The Density Functional Theory Investigation on the Structural, Relative Stable and Electronic Properties of Bimetallic PbnSbn (n = 2–12) Clusters
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Recently, bimetallic clusters have attracted a great deal of attention from research community because clusters yield intriguing properties ranging from the molecular and the bulk materials, which have extensive applications in nanomaterials. Clusters with tailored properties are governed by cluster size, geometrical structures, and elemental composition. Motivated by that we systematically investigated the structural, relative stable, and electronic properties of PbnSbn (n = 2–12) clusters by means of density functional theory. In this paper, the ground state structures, average binding energies, fragmentation energies, HOMO–LUMO gaps, and density of states were theoretically calculated. The results demonstrate that the large clusters adopt distorted ellipsoid structures with no symmetry. The average binding energies tend to be stable when cluster size n ≥ 4. Pb5Sb5 and Pb9Sb9 clusters are more chemically stable compared with the neighboring PbnSbn clusters, which may serve as the cluster assembled materials. The density of states of PbnSbn (n = 2–12) clusters moving toward more negative energy levels with the growing cluster size n, which also becoming more nonlocalized as the clusters size n increasing.
KeywordsBimetallic clusters Geometrical structures Density functional theory Electronic properties PbnSbn clusters
This work was supported by the Regional Foundation of the NSFC (51664032), General Program of the NSFC (51474116), Program of China Scholarships Council (No. 201808530022), Joint Foundation of the NSFC-Yunnan province (U1502271), Cultivating Plan Program for the Leader in Science and Technology of Yunnan Province (2014HA003), Program for Nonferrous Metals Vacuum Metallurgy Innovation Team of Ministry of Science and Technology (2014RA4018), National Key Research and Development Program of China (2016YFC0400404), Youth Program of NSFC (51504115) and Program for Innovative Research Team in University of Ministry of Education of China (IRT_17R48), Science and Technology Talent Cultivation Plan of Yunnan Province, China (2017HB009).
- 16.X. Bai, Q. Zhang, A. Gao, and J. Yang (1009). Comput. Theor. Chem. 2013, 94–102.Google Scholar
- 18.R. O. Jones, O. Ahlstedt, J. Akola, and M. Ropo (2017). J. Chem. Phys. 146, 1291–12100.Google Scholar
- 26.V. Senz, T. Fischer, P. Oelssner, J. Tiggesbaumker, J. Stanzel, C. Bostedt, H. Thomas, M. Schoffler, L. Foucar, M. Martins, J. Neville, M. Neeb, T. Moller, W. Wurth, E. Ruhl, R. Dorner, H. Schmidt-Bocking, W. Eberhardt, G. Gantefor, R. Treusch, P. Radcliffe, and K. H. Meiwes-Broer (2009). Phys. Rev. Lett. 102, 138303.CrossRefGoogle Scholar
- 36.B. Wang, J. Zhao, X. Chen, D. Shi, and G. Wang (2005). Phys. Rev. A 71, 309–315.Google Scholar
- 48.K. O. Alcantar-Medina, M. Herrera-Trejo, A. Tlahuice-Flores, S. Martinez-Vargas, J. Oliva, and A. I. Martinez (1099). Comput. Theor. Chem. 2017, 55–63.Google Scholar
- 49.D. Toprek and V. Koteski (1081). Comput. Theor. Chem. 2016, 9–17.Google Scholar
- 52.S. Safer, S. Mahtout, K. Rezouali, M. A. Belkhir, and F. Rabilloud (1090). Comput. Theor. Chem. 2016, 23–33.Google Scholar