Electrocatalytic NO reduction (NORR) to NH3 represents a promising approach for converting hazardous NO waste gases into high-value NH3 products under ambient conditions. However, exploring stable, low-cost, and highly efficient catalysts to enhance the NO-to-NH3 conversion process remains a significant challenge. Herein, through systematic computational studies based on density functional theory (DFT), we rationally designed transition metal triatomic cluster supported on graphdiyne (TM3/GDY) as potential single-cluster catalysts for high-performance NORR. The results indicated that the GDY support is incredibly effective at immobilizing these triatomic metal clusters, preventing metal aggregation and dissolution. Furthermore, the TM3/GDY systems exhibit tunable reactivity for NO activation due to the synergistic effect of triple-metal sites. Among all examined candidates, Ni3/GDY demonstrates the highest NORR catalytic performance with a record low limiting potential of -0.05 V. Notably, NO adsorption strength was identified as an effective descriptor to rationalize the NORR activity trend, which is highly dependent on the amount of the carrying charges on the anchored TM3 clusters. Additionally, the hydrogenation steps during NORR are kinetically feasible on Ni3/GDY with a small kinetic barrier of 0.34 V for the rate-determining step, corresponding to an outstanding turnover frequency (3.03 × 10-25) s-1 per site at 300 K for NH3 generation, implying an ultra-fast reaction rate. Our work not only identified promising NORR catalysts but also provided valuable insights for rationally designing atomically precise novel catalysts for the resource utilization of small molecules.
Keywords: DFT computations; GDY; Machine learning; NORR; Triatomic clusters.
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