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 Kosmotropesстатья
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Дата последнего поиска статьи во внешних источниках: 16 декабря 2020 г.
Аннотация:Macroporous poly(vinyl alcohol) cryogels (PVACGs) are physical gels formed viacryogenic processing of polymer solutions. The properties of PVACGs depend on many factors: thecharacteristics and concentration of PVA, the absence or presence of foreign solutes, and thefreezing-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 withdeveloping a scientifically-grounded classification of the morphological features inherent in variousPVACGs. In this study PVA cryogels have been prepared at different temperatures when the initialpolymer solutions contained chaotropic or kosmotropic additives. After the completion of gelation,the rigidity and heat endurance of the resultant PVACGs were evaluated, and their macroporousstructure was investigated using optical microscopy. The images obtained were treatedmathematically, and deep neural networks were used for the classification of these images. Trainingand test sets were used for their classification. The results of this classification for the specific deepneural network architecture are presented, and the morphometric parameters of the macroporousstructure are discussed. It was found that deep neural networks allow us to reliably classify the typeof additive or its absence when using a combined dataset.