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A Study on Pathway and QSPR Models for Debromination of PBDEs with Pseudopotential Method

机译:具有假软障方法的途径和QSPR模型的途径与QSPR模型

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Neutral PBDEs congeners and their corresponding radical anions were studied with the pseudopotential method of Stuttgart group (SDD) effective-core potentials basis set for the bromine atoms and the all-electron basis set for all other atoms. The pseudopotential method can be used for compounds containing heavy elements with relativistic effects and can reduce the computational time. The quantitative structure property relationship (QSPR) study was also performed in this work to develop models to predict the normolized reaction rate constants for the reductive debromination of polybrominated diphenyl ethers (PBDEs) by zero-valent iron (ZVI). The partial least squares regression (PLSR), principal component analysis-multiple linear regression analysis (PCA-MLR), and back propagation artificial neural network (BP-ANN) approaches were employed for the QSPR study between the molecular descriptors and the logarithm of normalized reaction rate constants of fourteen selected BDE congeners. The results show that the ANN models could be more satisfactorily to predict the rate constants than the PLSR and PCA-MLR models.
机译:中性多溴二苯醚同源物和它们相应的自由基阴离子进行了研究与斯图加特组(SDD)的溴原子和所有其它原子的所有电子基组有效芯电位的基础上设置赝势法。赝势方法可以用于含重元素与相对论效应化合物,并且可以减少计算时间。的定量结构性质相关(QSPR)研究也在这个工作进行开发模型来预测normolized反应速率常数由零价铁(ZVI)多溴二苯醚(PBDE类)的还原脱溴。偏最小二乘回归(PLSR),主成分分析,多重线性回归分析(PCA-MLR),和反向传播神经网络(BP-ANN)方法被用于该分子描述符和的归一化的对数之间的QSPR研究的14个选定溴化二苯醚同源反应速率常数。结果表明,人工神经网络模型可以更令人满意预测的速率常数比PLSR和PCA-MLR模型。

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