Three dimensional image analysis for prediction of vinyl triethoxysilane concentration and porosity percent of methyl methacrylate/ethylene glycol dimethacrylate copolymers using scanning electron microscopy

Abstract

Neste trabalho, micrografias eletrônicas de varredura de esferas de copolímero baseadas em metacrilato de metila (MMA), etilenoglicoldimetacrilato (EGDM) e vinil-trietoxissilano (VTES) foram usadas para construir modelos de regressão multivariada de imagem. Microscopia eletrônica de varredura (SEM) e ensaios de intumescimento indicaram que aumentando a concentração de silicone (VTES) dos copolímetros ocorre um aumento da quantidade de poros e porcentagem de porosidade. Micrografias eletrônicas de varredura dos copolímeros sintetizados com diferentes concentrações de VTES foram usadas para construir dois modelos de calibração, que puderam relacionar a concentração de VTES e a porcentagem de porosidade dos copolímeros com suas imagens de SEM. Para construção dos modelos foram utilizados o método dos mínimos quadrados parciais multi-modo (N-PLS) e componentes principais desdobrados (u-PCR). O poder de previsão dos modelos construídos foi avaliado utilizando um conjunto teste. Os resultados indicaram que os modelos conseguiram prever de forma muito efetiva a concentração de VTES e a porcentagem de porosidade dos copolímeros a partir de suas imagens de SEM.In this work, scanning electron micrographs of copolymer beads based on methyl methacrylate (MMA), ethylene glycol dimethacrylate (EGDM) and vinyl triethoxysilane (VTES) were used to construct multivariate image regression models. Scanning electron microscopy (SEM) and swelling measurements indicated that increasing the silicone concentration (VTES) of the copolymers enhances the amount of pores and porosity percent. Scanning electron micrographs of the synthesized copolymers with different VTES concentrations were used to construct two soft models, which were able to relate the VTES concentration and the porosity percent of the copolymers with their SEM images. The constructed models were the three-way partial least squares analysis (N-PLS) and unfolded principal component regression (unfold-PCR). The predictive ability of the constructed models were evaluated by a test set. The results showed the models were able to effectively predict the VTES concentration and the porosity percent of the copolymers using their corresponding SEM images.

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