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首页> 外文期刊>Analytica chimica acta >Study of gas-liquid partitioning of alkane solutes in several organic solvents by using principal component analysis and linear solvation energy relationships
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Study of gas-liquid partitioning of alkane solutes in several organic solvents by using principal component analysis and linear solvation energy relationships

机译:利用主成分分析和线性溶剂化能量关系研究烷烃溶质在几种有机溶剂中的气液分配

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摘要

Principal component analysis (PCA)was used to extract the number of factors which can descripe the 737 gas-liquid partition coefficients of five limera four baranched and two cyclic alkanes in 67 common solvents Base on te reconstruction of partition coefficient data matrix we concluded that the experimental dataset couldreadily be resuced to two relevent factors Using only these two factors theere were no errors larger than 3% 7 cases had errosr larger than 2% and in 34 cases errors were between 1 and 2% n-Hexane and ethylcyclohexane were chose as the test factors and all other partitin coefficients were expressed in term of these two test factors Prediction of the logarithmic partition coefficient of these alkanes in seven chemically different solvents which were originaly excluded fromthe data matrix was excellent the root maan square error was 0.064 only in 11 cases the errors were larger than 1% and only 3 had errors larger than 4%.Linear solvatin energy relatinships (LSERs) using both theoretical and empirical solvent paramethers wer eused to explain teh molecular interadtons responsible for partitin Sevedral combinatins of parameters were tried but th estandard deviations were not less than 0.31 This could be attriguted to the model itself imprecisions in the data matriz or in some of the LSER parameters Solvent cohesive parameters snd surface tension in combination with polarity-polarizability or dispersion parameters perform the best.Finally the two principal component factors were rotated onto the most relevat physicochemical parameters that conrol the gas-liquid partitioning phemomena.
机译:利用主成分分析(PCA)提取了可以描述67种常见溶剂中的5种石灰,4种直链烷烃和2种环烷烃的737气液分配系数的因素数量。基于分配系数数据矩阵的重建,我们得出结论:实验数据集可以容易地归结为两个相关因素,仅使用这两个因素,误差不大于3%的情况下误差不超过2%的7例,而误差34%的情况下,正己烷和乙基环己烷的误差在1-2%之间。测试因子和所有其他partitin系数均用这两个测试因子表示。预测这些烷烃在最初从数据矩阵中排除的七种化学不同溶剂中的对数分配系数非常好,仅11种情况的均方根误差为0.064误差大于1%,只有3个误差大于4%。线性溶剂溶剂能量相关性(LSER)使用尝试使用理论和经验上的溶剂对位异构体来解释负责partitin的分子内结合物尝试了参数的组合法,但标准偏差不小于0.31。这可能是由于模型本身在数据矩阵或某些LSER中的不精确性参数溶剂内聚参数和表面张力与极性-极化率或分散参数结合在一起表现最佳。最后,将两个主成分因子旋转到最相关的理化参数上,从而控制了气液分配现象。

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