Based on the location of bromine substituents and conjugation matrix, a new substituent po- sition index ~X not only was defined, but also molecular shape indexes Km and electronega- tivity distance vectors Mm of diph...Based on the location of bromine substituents and conjugation matrix, a new substituent po- sition index ~X not only was defined, but also molecular shape indexes Km and electronega- tivity distance vectors Mm of diphenylamine and 209 kinds of polybrominated diphenylamine （PBDPA） molecules were calculated. Then the quantitative structure-property relationships （QSPR） among the thermodynamic properties of 210 organic pollutants and 0X, K3, M29, M36 were founded by Leaps-and-Bounds regression. Using the four structural parameters as input neurons of the artificial neural network, three satisfactory QSPR models with network structures of 4：21：1, 4：24：1, and 4：24：1 respectively, were achieved by the back-propagation algorithm. The total correlation coefficients R were 0.9999, 0.9997, and 0.9995 respectively and the standard errors S were 1.036, 1.469, and 1.510 respectively. The relative mean deviation between the predicted value and the experimental value of Sθ, AfHe and △fGθ- were 0.11%, 0.34% and 0.24% respectively, which indicated that the QSPR models had good stability and superior predictive ability. The results showed that there were good nonlinear correlations between the thermodynamic properties of PBDPAs and the four structural pa- rameters. Thus, it was concluded that the ANN models established by the new substituent position index were fully applicable to predict properties of PBDPAs.展开更多
文摘Based on the location of bromine substituents and conjugation matrix, a new substituent po- sition index ~X not only was defined, but also molecular shape indexes Km and electronega- tivity distance vectors Mm of diphenylamine and 209 kinds of polybrominated diphenylamine （PBDPA） molecules were calculated. Then the quantitative structure-property relationships （QSPR） among the thermodynamic properties of 210 organic pollutants and 0X, K3, M29, M36 were founded by Leaps-and-Bounds regression. Using the four structural parameters as input neurons of the artificial neural network, three satisfactory QSPR models with network structures of 4：21：1, 4：24：1, and 4：24：1 respectively, were achieved by the back-propagation algorithm. The total correlation coefficients R were 0.9999, 0.9997, and 0.9995 respectively and the standard errors S were 1.036, 1.469, and 1.510 respectively. The relative mean deviation between the predicted value and the experimental value of Sθ, AfHe and △fGθ- were 0.11%, 0.34% and 0.24% respectively, which indicated that the QSPR models had good stability and superior predictive ability. The results showed that there were good nonlinear correlations between the thermodynamic properties of PBDPAs and the four structural pa- rameters. Thus, it was concluded that the ANN models established by the new substituent position index were fully applicable to predict properties of PBDPAs.
