{"title":"Estimation of Rate Constants for Radical Degradation of Aromatic Pollutants by Density Functional Theory","authors":"Jianhui Jiang, X. Yue, Q. Chen","doi":"10.1109/CDCIEM.2011.273","DOIUrl":null,"url":null,"abstract":"in present study, rate constants for radical degradation of 52 aromatic pollutants were predicted with QSAR model. The parameters of model were obtained from optimized calculation of aromatic pollutants that were carried out at B3LYP/6-311G** level with density functional theory. Then, different molecular descriptors were taken as theoretical descriptors to establish the QSAR models by partial least square regression analysis. The novel QSAR model contains four variables, of which square regression coefficient is 0.82, standard deviation is 0.10. The QSAR model obtained reveal the reliability and good predictivity for the prediction of the rate constants of aromatic pollutants.","PeriodicalId":6328,"journal":{"name":"2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring","volume":"14 1","pages":"1492-1495"},"PeriodicalIF":0.0000,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDCIEM.2011.273","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
in present study, rate constants for radical degradation of 52 aromatic pollutants were predicted with QSAR model. The parameters of model were obtained from optimized calculation of aromatic pollutants that were carried out at B3LYP/6-311G** level with density functional theory. Then, different molecular descriptors were taken as theoretical descriptors to establish the QSAR models by partial least square regression analysis. The novel QSAR model contains four variables, of which square regression coefficient is 0.82, standard deviation is 0.10. The QSAR model obtained reveal the reliability and good predictivity for the prediction of the rate constants of aromatic pollutants.