JOURNAL ARTICLE

Density Functional Theory Study of Structural Evolution, Relative Stability, and Electronic Properties of SnnAln (n = 2–12) Clusters.

  • Published In: Physica Status Solidi (B), 2024, v. 261, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zhang, Wei; Zhang, Huan; Li, Yifu; Chen, Xiumin; Yang, Bin; Xu, Baoqiang 3 of 3

Abstract

The ground‐state structure, average binding energy (Eb), fragmentation energies (ΔE), second‐order difference energy (Δ2E), gap between the highest occupied molecular orbital and the lowest unoccupied molecular orbital (HOMO‐LUMO), vertical ionization potential (VIP), vertical electron affinity (VEA), and charge transfer of SnnAln (n = 2–12) clusters are evaluated by density functional theory (DFT). With an increase in the cluster size n, Al atoms aggregate in the geometric center of the cluster, and Sn atoms encapsulate Al atoms in the periphery of the cluster. The variation of ΔE, Δ2E, the HOMO‐LUMO gap, VIP, and VEA of the SnnAln clusters follows similar trends, with certain odd–even oscillations. However, beyond the cluster size n ≥ 9, the HOMO‐LUMO gap decreases significantly, and the VIP and VEA fluctuate significantly, indicating a deterioration of the relative stability of the cluster. Charge transfer proceeds from the Sn atom to the Al atom. This study provides an analysis of the interaction between Sn and Al at the atomic level and explains the segregation phenomenon during the preparation of tin‐aluminum alloys. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Physica Status Solidi (B). 2024/02, Vol. 261, Issue 2, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:0370-1972
  • DOI:10.1002/pssb.202300438
  • Accession Number:175417781
  • Copyright Statement:Copyright of Physica Status Solidi (B) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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