pt修饰(9,0)单壁碳纳米管的DFT研究:氢吸附与传感

IF 2.5 4区 化学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Hongbing Shi, Shengping Yu, Fuming Chen, Minzhang Li
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引用次数: 0

摘要

碳纳米管通常用于氢检测。然而,纯碳纳米管在这一应用中表现出局限性。pt修饰的碳纳米管被认为具有吸附和传感氢的潜力。利用密度泛函理论(DFT)计算研究了H2在(9,0)单壁碳纳米管(SWCNT(9,0))外壁上的吸附。由于吸附能很小(0.023 eV),表明H2在纯swcnts外壁上具有较高的吸附势垒,因此采用金属原子(Pt, Fe, Ni)修饰swcnts。结果表明,Pt-SWCNT(9,0)具有最高的氢吸附容量(0.791 eV),同时也是最稳定的催化剂结构。氢分子在三种体系中均发生了吸附和解离。进一步讨论了Pt-SWCNT(9,0)和H2/Pt-SWCNT(9,0)的电子结构。有趣的是,Pt原子与SWCNT(9,0)的相互作用改变了碳纳米管的电子态,增强了H2的吸附能。在包含多个H2分子的构型中,大多数分子在Pt-SWCNT上表现为物理吸附(9,0),而只有一个分子表现为化学吸附。轨道杂化的增强促进了Pt的电子转移。吸附H2后,Pt-SWCNT的HOMO-LUMO间隙(Eg)增大,电导率(σ)(9,0)发生变化,与实验结果一致。我们的计算表明,pt修饰的SWCNT(9,0)具有吸附和传感氢的能力。未来,材料结构的设计将调整电导率,以获得更好的H₂传感器。方法采用Gaussian 16软件进行DFT计算。利用GaussView 6.0软件对所有相关物种的分子模型、分子轨道和静电势(ESP)进行可视化。利用Multiwfn 3.8软件绘制了定位轨道定位器(LOL)图和电子定位函数(ELF)分布图。DFT方法为B3LYP-D3BJ。C、H原子采用6-31G(d,p)基集,Pt、Fe、Ni原子采用Lanl2dz基集。在结构优化和频率计算中没有使用影响气相计算精度的额外计算参数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DFT study on Pt-decorated (9,0) single-walled carbon nanotubes: hydrogen adsorption and sensing

Context

Carbon nanotubes are commonly used for hydrogen detection. However, pure carbon nanotubes exhibit limitations in this application. The Pt-decorated carbon nanotubes are considered to have the potential of hydrogen adsorption and sensing. The adsorption of H2 on the outside wall of (9,0) single-walled carbon nanotubes (SWCNT(9,0)) were studied using density functional theory (DFT) calculations. The tiny adsorption energy (0.023 eV) implied a high barrier of the adsorption of H2 on the outer wall of pure SWCNT, so metal atom (Pt, Fe, Ni) was used to decorate SWCNT. The result showed that Pt-SWCNT(9,0) exhibited the highest hydrogen adsorption capacity (0.791 eV) and meanwhile as the most stable catalyst structure. The hydrogen molecule underwent adsorption and dissociation on all three systems. Furthermore, the electronic structures of Pt-SWCNT(9,0) and H2/Pt-SWCNT(9,0) were discussed. It is interesting that the interaction between Pt atom and SWCNT(9,0) changed the electronic state of carbon nanotubes and enhanced the adsorption energy of H2. In the configurations involving multiple H2 molecules, most molecules exhibited physical adsorption on Pt-SWCNT(9,0), whereas only one molecule was chemical adsorption. The electron transfer of Pt was promoted with the enhancement of orbital hybridization. In addition, the HOMO–LUMO gap (Eg) increased and the conductivity (σ) of Pt-SWCNT(9,0) changed after the adsorption of H2, which was according with the experimental studies. Our calculations reveal that Pt-decorated SWCNT(9,0) has the capabilities of hydrogen adsorption and sensing. In the future, the design of the material structure will tune the conductivity for better H₂ sensor.

Methods

All DFT calculations were performed using the Gaussian 16 software. GaussView 6.0 software was utilized to visualize the molecular models, molecular orbitals, and electrostatic potential (ESP) of all relevant species. Multiwfn 3.8 software was utilized to plot localized orbital locator (LOL) graph and electron localization function (ELF) distribution maps. The DFT method was the B3LYP-D3BJ. The 6-31G(d,p) basis set was utilized for the C, H atoms, and Lanl2dz basis set for the Pt, Fe, and Ni atoms. No extraneous computational parameters were used in the structure optimization and frequency calculation that could affect calculation accuracy in the gas phase.

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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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