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Prediction of flash point of organosilicon compounds using quantitative structure property relationship approach / Chan-Cheng Chen in Industrial & engineering chemistry research, Vol. 49 N° 24 (Décembre 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 24 (Décembre 2010) . - pp. 12702–12708 Titre : Prediction of flash point of organosilicon compounds using quantitative structure property relationship approach Type de document : texte imprimé Auteurs : Chan-Cheng Chen, Auteur ; Horng-Jang Liaw, Auteur ; Yi-Jen Tsai, Auteur Année de publication : 2011 Article en page(s) : pp. 12702–12708 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Organosilicon  compounds Résumé : Flash point (FP) is the primary property to classify flammable liquids for the purpose of assessing their fire and explosion hazards. Because of the advancement of technology in discovering or synthesizing new compounds, there is often a significant gap between the demand for such data and their availability. In this regard, reliable methods to estimate the FP of a compound are indispensible. In this work, a quantitative structure property relationship study is presented for predicting the FP of organosilicon compounds. To build up and validate the proposed models, a data set of 230 organosilicon compounds are collected and divided into a training set of 184 compounds and a testing set of 46 compounds. The stepwise regression method is used to select the required molecular descriptors for predicting the FP of organosilicon compounds from 1538 molecular descriptors. Depending on the p-value for accepting a descriptor to enter the model, models with different number of descriptors are obtained. A 13-descriptor model and a 6-descriptor model are obtained with the p-value of 5 × 10−4 and 1 × 10−5, respectively. It is found that the 6-descriptor model could fit the training data with R2 = 0.8981 and predict the test data with Q2 = 0.8533 and the 13-descriptor model could fit the training data with R2 = 0.9293 and predict the test data with Q2 = 0.9245. The average predictive errors are less than 5% for both proposed models and they are useful for many practical applications, because the proposed models used only calculated descriptors from the molecular structure. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie101381b [article] Prediction of flash point of organosilicon compounds using quantitative structure property relationship approach [texte imprimé] / Chan-Cheng Chen, Auteur ; Horng-Jang Liaw, Auteur ; Yi-Jen Tsai, Auteur . - 2011 . - pp. 12702–12708. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 24 (Décembre 2010) . - pp. 12702–12708 Mots-clés : Organosilicon  compounds Résumé : Flash point (FP) is the primary property to classify flammable liquids for the purpose of assessing their fire and explosion hazards. Because of the advancement of technology in discovering or synthesizing new compounds, there is often a significant gap between the demand for such data and their availability. In this regard, reliable methods to estimate the FP of a compound are indispensible. In this work, a quantitative structure property relationship study is presented for predicting the FP of organosilicon compounds. To build up and validate the proposed models, a data set of 230 organosilicon compounds are collected and divided into a training set of 184 compounds and a testing set of 46 compounds. The stepwise regression method is used to select the required molecular descriptors for predicting the FP of organosilicon compounds from 1538 molecular descriptors. Depending on the p-value for accepting a descriptor to enter the model, models with different number of descriptors are obtained. A 13-descriptor model and a 6-descriptor model are obtained with the p-value of 5 × 10−4 and 1 × 10−5, respectively. It is found that the 6-descriptor model could fit the training data with R2 = 0.8981 and predict the test data with Q2 = 0.8533 and the 13-descriptor model could fit the training data with R2 = 0.9293 and predict the test data with Q2 = 0.9245. The average predictive errors are less than 5% for both proposed models and they are useful for many practical applications, because the proposed models used only calculated descriptors from the molecular structure. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie101381b