Jiangsu Provincial Laboratory of Water Environmental Protection Engineering, School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
School of Civil Engineering, Southeast University, Nanjing, 210096, China.
Environ Sci Pollut Res Int. 2020 May;27(15):17550-17560. doi: 10.1007/s11356-019-06389-z. Epub 2019 Sep 6.
The partition coefficient is essential for the analysis of organic chemicals using solid-phase microextraction (SPME) techniques. In this study, a quantitative structure-property relationship (QSPR) model was developed with chemical descriptors for the prediction of the polyacrylate (PA)-water partition coefficient (K). The major variables influencing K in the QSPR model were CrippenlogP (crippen octanal-water partition coefficient), RNCG (relative negative charge-most negative charge/total negative charge), VE2_Dzv (average coefficient sum of the last eigenvector from the Barysz matrix/weighted by van der Waals volume), and ATSC4v (centred Broto-Moreau autocorrelation-lag 4/weighted by van der Waals volume). The relative determination coefficient (R) and cross-validation coefficient (Q) were 0.898 and 0.858, respectively, which implied that the model had excellent robustness. Mechanistic interpretation suggested that the factors affecting the partitioning process between PA and water are the hydrophobicity, relative negative charge, and van der Waals volume of a chemical. The results of this study provide a good tool for predicting the log K values of diverse hydrophobic organic compounds (HOCs) within the applicability domain to reduce experimental costs and the time required for innovation.
分配系数对于使用固相微萃取（SPME）技术分析有机化学品至关重要。在这项研究中，建立了一个定量结构-性质关系（QSPR）模型，使用化学描述符来预测聚丙烯酸酯（PA）-水分配系数（K）。在 QSPR 模型中，影响 K 的主要变量是 CrippenlogP（克里彭辛醇-水分配系数）、RNCG（相对负电荷-最负电荷/总负电荷）、VE2_Dzv（Barysz 矩阵最后一个特征向量的平均系数之和/范德华体积加权）和 ATSC4v（中心 Broto-Moreau 自相关滞后 4/范德华体积加权）。相对确定系数（R）和交叉验证系数（Q）分别为 0.898 和 0.858，这表明该模型具有出色的稳健性。机理解释表明，影响 PA 和水之间分配过程的因素是化学物质的疏水性、相对负电荷和范德华体积。本研究的结果为预测不同疏水性有机化合物（HOC）在适用范围内的 log K 值提供了一个很好的工具，以降低实验成本和创新所需的时间。
