Prediction of the deswelling behaviors of pH- and temperature-responsive poly(NIPAAm-co-AAc) IPN hydrogel by artificial intelligence techniques

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dc.authoridKünkül, Asım/0000-0002-6080-2588
dc.authoridYuceer, Mehmet/0000-0002-2648-3931
dc.authoridBoztepe, Cihangir/0000-0001-5019-2010
dc.authorwosidBoztepe, Cihangir/H-5877-2018
dc.authorwosidKünkül, Asım/ABG-8608-2020
dc.authorwosidYuceer, Mehmet/E-5110-2012
dc.contributor.authorBoztepe, Cihangir
dc.contributor.authorYuceer, Mehmet
dc.contributor.authorKunkul, Asim
dc.contributor.authorSolener, Musa
dc.contributor.authorKabasakal, Osman S.
dc.date.accessioned2024-08-04T20:46:46Z
dc.date.available2024-08-04T20:46:46Z
dc.date.issued2020
dc.departmentİnönü Üniversitesien_US
dc.description.abstractOne of the most important fields of interest in respect of stimuli-responsive hydrogels is modeling and simulation of their deswelling behavior. The problem mentioned above plays an important role regarding diffusion of fluid from hydrogel to media, what is very useful in biomedical applications, such as controlled drug delivery systems, biomaterials or biosensors. In this study, the pH- and temperature-responsive poly(N-isopropylacrylamide-co-acrylic acid) interpenetrating polymer network (poly(NIPAAm-co-AAc) IPN) hydrogel was synthesized by free radical solution polymerization method. In order to improve the deswelling rate of the conventional poly(NIPAAm-co-AAc) hydrogels, their IPN structure was synthesized by using poly(NIPAAm-co-AAc) microgels. The chemical structure and surface morphology of poly(NIPAAm-co-AAc) IPN hydrogels were characterized by FT-IR and SEM analysis techniques. The synthesized poly(NIPAAm-co-AAc) IPN hydrogel has high swelling capacity (112 g water/g dry polymer at 20 degrees C and pH 7) and exhibited fast and multivariable deswelling behaviors dependent on pH and temperature. The pH- and temperature-dependent mechanical property of IPN hydrogel was investigated. It was found that the compressive strength of the IPN hydrogels was changed inversely proportional to the swelling capacity. To develop the model for deswelling behaviors of IPN hydrogel, artificial neural network (ANN) model and least squares support vector machine model techniques were used. The predictions from the ANN model showed very good correlation with observed data. The results indicated that the ANN model could accurately predict complex deswelling behavior of pH- and temperature-responsive IPN hydrogels.en_US
dc.identifier.doi10.1007/s11164-019-03957-3
dc.identifier.endpage428en_US
dc.identifier.issn0922-6168
dc.identifier.issn1568-5675
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85072017141en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage409en_US
dc.identifier.urihttps://doi.org/10.1007/s11164-019-03957-3
dc.identifier.urihttps://hdl.handle.net/11616/98950
dc.identifier.volume46en_US
dc.identifier.wosWOS:000513233200025en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofResearch on Chemical Intermediatesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectStimuli-responsive hydrogelsen_US
dc.subjectDeswelling kineticen_US
dc.subjectANNen_US
dc.subjectModelingen_US
dc.subjectBiomedical hydrogelsen_US
dc.titlePrediction of the deswelling behaviors of pH- and temperature-responsive poly(NIPAAm-co-AAc) IPN hydrogel by artificial intelligence techniquesen_US
dc.typeArticleen_US

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