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Prediction of subcooled vapor pressures (log PL) of 399 polychlorinated trans-azoxybenzenes by using the QSPR and ANN approach
Environmentally relevant partitioning properties such as the sub-cooled vapor pressures (log PL) have been predicted for 399 congeners of chloro-trans-azoxybenzene (C-t-AOBs) by two computational methods. The quantitative structure-property relationship (QSPR), an approach which is based on geometry optimalization and quantum-chemical structural descriptors in RM1 and DFT methods and artificial neural networks (ANNs), an approach that predicts abilities that give similar results of estimated log PL and the accuracy of the methods was also similar. The RM1 method was less time consuming and less costly compared to calculations by the DFT method. Estimated from the RM1 and DFT methods of log PL values of 399 Ct-AOBs varied between -1.98 to -0.93 and -1.83 to -0.79 for Mono-, 3.12 to -1.46 and -3.00 to -1.46 for Di-, -4.03 to -1.39 and -3.53 to -1.67 for Tri-, -4.75 to -2.33 and -4.59 to -1.91 for Tetra-, -5.37 to -2.59 and -5.42 to -2.09 for Penta-, -5.82 to -2.88 and -5.66 to -2.58 for Hexa-, -5.88 to -3.24 and -5.60 to -2.93 for Hepta-, -6.28 to -4.33 and -5.60 to -4.29 for Octa-, -6.54 to -5.28 and -5.66 to -4.93 for NonaCt-AOBs, and -6.59 and -5.61 for DecaCt-AOB. According to a common classification of environmental contaminants and by sub-cooled vapor pressure values, MonoCt-AOBs and a few of the Di- and TriCt-AOBs (log PLfrom -2 to 0) fall into the group of compounds that are relatively well mobile in the ambient environment, while most of the Di- to HeptaCt-AOBs (log PL < -4 to -2) mobility is relatively weak. Octa- and NonaCt-AOBs and DecaCt-AOB (log PL < -4) are also weak mobile contaminants.
Prediction of subcooled vapor pressures (log PL) of 399 polychlorinated trans-azoxybenzenes by using the QSPR and ANN approach
Environmentally relevant partitioning properties such as the sub-cooled vapor pressures (log PL) have been predicted for 399 congeners of chloro-trans-azoxybenzene (C-t-AOBs) by two computational methods. The quantitative structure-property relationship (QSPR), an approach which is based on geometry optimalization and quantum-chemical structural descriptors in RM1 and DFT methods and artificial neural networks (ANNs), an approach that predicts abilities that give similar results of estimated log PL and the accuracy of the methods was also similar. The RM1 method was less time consuming and less costly compared to calculations by the DFT method. Estimated from the RM1 and DFT methods of log PL values of 399 Ct-AOBs varied between -1.98 to -0.93 and -1.83 to -0.79 for Mono-, 3.12 to -1.46 and -3.00 to -1.46 for Di-, -4.03 to -1.39 and -3.53 to -1.67 for Tri-, -4.75 to -2.33 and -4.59 to -1.91 for Tetra-, -5.37 to -2.59 and -5.42 to -2.09 for Penta-, -5.82 to -2.88 and -5.66 to -2.58 for Hexa-, -5.88 to -3.24 and -5.60 to -2.93 for Hepta-, -6.28 to -4.33 and -5.60 to -4.29 for Octa-, -6.54 to -5.28 and -5.66 to -4.93 for NonaCt-AOBs, and -6.59 and -5.61 for DecaCt-AOB. According to a common classification of environmental contaminants and by sub-cooled vapor pressure values, MonoCt-AOBs and a few of the Di- and TriCt-AOBs (log PLfrom -2 to 0) fall into the group of compounds that are relatively well mobile in the ambient environment, while most of the Di- to HeptaCt-AOBs (log PL < -4 to -2) mobility is relatively weak. Octa- and NonaCt-AOBs and DecaCt-AOB (log PL < -4) are also weak mobile contaminants.
Prediction of subcooled vapor pressures (log PL) of 399 polychlorinated trans-azoxybenzenes by using the QSPR and ANN approach
Piliszek, Sławomir (Autor:in) / Wilczyńska-Piliszek, Agata J. (Autor:in) / Falandysz, Jerzy (Autor:in)
Journal of Environmental Science and Health, Part A ; 47 ; 450-462
01.02.2012
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
| Taylor & Francis Verlag | 2012