Chloronaphthalenes （PCNs, polychlorinated naphthalenes） are a group of persistent environmental pollutants. In the present study, geometrical optimization and electrostatic potential calculations have been performed...Chloronaphthalenes （PCNs, polychlorinated naphthalenes） are a group of persistent environmental pollutants. In the present study, geometrical optimization and electrostatic potential calculations have been performed for all 75 PCNs at the HF/6-31G^＊ level of theory. A number of statistic based parameters have been extracted. Linear relationships between gas-chromatographic retention index （RI） of 62 PCNs in a non-polar column （DB-5） and the structural descriptors have been established by stepwise multiple regression technique. The result shows that two quantities derived from electrostatic potential on molecular surface, ∑Vs- and σ＋^2, together with the number of chlorine （ NCl ） and the energy of the highest occupied molecular orbital （EHOMO） can be well used to express the quantitative structure-retention relationship （QSRR） of PCNs. Predictive capability of the model has been demonstrated by leave-one-out cross-validation with the cross-validated correlation coefficient （Rcv^2） of 0.997, and further compared with the results from similar researches published recently. Furthermore, when splitting the 62 PCNs into training and validation sets in the ratio of 2：1, a similar treatment yields an equation of almost equal statistical quality and very similar regression coefficients, validating the robustness and prediction capability of our model. The QSRR model established may provide a new powerful method for predicting chromatographic properties of polychlorinated naphthalenes.展开更多
基金Supported by the Analysis and Measurement Planning Project of Zhejiang Province (2007F70053)
文摘Chloronaphthalenes （PCNs, polychlorinated naphthalenes） are a group of persistent environmental pollutants. In the present study, geometrical optimization and electrostatic potential calculations have been performed for all 75 PCNs at the HF/6-31G^＊ level of theory. A number of statistic based parameters have been extracted. Linear relationships between gas-chromatographic retention index （RI） of 62 PCNs in a non-polar column （DB-5） and the structural descriptors have been established by stepwise multiple regression technique. The result shows that two quantities derived from electrostatic potential on molecular surface, ∑Vs- and σ＋^2, together with the number of chlorine （ NCl ） and the energy of the highest occupied molecular orbital （EHOMO） can be well used to express the quantitative structure-retention relationship （QSRR） of PCNs. Predictive capability of the model has been demonstrated by leave-one-out cross-validation with the cross-validated correlation coefficient （Rcv^2） of 0.997, and further compared with the results from similar researches published recently. Furthermore, when splitting the 62 PCNs into training and validation sets in the ratio of 2：1, a similar treatment yields an equation of almost equal statistical quality and very similar regression coefficients, validating the robustness and prediction capability of our model. The QSRR model established may provide a new powerful method for predicting chromatographic properties of polychlorinated naphthalenes.
