Issue 30, 2022

Theoretical insights into the electroreduction of nitrate to ammonia on graphene-based single-atom catalysts

Abstract

Electrocatalytic reduction of harmful nitrate (NO3) to valuable ammonia (eNO3RR) is critical and attractive for both environmental remediation and energy transformation. A single atom catalyst (SAC) based on graphene represents one of the most promising eNO3RR catalysts. However, the underlying catalytic mechanism and the intrinsic factors dictating the catalytic activity trend remain unclear. Herein, using first-principles calculations, eNO3RR on TMN3 and TMN4 (TM = Ti–Ni) doped graphene was thoroughly investigated. Our results reveal that FeN4 doped graphene exhibits excellent eNO3RR performance with a low limiting potential of −0.38 V, agreeing with the experimental finding, which can be ascribed to the effective adsorption and activation of NO3via the charge “acceptance–donation” mechanism and its moderate binding due to the occupation of the d–p antibonding orbital. In particular, we found that eNO3RR activities are well correlated with the intrinsic properties of TM centers and their local environments. With the established activity descriptor, several other graphene-based SACs were efficiently screened out with excellent eNO3RR performance. Our studies could not only provide an atomic insight into the catalytic mechanism and activity origin of eNO3RR on graphene-based SACs, but also open an avenue for the rational design of SACs for eNO3RR towards ammonia by regulating the metal center and its local coordination environment.

Graphical abstract: Theoretical insights into the electroreduction of nitrate to ammonia on graphene-based single-atom catalysts

Supplementary files

Article information

Article type
Paper
Submitted
21 Mei 2022
Accepted
08 Jul. 2022
First published
08 Jul. 2022

Nanoscale, 2022,14, 10862-10872

Theoretical insights into the electroreduction of nitrate to ammonia on graphene-based single-atom catalysts

Y. Wang, D. Wu, P. Lv, B. He, X. Li, D. Ma and Y. Jia, Nanoscale, 2022, 14, 10862 DOI: 10.1039/D2NR02813A

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