2025-08-07 北海道大学
タンパク質模倣高分子設計による超接着性ハイドロゲルの実用例。開発されたハイドロゲルのうちの一つを用いて、ゴム製アヒルを海岸の岩に接着させた実演の様子。このゲルは海の潮の満ち引きや波の衝撃にも耐え、過酷な海洋環境下での強力な接着性能を実証。
＜関連情報＞
- https://www.hokudai.ac.jp/news/2025/08/post-2013.html
- https://www.hokudai.ac.jp/news/pdf/250807_pr.pdf
- https://www.nature.com/articles/s41586-025-09269-4
データ駆動型デノボ設計による超接着性ハイドロゲル Data-driven de novo design of super-adhesive hydrogels
Hongguang Liao,Sheng Hu,Hu Yang,Lei Wang,Shinya Tanaka,Ichigaku Takigawa,Wei Li,Hailong Fan & Jian Ping Gong
Nature Published:06 August 2025
DOI:https://doi.org/10.1038/s41586-025-09269-4
Abstract
Data-driven methodologies have transformed the discovery and prediction of hard materials with well-defined atomic structures by leveraging standardized datasets, enabling accurate property predictions and facilitating efficient exploration of design spaces1,2,3. However, their application to soft materials remains challenging because of complex, multiscale structure–property relationships4,5,6. Here we present a data-driven approach that integrates data mining, experimentation and machine learning to design high-performance adhesive hydrogels from scratch, tailored for demanding underwater environments. By leveraging protein databases, we developed a descriptor strategy to statistically replicate protein sequence patterns in polymer strands by ideal random copolymerization, enabling targeted hydrogel design and dataset construction. Using machine learning, we optimized hydrogel formulations from an initial dataset of 180 bioinspired hydrogels, achieving remarkable improvements in adhesive strength, with a maximum value exceeding 1 MPa. These super-adhesive hydrogels hold immense potential across diverse applications, from biomedical engineering to deep-sea exploration, marking a notable advancement in data-driven innovation for soft materials.
