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Ni-based alloys are considered as one of the most viable materials to achieve difunctional tasks beyond pure metals. However, the local atomic structures of active sites lack precise control in the conventional alloys, and the effects of the ratio of a metal site on reaction activity are unclear. In this work, we investigated the effect of the local atomic structures, including Mo1/Ni(111) (single Mo site), Mo2/Ni(111) (two successive Mo sites), and MoNi(111) surfaces (successive Mo sites) on the hydrodeoxygenation (HDO) reaction mechanism of furfural (FAL) conversion to 2-methylfuran (2-MF). The adsorption energies exhibit an increase in the dependence of the Mo content, which is attributed to the stronger activation of Mo sites for O atoms from the terminal group. The reaction potential energy surfaces reveal that the Mo1/Ni(111) surface results in FAL conversion to 2-MF by the OH path, while the MoNi(111) surface has an obvious preference for the H path. The microkinetic modeling further illustrates that the Mo2/Ni(111) surface is inclined to the OH path for FAL conversion to 2-MF. Moreover, the Mo1/Ni(111) surface with atomically dispersed Mo sites exhibits the optimal catalytic activity in three atomic structures. Owing to the stronger oxophilicity of Mo, fewer Mo sites can achieve OH species hydrogenation and even C–O bond breaking, and the surface containing more Mo sites has a tendency toward the H path to avoid surface poisoning. This work highlights the fine-tuning of the atomic structures and reveals the effect on reaction pathways.

Dual-metal-site catalysts (DMSCs) are increasingly important catalysts in the field of electrochemical carbon dioxide reduction reaction (CO2RR) in recent years. However, rapid screening of suitable metal combinations of DMSCs remains a huge challenge. Herein, we constructed an active learning (AL) framework to study CO2RR to HCOOH. This AL framework turned out a success in the accurate prediction of 282 DMSCs for CO2RR through interactive learning between users and machine learning (ML) models. Among the 42 DMSCs calculated in three iteration loops of AL, 29 DMSCs were obtained, where the screening success rate was as high as 70%. Furthermore, we found five experimentally unexplored DMSCs that exhibited better CO2RR activity and selectivity than pure Bi. Low prediction errors on other DMSCs show that the AL model possessed outstanding universality. The results prove the excellent potential of the AL method and provide guidance on the design of high-performance electrocatalysts for CO2RR.

BiVO4 is one of the fascinating materials with excellent photocatalytic properties. A top-down approach composed of solid state reaction and ion exchange reaction is introduced to fabricate Ti doped BiVO4...