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

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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 http://hdl.handle.net/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


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