First-Principles Study of Bimetallic Pairs Embedded on Graphene Co-Doped with N and O for N2 Electroreduction.

The electrocatalytic nitrogen reduction reaction (NRR) is considered a viable alternative to the Haber-Bosch process for ammonia synthesis, and the design of highly active and selective catalysts is crucial for the industrialization of the NRR. Dual-atom catalysts (DACs) with dual active sites offer flexible active sites and synergistic effects between atoms, providing more possibilities for the tuning of catalytic performance. In this study, we designed 48 graphene-based DACs with N4O2 coordination (MM'@N4O2-G) using density functional theory. Through a series of screening strategies, we explored the reaction mechanisms of the NRR for eight catalysts in depth and revealed the "acceptance-donation" mechanism between the active sites and the N2 molecules through electronic structure analysis. The study found that the limiting potential of the catalysts exhibited a volcano-shaped relationship with the d-band center of the active sites, indicating that the synergistic effect between the bimetallic components can regulate the d-band center position of the active metal M, thereby controlling the reaction activity. Furthermore, we investigated the selectivity of the eight DACs and identified five potential NRR catalysts. Among them, MoCo@N4O2-G showed the best NRR performance, with a limiting potential of -0.20 V. This study provides theoretical insights for the design and development of efficient NRR electrocatalysts.

Modification of metals and ligands in two-dimensional conjugated metal-organic frameworks for CO2 electroreduction: A combined density functional theory and machine learning study.

Electrochemical carbon dioxide reduction reaction (CO2RR) is a promising technology to establish an artificial carbon cycle. Two-dimensional conjugated metal-organic frameworks (2D c-MOFs) with high electrical conductivity have great potential as catalysts. Herein, we designed a range of 2D c-MOFs with different transition metal atoms and organic ligands, TMNxO4-x-HDQ (TM = Cr∼Cu, Mo, Ru∼Ag, W∼Au; x  = 0, 2, 4; HDQ = hexadipyrazinoquinoxaline), and systematically studied their catalytic performance using density functional theory (DFT). Calculation results indicated that all of TMNxO4-x-HDQ structures possess good thermodynamic and electrochemical stability. Notably, among the examined 37 MOFs, 6 catalysts outperformed the Cu(211) surface in terms of catalytic activity and product selectivity. Specifically, NiN4-HDQ emerged as an exceptional electrocatalyst for CO production in CO2RR, yielding a remarkable low limiting potential (UL) of -0.04 V. CuN4-HDQ, NiN2O2-HDQ, and PtN2O2-HDQ also exhibited high activity for HCOOH production, with UL values of -0.27, -0.29, and -0.27 V, respectively, while MnN4-HDQ, and NiO4-HDQ mainly produced CH4 with UL values of -0.58 and -0.24 V, respectively. Furthermore, these 6 catalysts efficiently suppressed the competitive hydrogen evolution reaction. Machine learning (ML) analysis revealed that the key intrinsic factors influencing CO2RR performance of these 2D c-MOFs include electron affinity (EA), electronegativity (χ), the first ionization energy (Ie), p-band center of the coordinated N/O atom (εp), the radius of metal atom (r), and d-band center (εd). Our findings may provide valuable insights for the exploration of highly active and selective CO2RR electrocatalysts.