The more complex structure of active metals on supports poses a challenge for studying the metal cluster structure and interactions of loaded catalysts. In this work, we investigated the nucleation growth of small Co clusters (up to Co6) on the surface of CeO2(110) using density functional theory, from which a stable loaded Co/CeO2(110) structure was selected for studying the activation mechanism of methane. A detailed nucleation pattern is demonstrated by depositing Co clusters obtained from the face-centered cubic structure of Co on the surface of CeO2(110). Despite the relatively small size of the selected Co clusters, the obtained Cox/CeO2(110) exhibits interesting features. Our study indicated that the oxygen ions on the CeO2(110) surface are extremely unstable and prone to form oxygen vacancy defects, which is particularly evident in the structure species loaded with Co clusters. The surface oxygen ions close to the initial adsorption site are susceptible or inclined to be detached from the surface by the effect of metallic Co during the optimization process. Cox on the CeO2(110) surface tends to grow more toward the cluster structure, i.e., Co4 is more likely to form a spatial tetrahedral structure rather than a planar rhombic structure. Interestingly, the optimized Co5/CeO2(110) structure was chosen as the optimal structure to study the activation mechanism of methane because of its competitive electronic structure, adsorption energy and binding energy. The metal site adsorption of methane on the Co5/CeO2(110) surface is thermodynamically favorable. Moreover, the energy barrier and reaction energy to be overcome for the dissociation of methane from the first C-H bond are 10.104 and -9.486 Kcal/mol, respectively. This theoretical study of Co growth on the CeO2(110) surface contributes to understand metal-support interactions and explain the promising catalytic behavior of Co/CeO2 catalysts. Provide theoretical guidance for better design of optimal Co/CeO2 catalysts for tailored catalytic reactions.
This paper considers the planning effectiveness and operational efficiency of battery energy storage systems (BESSs). A multi-objective particle swarm optimization (MOPSO) algorithm is proposed to solve the optimal solution of BESSs charging and discharging operation strategy; the outer layer aims to minimize the investment and operation cost of BESSs, voltage fluctuation and load fluctuation of distribution network. MOPSO algorithm solves the Pereto non-dominated solution set of the location-determined-volume planning scheme. The proposed algorithm achieves a balance between local exploration and global search, and effectively obtains high-quality solutions. Finally, the simulation results based on the IEEE-33 node system show that compared with the traditional multi-objective optimization algorithm, the proposed algorithm can obtain a more widely distributed and uniform Pareto front, which not only realizes the optimal investment benefit of BESSs, but also realizes the distribution of power distribution. The grid voltage quality and power stability are also significantly better than other algorithms.
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