期刊
INORGANIC CHEMISTRY FRONTIERS
卷 10, 期 22, 页码 6578-6587出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/d3qi01390a
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Selectivity toward ammonia is an important indicator for electrocatalysts in the electrochemical nitrogen reduction reaction (eNRR). This study focuses on improving selectivity through multiple adsorptions and unoccupied d-orbitals of the supported metal atom. The machine learning model successfully predicts the low limiting potential and high selectivity of the catalysts, which was further confirmed by experiments.
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-C3N4 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-C3N4 (TM = V, Cr, Mn, Mo, Tc, W, and Re) is predicted to be 100% while three N2 were adsorbed on TM (3N(2)@TM/gt-C3N4). Furthermore, 3N(2)@TM/gt-C3N4 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.
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