有机污染物在碳纳米管吸附的定量结构-性质关系

引用本文: 刘芬, 邹建卫, 胡桂香, 蒋勇军. 有机污染物在碳纳米管吸附的定量结构-性质关系[J]. 物理化学学报, 2014, 30(9): 1616-1624. doi: 10.3866/PKU.WHXB201406182 shu

Citation: LIU Fen, ZOU Jian-Wei, HU Gui-Xiang, JIANG Yong-Jun. Quantitative Structure-Property Relationship Studies on the Adsorption of Aromatic Contaminants by Carbon Nanotubes[J]. Acta Physico-Chimica Sinica, 2014, 30(9): 1616-1624. doi: 10.3866/PKU.WHXB201406182 shu

有机污染物在碳纳米管吸附的定量结构-性质关系

基金项目:
国家自然科学基金（21272211)

宁波市科技计划（2011A610021，2013D1003）资助项目

摘要:

对一系列共59个芳香性分子进行了HF/6-31G^*水平上的结构优化，并在优化结构上进行了分子静电势及其导出参数的计算，应用多元线性回归方法建立了碳纳米管吸附有机污染物的平衡常数与分子结构间的定量关系. 结果表明，分子表面静电势参数（V_min、σ₊² 和ΣV_ind⁺）结合分子表面积（S）和最低空轨道能级（ε_LUMO）可以很好地用于构建碳纳米管吸附的定量结构-性质关系（QSPR）模型. 模型中引入的参数均具有明确的物理意义，其合理性可以从污染物与碳纳米管或水分子间相互作用的角度进行解释. 模型的稳定性和预测能力经“留一法”和Monte Carlo 交叉验证法进行了确证. 本文亦采用支持向量机（SVM）、最小二乘支持向量机（LSSVM）和高斯过程（GP）等三种方法建立了上述参数与碳纳米管吸附性质的非线性模型. SVM和LSSVM模型表现出强的拟合能力，但预测能力明显不如其他模型. GP模型无论是拟合能力还是预测能力都是最佳，但并没有明显地优于线性模型，说明对本文研究体系而言，其分子结构与性质间的关系主要以线性形式存在.

关键词:

English

Quantitative Structure-Property Relationship Studies on the Adsorption of Aromatic Contaminants by Carbon Nanotubes

1.
1. College of Biological and Chemical Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, Zhejiang Province, P. R. China;
2. Department of Chemical Engineering and Biological Engineering, Zhejiang University, Hangzhou 310027, P. R. China
Received Date: 30 April 2014
Available Online: 29 August 2014
Fund Project:
国家自然科学基金（21272211)

宁波市科技计划（2011A610021，2013D1003）资助项目

Abstract:

Ab initio calculations have been performed for a group of 59 aromatic compounds at the HF/6-31G^* level of theory. Electrostatic potentials (ESPs) and the statistically based structural descriptors derived from ESPs on the molecular surface have been obtained. The linear relationships between the adsorption equilibrium constants of organic contaminants by carbon nanotubes and the theoretical descriptors have been established by multiple linear regression. It is shown that the quantities derived from electrostatic potentials, V_min, σ₊² and ΣV_ind⁺ together with the molecular surface area (S) and the energy level of lowest occupied molecular orbital (ε_LUMO) can be used to express the quantitative structure-property relationship (QSPR) of this sample set. All of the descriptors introduced in the QSPR models have definite physical meanings and their reasonability can be explained in terms of intermolecular interactions between the aromatic pollutants and carbon nanotubes or water. The stabilities and predictive powers of the models have been validated by "leave-one-out" and Monte Carlo cross-validation methods. Three nonlinear modeling techniques, namely supported vector machine (SVM), least-square supported vector machine (LSSVM), as well as Gaussian process (GP), have also been used to construct the predictive models. Though the SVM and LSSVM models exhibit strong fitting abilities, their predictive powers are inferior to the other models tested. The GP model yields the best fit and predictive ability among all of the models. Its advantage over the linear model, however, is not as remarkable as expected, which means that the relationship between the molecular structure and the adsorption property for the present system is primarily linear.

Key words:

Carbon nanotube
/ Organic contaminant
/ Electrostatic potentials on molecular surface
/ Quantitative structure-property relationship
/ Gaussian process

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1.
沈阳化工大学材料科学与工程学院沈阳 110142

有机污染物在碳纳米管吸附的定量结构-性质关系

English

Quantitative Structure-Property Relationship Studies on the Adsorption of Aromatic Contaminants by Carbon Nanotubes

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通讯作者: 陈斌, bchen63@163.com

中国化学会秘书处

有机污染物在碳纳米管吸附的定量结构-性质关系

English

Quantitative Structure-Property Relationship Studies on the Adsorption of Aromatic Contaminants by Carbon Nanotubes

CCS Chemistry：嵌段共聚物自组装成 “三明治”二维有机材料，实现纳米级压力传感功能

CCS Chemistry：颜徐州、鲍哲南： 你的皮肤可能是一片SPMs

CCS Chemistry：工作高效不疲劳，只须让催化剂动起来

CCS Chemistry：静电组装导向聚合- 聚电解质纳米凝胶量化制备新策略

CCS Chemistry：金黄色葡萄球菌醛基脱氢酶是如何识别特异性底物的？

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