano.3c05946 ↗
29. 10.1038/s41467-023-43897-610.1038/s41467-023-43897-6 ↗
30. 10.1021/acscatal.1c0391810.1021/acscatal.1c03918 ↗
31. 10.1039/b816349a10.1039/b816349a ↗