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首页> 外文期刊>Journal of environmental science and health >The aqueous solubility of some herbicidal by-side toxic impurities: Predicted data of the 399 chlorinated trans-azoxybenzene congeners
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The aqueous solubility of some herbicidal by-side toxic impurities: Predicted data of the 399 chlorinated trans-azoxybenzene congeners

机译：某些除草副产物有毒杂质的水溶性：399种氯化反式偶氮苯的同类物的预测数据

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The quantitative structure - property relationship (QSPR) and the artificial neural networks (ANNs) methods were used to estimate aqueous solubility (log S and μg/L) of polychlorinated trans-azoxybenzenes (PCt-ABs). These QSPR and ANN models are based on geometry optimalization and quantum-chemical structural descriptors, which were computed on the level of density functional theory (DFT) using B3LYP functional and 6-311++G~(**) basis set in Gaussian 03 software and the semi-empirical quantum chemistry method for property parameterization (RM1) in the molecular orbital package (MOPAC) software. The predicted solubility of PCt-AOBs by RM1 and DFT models and depending on a congener varied within a homologue class between 47-19498 and 371-1738 μg/L for Mono-; 33-11481 and 7.9-3630 /μg/L for Di-; 6.1-4786 and 4.7-12882 μg/L for Tri-; 1.3-1174 and 0.3-14791 μg/L for Tetra-; 0.4-646 and 0.1-38904 μg/L for Penta-; 0.1-155 and 0.2-63096 μg/L for Hexa-; 0.2-27 and 0.1-646 μg/L for Hepta-; < 0.1-6.2 and 0.8-282 μg/L for Octa-; 0.6-2.6 and 0.8-12 μg/L for NonaCt-AOBs; and 1.2 and 0.5 μg/L for DecaCt-AOB, respectively. Both computational models used were characterized by good predictive abilities and small errors, while calculations by RM1 method were highly competitive compared to a much more time-consuming and expensive method by DFT.
机译：定量结构-性质关系（QSPR）和人工神经网络（ANNs）方法用于估算多氯反式-偶氮苯（PCt-ABs）的水溶性（log S和μg/ L）。这些QSPR和ANN模型基于几何优化和量子化学结构描述符，这些模型是使用高斯03中设置的B3LYP泛函和6-311 ++ G〜（**）基在密度泛函理论（DFT）级别上计算的分子轨道包装（MOPAC）软件中用于属性参数化（RM1）的软件和半经验量子化学方法。通过RM1和DFT模型预测的PCt-AOB的溶解度，取决于同源物，对于Mono-，在47-19498和371-1738μg/ L的同系物类别内变化； Di-; 33-11481和7.9-3630 /μg/ L Tri-为6.1-4786和4.7-12882μg/ L； Tetra-为1.3-1174和0.3-14791μg/ L；五溴-0.4-646和0.1-38904μg/ L Hexa-为0.1-155和0.2-63096μg/ L； Hepta-为0.2-27和0.1-646μg/ L；对于Octa- <0.1-6.2和0.8-282μg/ L; NonaCt-AOB的浓度为0.6-2.6和0.8-12μg/ L； DecaCt-AOB分别为1.2和0.5μg/ L。所使用的两种计算模型均具有良好的预测能力和较小的误差，而与DFT相比，使用RM1方法进行的计算更具竞争性，而后者耗时且昂贵。

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