Chemical Structure Evaluations of Amine Hardeners to Ensure and Predict the Performance of Wet Adhesion of Epoxies

  • Yasuyuki Nakamura
    Data-Driven Polymer Design Group, Research Center for Macromolecules and Biomaterials, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047 , Japan
  • Yusuke Hibi
    Data-Driven Polymer Design Group, Research Center for Macromolecules and Biomaterials, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047 , Japan
  • Kimiyoshi Naito
    Polymer Matrix Composites Group, Materials Manufacturing Field, Research Center for Structural Materials, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047 , Japan
  • Norie Yamamoto
    Data-Driven Polymer Design Group, Research Center for Macromolecules and Biomaterials, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047 , Japan
  • Misato Hanamura
    Data-Driven Polymer Design Group, Research Center for Macromolecules and Biomaterials, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047 , Japan

説明

<jats:title>Abstract</jats:title> <jats:p>The relationship between the chemical structure and performance of a water-sorbed epoxy adhesive (wet adhesion) provides fundamental data for epoxy adhesives for application in wet and underwater environments. However, data on the effect of the chemical structure on wet adhesion remains insufficient. This study systematically examined the wet adhesion strengths of epoxies comprising bisphenol A diglycidyl ether and various amines. The use of numerical parameters quantifying the features of the chemical structure and physicochemical properties via theoretical calculations to analyze the correlation between wet adhesion and the chemical structure of amine yielded clear linear relationships. This enabled the extraction of the amine molecular structural features that were superior in wet adhesion, in addition to quantification of the certainties of the features contributing to the physical properties. Furthermore, a prediction model for wet adhesive strength was prepared using machine-learning least absolute shrinkage and selection operator regression analysis. The model exhibited a reasonable accuracy, even using only 14 experimental values, and its effectiveness was verified experimentally. This process facilitates the rational design and selection of amine hardeners for preparing epoxies with excellent performance in wet conditions and underwater environments.</jats:p>

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