铅烯导热性的分子动力学模拟研究

IF 2.9 3区 化学 Q3 CHEMISTRY, PHYSICAL
Rafat Mohammadi, Behrad Karimi, John Kieffer and Daniel Hashemi
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引用次数: 0

摘要

通过人工神经网络优化特尔索夫电势和斯蒂尔林格-韦伯电势的参数,我们克服了现有的限制,利用分子动力学模拟研究了铅的晶格热导率。我们的研究结果表明,在室温下,1050 Å × 300 Å 铅片的热导率约为 8 W/m.K,明显低于块状铅片的热导率(23%)。我们的分析表明,热导率会随着样品长度的增加而增加,但会随着温度的升高而降低。此外,与具有扶手椅边缘的铅铂样品相比,具有人字形边缘的铅铂样品具有更高的热导率。此外,铅笔烯的热导率在低拉伸应变时会增加,而随着应变的增大则会降低。这项研究为了解不同条件下铅笔烯的导热性能提供了重要依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A molecular dynamics simulation study of thermal conductivity of plumbene

A molecular dynamics simulation study of thermal conductivity of plumbene

We investigate the lattice thermal conductivity of plumbene using molecular dynamics simulations, overcoming existing limitations by optimizing the parameters of Tersoff and Stillinger–Weber potentials via artificial neural networks. Our findings indicate that at room temperature, the thermal conductivity of a 1050 Å × 300 Å plumbene sheet is approximately 8 W m−1 K−1, significantly lower (23%) than that of bulk lead. Our analysis elucidates that thermal conductivity is enhanced by increased sample length, while it is reduced by temperature. Moreover, plumbene samples with zigzag edges display superior thermal conductivity compared to those with armchair edges. In addition, the thermal conductivity of plumbene exhibits an increase at low tensile strains, whereas it decreases as the strains become larger. This investigation provides crucial insights into the thermal conductivity behavior of plumbene under varying conditions.

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来源期刊
Physical Chemistry Chemical Physics
Physical Chemistry Chemical Physics 化学-物理:原子、分子和化学物理
CiteScore
5.50
自引率
9.10%
发文量
2675
审稿时长
2.0 months
期刊介绍: Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
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