Prediction of gelation by artificial neural network using Hansen solubility parameters

2021 Fiscal Year Final Research Report

• Project/Area Number: 20K22471
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Multi-year Fund
• Review Section: 0401:Materials engineering, chemical engineering, and related fields
• Research Institution: Tokyo University of Science
• Principal Investigator: Murakami Yuya 東京理科大学, 工学部工業化学科, 助教 (80880757)
• Project Period (FY): 2020-09-11 – 2022-03-31
• Keywords: ゲル化剤 / Hansen溶解度パラメータ / 機械学習

Outline of Final Research Achievements

In this research, the fabrication of supramolecular gel was conducted with dozens of organic solvents in order to establish the selection criteria for gelator-solvent pairs. When 12-hyroxystearic acid is used as a gelator, a hydrogen bond term in Hansen solubility parameters of solvents could be used for the prediction of gelation. Additionally, the transparency of the gels has a negative linear correlation with the distance between the gelator and solvents in Hansen space. Since the transparency of the gels reflects molecular structures in the gels, it can be a good indicator to quantitatively explain the mechanism of gelation.

Free Research Field

化学工学

Academic Significance and Societal Importance of the Research Achievements

超分子ゲルは，弱い分子間の結合によって形成されるゲルであり，光・熱・pHなどに応答して構造崩壊が起きるため，薬物輸送システムやセンサーなどに活用される．本研究では，分子構造を反映可能なHansen溶解度パラメータを活用することで，このようなゲルの形成可否および物性を予測する手法の構築を目指した．結果，ゲルの透過率と用いる溶媒のHansen溶解度の間に強い線形相関関係がある事が明らかとなり，ゲル物性の制御・予測にはこれらの指標が重要であることを明らかにした．本研究の成果により，超分子ゲルの物性制御が可能となり，本材料のより広範な分野での活用が期待される．