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开发描述符以预测纳米管的力学性能。

Developing descriptors to predict mechanical properties of nanotubes.

机构信息

Department of Chemistry, University of North Texas, Denton, Texas 76203, USA.

出版信息

J Chem Inf Model. 2013 Apr 22;53(4):773-82. doi: 10.1021/ci300482n. Epub 2013 Mar 25.

Abstract

Descriptors and quantitative structure property relationships (QSPR) were investigated for mechanical property prediction of carbon nanotubes (CNTs). 78 molecular dynamics (MD) simulations were carried out, and 20 descriptors were calculated to build quantitative structure property relationships (QSPRs) for Young's modulus and Poisson's ratio in two separate analyses: vacancy only and vacancy plus methyl functionalization. In the first analysis, C(N2)/C(T) (number of non-sp2 hybridized carbons per the total carbons) and chiral angle were identified as critical descriptors for both Young's modulus and Poisson's ratio. Further analysis and literature findings indicate the effect of chiral angle is negligible at larger CNT radii for both properties. Raman spectroscopy can be used to measure C(N2)/C(T), providing a direct link between experimental and computational results. Poisson's ratio approaches two different limiting values as CNT radii increases: 0.23-0.25 for chiral and armchair CNTs and 0.10 for zigzag CNTs (surface defects <3%). In the second analysis, the critical descriptors were C(N2)/C(T), chiral angle, and M(N)/C(T) (number of methyl groups per total carbons). These results imply new types of defects can be represented as a new descriptor in QSPR models. Finally, results are qualified and quantified against experimental data.

摘要

研究了描述符和定量结构-性质关系(QSPR),以预测碳纳米管(CNT)的力学性能。进行了 78 次分子动力学(MD)模拟,并计算了 20 个描述符,以在两个单独的分析中建立杨氏模量和泊松比的定量结构-性质关系(QSPR):仅空位和空位加甲基官能化。在第一个分析中,C(N2)/C(T)(每总碳的非 sp2 杂化碳数)和手性角被确定为杨氏模量和泊松比的关键描述符。进一步的分析和文献发现表明,在手性角较大时,这两种性质的手性角的影响可以忽略不计。拉曼光谱可用于测量 C(N2)/C(T),为实验和计算结果之间提供了直接联系。随着 CNT 半径的增加,泊松比接近两个不同的极限值:手性和扶手椅 CNT 为 0.23-0.25,锯齿形 CNT 为 0.10(表面缺陷 <3%)。在第二个分析中,关键描述符为 C(N2)/C(T)、手性角和 M(N)/C(T)(每总碳的甲基数)。这些结果表明,新型缺陷可以作为 QSPR 模型中的新描述符。最后,对结果进行了定性和定量分析,并与实验数据进行了比较。

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