Urea (URE) and guanidine hydrochloride (GHC) possessing strong chaotropic properties in aqueous media were added to DMSO solutions of poly(vinyl alcohol) (PVA) to be gelled via freeze–thaw processing. Unexpectedly, it turned out that in the case of the PVA cryotropic gel formation in DMSO medium, the URE and GHC additives caused the opposite effects to those observed in water, i.e., the formation of the PVA cryogels (PVACGs) was strengthened rather than inhibited. Our studies of this phenomenon showed that such “kosmotropic-like” effects were more pronounced for the PVACGs that were formed in DMSO in the presence of URE additives, with the effects being concentration-dependent. The additives also caused significant changes in the macroporous morphology of the cryogels; the commonly observed trend was a decrease in the structural regularity of the additive-containing samples compared to the additive-free gel sample. The viscosity measurements revealed consistent changes in the intrinsic viscosity, Huggins constant, and the excess activation heat of the viscosity caused by the additives. The results obtained evidently point to the urea-induced decrease in the solvation ability of DMSO with respect to PVA. As a result, this effect can be the key factor that is responsible for strengthening the structure formation upon the freeze–thaw gelation of this polymer in DMSO additionally containing additives such as urea, which is capable of competing with PVA for the solvent.
Macroporous poly(vinyl alcohol) cryogels (PVACGs) are physical gels formed via cryogenic processing of polymer solutions. The properties of PVACGs depend on many factors: the characteristics and concentration of PVA, the absence or presence of foreign solutes, and the freezing-thawing conditions. These factors also affect the macroporous morphology of PVACGs, their total porosity, pore size and size distribution, etc. In this respect, there is the problem with developing a scientifically-grounded classification of the morphological features inherent in various PVACGs. In this study PVA cryogels have been prepared at different temperatures when the initial polymer solutions contained chaotropic or kosmotropic additives. After the completion of gelation, the rigidity and heat endurance of the resultant PVACGs were evaluated, and their macroporous structure was investigated using optical microscopy. The images obtained were treated mathematically, and deep neural networks were used for the classification of these images. Training and test sets were used for their classification. The results of this classification for the specific deep neural network architecture are presented, and the morphometric parameters of the macroporous structure are discussed. It was found that deep neural networks allow us to reliably classify the type of additive or its absence when using a combined dataset.
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