Underappreciated Role of Low-Energy Facets in Nitrogen Electroreduction
Source abstract
Abstract
Electrocatalytic nitrogen reduction reaction (NRR) is a promising route to replace the energy-intensive Haber–Bosch process. However, weak nitrogen-surface interaction impedes the reaction efficiency and demands better understanding of the catalyst surface chemistry. Here, we expand the design rules for NRR to include the low-energy smooth surfaces, in contrast to conventional wisdom favoring irregular or high-index facets, grain boundaries, and edge sites. Our Ni3S4 catalyst with low-energy facets achieves a 6.8 ± 3.3% Faradaic efficiency (FE) for NRR, compared to 1.9 ± 3.6% for a control of the same crystal structure and composition but with a more disordered surface. First-principles calculations reveal that the synthetic conditions allow choosing whether Ni-rich or S-rich surfaces would be more stable, which affects the coordination of the active Ni site and its reactivity. In addition, the combination of Lewis acid and Lewis base sites allows separating the adsorption of different reactants, resulting in a suppression of the competing hydrogen evolution reaction.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Ammonia Synthesis and Nitrogen Reduction · Electrocatalysts for Energy Conversion · Advanced Photocatalysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Polyoxometalate Chemistry, College of Chemistry and Chemical Engineering, Henan University, Kaifeng, 475004, China
Department of Physical and Environmental Sciences, Department of Chemistry, University of Toronto, Scarborough, Ontario M1C 1A4, Canada
Funding
University of Toronto Scarborough · Natural Sciences and Engineering Research Council of Canada · National Natural Science Foundation of China
References (23)
Show source-provided references
1. 10.1021/acsenergylett.9b0267910.1021/acsenergylett.9b02679 ↗
2. 10.1021/acs.chemmater.9b0531310.1021/acs.chemmater.9b05313 ↗
3. 10.1002/anie.20190396910.1002/anie.201903969 ↗
4. 10.1039/c3cs35486e10.1039/c3cs35486e ↗
5. 10.1038/nchem.62310.1038/nchem.623 ↗
6. 10.1038/natrevmats.2017.5010.1038/natrevmats.2017.50 ↗
7. 10.1021/acs.chemmater.9b0368610.1021/acs.chemmater.9b03686 ↗
8. 10.1021/ja035790210.1021/ja0357902 ↗
9. 10.1021/acsmaterialslett.9b0006410.1021/acsmaterialslett.9b00064 ↗
10. 10.1126/science.aab350110.1126/science.aab3501 ↗
11. 10.1021/jacs.5b0818610.1021/jacs.5b08186 ↗
12. 10.1002/anie.20160223710.1002/anie.201602237 ↗
13. 10.1039/C8TA01928B10.1039/C8TA01928B ↗
14. 10.1016/j.apcatb.2018.11.00310.1016/j.apcatb.2018.11.003 ↗
15. 10.1016/j.electacta.2015.05.18610.1016/j.electacta.2015.05.186 ↗
16. 10.1039/C4NR07648F10.1039/C4NR07648F ↗
17. 10.1016/j.apsusc.2018.09.17710.1016/j.apsusc.2018.09.177 ↗
18. 10.1039/C4NR07648F10.1039/C4NR07648F ↗
19. 10.1107/S002188981103897010.1107/S0021889811038970 ↗
20. 10.1021/ja050469010.1021/ja0504690 ↗
21. 10.1021/acscatal.9b0224510.1021/acscatal.9b02245 ↗
22. 10.1002/cssc.20150032210.1002/cssc.201500322 ↗
23. Ponce, M.; van Zon, R.; Northrup, S.; Gruner, D.; Chen, J.; Ertinaz, F.; Fedoseev, A.; Groer, L.; Mao, F.; Mundim, B.; Nolta, M.; Pinto, J.; Saldarriaga, M.; Slavnic, V.; Spence, E.; Yu, C.; Peltier, W. Deploying A top-100 supercomputer for large parallel workloads. Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines Learning; 2019.10.1145/3332186.3332195 ↗