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In this study, the hydrogels have been synthesized by the crosslinking reactions maleic anhydridestyrene-methyl methacrylate terpolymer with N, N'-methylene-bis-acrylamide and glutaraldehyde in various crosslinker ratios. Crosslinking reactions were carried out in the presence of tetrahydrofuran as solvent at 25-50ºC for different time period, and the resulting material properties compared. The best conditions for effective crosslinking, i.e., crosslinking temperature, time and crosslinker ratios were determined for each crosslinker type. The swelling behaviour of the hydrogels was examined in Tris-buffer solutions at various pH at 37ºC. Swelling depending on pH was observed in the hydrogels. The pH-dependent swelling of hydrogels was strongly influenced by the functional group of crosslinker. The molecular structure of the hydrogels was studied by Fourier Transform Infrared Spectroscopy and their pore structure was investigated by using Scanning Electron Microscope. terpolimerinin N, N'metilenbisakrilamid ve glutaraldehitin farklı oranları ile çapraz bağlanmasıyla sentezlenmiştir. Çapraz bağlanma reaksiyonları çözücü olarak tetrahidrofuran varlığında farklı zaman periyodunda 25-50ºC aralığında gerçekleştirilmiş ve malzeme özellikleri kıyaslanmıştır. Her bir çapraz bağlayıcı tipi için çapraz bağlanma sıcaklığı, zamanı ve çapraz bağlayıcı oranı gibi etkin çapraz-bağlanma koşulları belirlenmiştir. Hidrojellerin şişme davranışı Tris tampon ortamlarda farklı pH aralığında 37ºC'de incelenmiştir. Hidrojellerin pH-duyarlı olduğu ve pH'ya bağlı şişmenin çapraz bağlayıcının fonksiyonel grubundan etkilendiği görülmüştür. Hidrojellerin moleküler yapısı Fourier Transform Infrared Spektroskopisi ile, gözenekyapısı da taramalı electron mikroskobu ile incelenmiştir.AnahtarKelimeler:Maleikanhidrit stiren metilmetakrilat terpolimer, çaprazbağlanma, N, N'-metilen-bisakrilamid, glutaraldehit, pH-duyarlı hidrojel.

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This research aims to introduce a novel radial basis functional link net (RBFLN)-based QSPR (quantitative structure-property relationship) model to predict the solubility parameters of the polymers with the structure – (C1H-2-C2R3R4) – and provides its comparison with the multi-layer feed forward network (MLFFN)-based QSPR model, as well as previous genetic programming (GP) and multiple linear regression (MLR)-based QSPR models in the literature. During the implementation of the RBFLN and MLFFN-based QSPR models, the networks which are associated with the minimum weighted average AIC (Akaike’s information criterion) and BIC (Bayesian information criterion) scores are trained by using a hybrid scheme combining the cuckoo search and Levenberg-Marquardt algorithm. Our results show that the RBFLN-based QSPR model outperforms the other ones in terms of the external validation metrics. The study also reveals that it may have a promising potential to study the relationship between various measurement/experimental data or processing elements in a hybrid way of artificial intelligence modelling.