In:
Inorganic Chemistry Frontiers, Royal Society of Chemistry (RSC)
Kurzfassung:
Selectivity toward ammonia is an important indicator of a good electrocatalyst for the electrochemical nitrogen reduction reaction (eNRR). The multi-adsorption of N 2 on TM/ gt -C 3 N 4 greatly decreases the possibility of H binding, thus, self-promoting the selectivity toward NRR. Furthermore, the amount of nitrogen that can be trapped on the active sites of the studied catalysts is determined by the numbers of unoccupied d-orbitals of the supported single metal atom. The NRR selectivity on TM/ gt -C 3 N 4 (TM = V, Cr, Mn, Mo, Tc, W, and Re) is predicted to be 100% while three N 2 were adsorbed on TM (3N 2 @TM/ gt -C 3 N 4 ). Furthermore, 3N 2 @TM/ gt -C 3 N 4 is the dominant configuration under a high pressure region at room temperature. Multiple dinitrogen molecules can be stably adsorbed on the active site, which is a good indicator of thermal stability by AIMD simulation in the canonical ensemble. Machine-learning analysis indicates that the high selectivity toward ammonia is determined by the numbers of effectively bound N 2 molecules, and the low limiting-potential may correlate with the charging states of the supported metal atom, adsorption energy, and N–N bond length of the adsorbed N 2 molecule. W/N 3 –G (W atom supported on three-pyrimidine-nitrogen-doped graphene) is predicted as a potential single atom catalyst with a low limiting-potential of −0.44 V and high selectivity based on the machine learning model, which is verified by further DFT calculations. This suggests a good generalization capability of the machine learning model.
Materialart:
Online-Ressource
ISSN:
2052-1553
Sprache:
Englisch
Verlag:
Royal Society of Chemistry (RSC)
Publikationsdatum:
2023
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