Prediction of intrinsic viscosity in polymer-solvent combinations using a QSPR model

Afantitis, A; Melagraki, G; Sarimveis, H; Koutentis, PA; Markopoulos, J; Igglessi-Markopoulou, O

dc.contributor.author	Afantitis, A	en
dc.contributor.author	Melagraki, G	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Koutentis, PA	en
dc.contributor.author	Markopoulos, J	en
dc.contributor.author	Igglessi-Markopoulou, O	en
dc.date.accessioned	2014-03-01T01:24:52Z
dc.date.available	2014-03-01T01:24:52Z
dc.date.issued	2006	en
dc.identifier.issn	0032-3861	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17484
dc.subject	Intrinsic viscosity	en
dc.subject	Molecular descriptors	en
dc.subject	QSPR	en
dc.subject.classification	Polymer Science	en
dc.subject.other	Database systems	en
dc.subject.other	Regression analysis	en
dc.subject.other	Solvents	en
dc.subject.other	Viscosity	en
dc.subject.other	Intrinsic viscosity	en
dc.subject.other	MLR model	en
dc.subject.other	Molecular descriptors	en
dc.subject.other	QSPR	en
dc.subject.other	Polymers	en
dc.subject.other	molecular structure	en
dc.subject.other	polymer	en
dc.subject.other	polymer solution	en
dc.subject.other	solvent	en
dc.subject.other	viscosity	en
dc.title	Prediction of intrinsic viscosity in polymer-solvent combinations using a QSPR model	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.polymer.2006.02.060	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.polymer.2006.02.060	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	In this work, a linear quantitative structure-property relationship (QSPR) model is presented for the prediction of intrinsic viscosity in polymer solutions. The model was produced by using the multiple linear regression (MLR) technique on a database that consists of 65 polymer-solvent combinations involving 10 different polymer. Among the 30 different physicochemical, topological and structural descriptors that were considered as inputs to the model, only eight variables (four variables for the polymer and four descriptors for the solvent) were selected using the elimination selection stepwise regression method (ES-SWR). The physical meaning of each descriptor is discussed in details. The accuracy of the proposed MLR model is illustrated using various evaluation techniques: leave-one-out cross validation procedure, validation through an external test set and Y-randomization. Furthermore, the calculation of the domain of applicability defines the area of reliable predictions. (c) 2006 Elsevier Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Polymer	en
dc.identifier.doi	10.1016/j.polymer.2006.02.060	en
dc.identifier.isi	ISI:000237773600036	en
dc.identifier.volume	47	en
dc.identifier.issue	9	en
dc.identifier.spage	3240	en
dc.identifier.epage	3248	en