Abstract
Supported sub-nano Ni clusters are of great significance to many heterogeneous catalysis applications. We conducted density functional theory based particle swarm optimization calculations to study the low-energy structures of sub-nano Nin (n = 2-21) clusters in the gas phase and on two oxide surfaces, ZnO(0001̄) and γ-Al2O3(100). Our results show that sub-nano Ni clusters have magic structures, and the magic numbers are 4, 8, 11, 13, 15, and 19 on ZnO and 3, 5, 8, 11, 16, and 21 on γ-Al2O3, respectively, essentially different from gas-phase Ni clusters (magic n = 2, 6, 10, 11, 13, 20). The morphological transformation of Ni clusters relies on the interplay between the Ni-oxide interaction and the Ni-Ni interaction inside the Ni clusters. The Ni-oxide interaction becomes weakened with the cluster size growing and the Ni-ZnO interaction is generally larger than the Ni-Al2O3 interaction, while the Ni-Ni interaction is independent of the type of oxide substrate and slightly grows with the cluster size increase. On ZnO, the Ni-Ni interaction exceeds the Ni-ZnO interaction beyond the Ni10 cluster, which evolves into a double-layer structure from planar 2D configurations, while on γ-Al2O3(100), the morphological transition of the planar 2D structure towards a layered 3D structure occurs at the Ni7 cluster. Hydrogen coverage on Ni clusters lead to the conversion of the cluster morphology from layered 3D geometries to more open one-layer 2D structures. Ab initio thermodynamics analysis on Ni11Hx clusters revealed that under the typical hydrogenation conditions (T = 673 K; PH2 = 10 atm), the most stable hydrogen-containing structures are the Ni11H10 cluster on ZnO and the Ni11H8 cluster on γ-Al2O3, which both expose more Ni sites compared with the bare Ni11 cluster indicating an enhanced reactivity induced by a hydrogen environment.
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CITATION STYLE
Ma, Q., Zhu, H., Liu, D., Li, R., Li, T., Ren, H., … Guo, W. (2023). Identifying magic-number structures of supported sub-nano Ni clusters and the influence of hydrogen coverage: a density functional theory based particle swarm optimization investigation. Catalysis Science and Technology, 13(7), 2080–2091. https://doi.org/10.1039/d3cy00037k
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