Structural transformation in Pd nanoclusters induced by Cu doping: an ADFT study

IF 2.1 4区 化学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
L. Santiago-Silva, H. Cruz-Martínez, H. Rojas-Chávez, L. López-Sosa, P. Calaminici
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引用次数: 0

Abstract

Context

Transition metal nanoparticles have gained great importance due to their promising applications in various fields such as energy, electronics, medicine, and agriculture. For these applications, materials with outstanding properties are currently required. Therefore, different strategies have been established to improve the properties of pure nanoparticles such as alloying, doping, and formation of composites. Among these strategies, doping is gaining great importance because it has been demonstrated that doped nanoparticles have better properties than pure nanoparticles. Therefore, it is essential to know the role of doping on the structures and properties of clusters with more than 16 atoms. Consequently, in this study, we propose a theoretical study of structures and properties focusing on pure Pd19, Cu-doped Pd18 (Pd18Cu), and Cu2-doped Pd17 (Pd17Cu2) nanoclusters and thus elucidate the role of Cu atoms on the structures and properties of larger doped Pd nanoclusters than those already presented in the literature. We have selected a nanocluster with 19 atoms since the most stable structure of this system is characterized by defined shapes such as octahedron or double-icosahedron.

Methods

Ground state structures and properties of Pd19, Pd18Cu, and Pd17Cu2 nanoclusters were studied using the auxiliary density functional theory (ADFT), as implemented in the deMon2k code. For obtaining the ground state structures of Pd19, Pd18Cu, and Pd17Cu2 nanoclusters, several dozen initial structures were taken along Born–Oppenheimer molecular dynamics (BOMD) trajectories and subsequently optimized without symmetry restrictions. The optimizations were performed with the revised PBE functional in combination with TZVP-GGA for the Cu atoms and using an 18-electron QECP|SD basis set for the Pd atoms. Different energetic and electronic properties were calculated for the most stable structures of Pd19, Pd18Cu, and Pd17Cu2 nanoclusters. Interestingly, when the Pd nanocluster is doped with two Cu atoms (Pd17Cu2), there is a structural transition, because the most stable structures for Pd19 and Pd18Cu are icosahedral. While the Pd17Cu nanocluster is characterized for a double-icosahedral-base structure. The binding energy per atom increases when the Cu concentration in the nanoclusters increases. According to the HOMO–LUMO gap, the chemical reactivity of the nanoclusters tends to increase as the Cu content in the nanoclusters increases.

Cu掺杂诱导Pd纳米团簇结构转变的ADFT研究
过渡金属纳米颗粒在能源、电子、医药和农业等各个领域具有广阔的应用前景,因此得到了广泛的重视。对于这些应用,目前需要具有优异性能的材料。因此,人们建立了不同的策略来改善纯纳米颗粒的性能,如合金化、掺杂和复合材料的形成。在这些策略中,掺杂越来越受到重视,因为已经证明掺杂纳米颗粒比纯纳米颗粒具有更好的性能。因此,了解掺杂对16个以上原子团簇结构和性质的影响是十分必要的。因此,在本研究中,我们提出了对纯Pd19, Cu掺杂Pd18 (Pd18Cu)和cu2掺杂Pd17 (Pd17Cu2)纳米团簇的结构和性质的理论研究,从而阐明了Cu原子对更大的掺杂Pd纳米团簇的结构和性质的作用。我们选择了一个有19个原子的纳米团簇,因为该体系最稳定的结构特征是八面体或双二十面体。方法利用辅助密度泛函理论(ADFT)研究了Pd19、Pd18Cu和Pd17Cu2纳米团簇的基态结构和性质。为了获得Pd19、Pd18Cu和Pd17Cu2纳米团簇的基态结构,我们沿着Born-Oppenheimer分子动力学(BOMD)轨迹取了几十个初始结构,然后在没有对称限制的情况下进行了优化。利用修正后的PBE泛函结合TZVP-GGA对Cu原子进行优化,并使用18个电子的QECP|SD基集对Pd原子进行优化。计算了Pd19、Pd18Cu和Pd17Cu2纳米团簇最稳定结构的能量和电子性质。有趣的是,当Pd纳米簇中掺杂两个Cu原子(Pd17Cu2)时,会发生结构转变,因为Pd19和Pd18Cu最稳定的结构是二十面体结构。而Pd17Cu纳米团簇具有双二十面体碱基结构。随着纳米团簇中Cu浓度的增加,每个原子的结合能增加。根据HOMO-LUMO间隙,随着Cu含量的增加,纳米团簇的化学反应活性有增加的趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Molecular Modeling
Journal of Molecular Modeling 化学-化学综合
CiteScore
3.50
自引率
4.50%
发文量
362
审稿时长
2.9 months
期刊介绍: The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling. Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry. Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.
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