Cryostructuring of Polymeric Systems †: Application of Deep Neural Networks for the Classification of Structural Features Peculiar to Macroporous Poly(vinyl alcohol) Cryogels Prepared without and with the Additives of Chaotropes or Kosmotropes

Abstract

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.