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

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Abstract

One 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 °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.

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Correspondence to Mehmet Yüceer.

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Boztepe, C., Yüceer, M., Künkül, A. et al. Prediction of the deswelling behaviors of pH- and temperature-responsive poly(NIPAAm-co-AAc) IPN hydrogel by artificial intelligence techniques. Res Chem Intermed 46, 409–428 (2020) doi:10.1007/s11164-019-03957-3

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Keywords

  • Stimuli-responsive hydrogels
  • Deswelling kinetic
  • ANN
  • Modeling
  • Biomedical hydrogels