Department of Environmental Engineering and Earth Sciences, Clemson University, Anderson, SC 29625, USA.
Department of Civil, Environmental and Sustainable Engineering, Arizona State University, Tempe, AZ 85287, USA.
Water Res. 2016 Jul 1;98:28-38. doi: 10.1016/j.watres.2016.03.067. Epub 2016 Mar 31.
The objective of this paper was to create a comprehensive database for the adsorption of organic compounds by carbon nanotubes (CNTs) and to use the Linear Solvation Energy Relationship (LSER) technique for developing predictive adsorption models of organic compounds (OCs) by multi-walled carbon nanotubes (MWCNTs) and single-walled carbon nanotubes (SWCNTs). Adsorption data for 123 OCs by MWCNTs and 48 OCs by SWCNTs were compiled from the literature, including some experimental results obtained in our laboratory. The roles of selected OCs properties and CNT types were examined with LSER models. The results showed that the r(2) values of the LSER models displayed small variability for aromatic compounds smaller than 220 g/mol, after which a decreasing trend was observed. The data available for aliphatics was mainly for molecular weights smaller than 250 g/mol, which showed a similar trend to that of aromatics. The r(2) values for the LSER model on the adsorption of aromatic and aliphatic OCs by SWCNTs and MWCNTs were relatively similar indicating the linearity of LSER models did not depend on the CNT types. Among all LSER model descriptors, V term (molecular volume) for aromatic OCs and B term (basicity) for aliphatic OCs were the most predominant descriptors on both type of CNTs. The presence of R term (excess molar refractivity) in LSER model equations resulted in decreases for both V and P (polarizability) parameters without affecting the r(2) values. Overall, the results demonstrate that successful predictive models can be developed for the adsorption of OCs by MWCNTs and SWCNTs with LSER techniques.
本文的目的是创建一个关于碳纳米管（CNTs）吸附有机化合物的综合数据库，并使用线性溶剂化能关系（LSER）技术为多壁碳纳米管（MWCNTs）和单壁碳纳米管（SWCNTs）对有机化合物（OCs）的吸附建立预测模型。从文献中收集了 123 种 OC 在 MWCNTs 上的吸附数据和 48 种 OC 在 SWCNTs 上的吸附数据，其中包括我们实验室获得的一些实验结果。使用 LSER 模型检验了所选 OC 性质和 CNT 类型的作用。结果表明，对于小于 220 g/mol 的芳香族化合物，LSER 模型的 r(2) 值变化较小，之后观察到下降趋势。可用于脂肪族化合物的数据主要是分子量小于 250 g/mol 的化合物，其趋势与芳香族化合物相似。SWCNTs 和 MWCNTs 吸附芳香族和脂肪族 OC 的 LSER 模型的 r(2) 值相对相似，表明 LSER 模型的线性不取决于 CNT 类型。在所有 LSER 模型描述符中，对于芳香族 OC 的 V 项（分子体积）和对于脂肪族 OC 的 B 项（碱性）是两种 CNT 上最主要的描述符。LSER 模型方程中 R 项（过剩摩尔折射率）的存在导致 V 和 P（极化率）参数的减小，而不影响 r(2) 值。总体而言，结果表明可以使用 LSER 技术为 MWCNTs 和 SWCNTs 对 OCs 的吸附建立成功的预测模型。
