Experiments have shown that graphene-supported Ni-single atom catalysts (Ni-SACs) provide a promising strategy for the electrochemical reduction of CO₂ to CO, but the nature of the Ni sites (Ni-N₂C₂, Ni-N₃C₁, Ni-N₄) in Ni-SACs has not been determined experimentally. Here, we apply the recently developed grand canonical potential kinetics (GCP-K) formulation of quantum mechanics to predict the kinetics as a function of applied potential (U) to determine faradic efficiency, turn over frequency, and Tafel slope for CO and H₂ production for all three sites. We predict an onset potential (at 10 mA cm⁻²) U_onset = −0.84 V (vs. RHE) for Ni-N₂C₂ site and U_onset = −0.92 V for Ni-N₃C₁ site in agreement with experiments, and U_onset = −1.03 V for Ni-N₄. We predict that the highest current is for Ni-N₄, leading to 700 mA cm⁻² at U = −1.12 V. To help determine the actual sites in the experiments, we predict the XPS binding energy shift and CO vibrational frequency for each site.

Two-dimensional (2D) transition metal dichalcogenides (TMDs) have stimulated the modern technology due to their unique and tunable electronic, optical, and chemical properties. Therefore, it is very important to study the control parameters for material preparation to achieve high quality thin films for modern electronics, as the performance of TMDs-based device largely depends on their layer number, grain size, orientation, and morphology. Among the synthesis methods, chemical vapor deposition (CVD) is an excellent technique, vastly used to grow controlled layer of 2D materials in recent years. In this review, we discuss the different growth routes and mechanisms to synthesize high quality large size TMDs using CVD method. We highlight the recent advances in the controlled growth of mono- and few-layer TMDs materials by varying different growth parameters. Finally, different strategies to control the grain size, boundaries, orientation, morphology and their application for various field of are also thoroughly discussed.

Machine learning (ML) integrated density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of heterogeneous catalysts such as single atom catalysts (SACs) through the establishment of deep structure–activity relationships.