灵活性(工程)
弹性体
材料科学
聚合
纳米技术
计算机科学
自由基聚合
聚合物
生化工程
工程类
复合材料
统计
数学
作者
Yiyi Xu,Yinliang Huang,Jinyu Wang,Shuai Huang,Hong Yang,Quan Li
标识
DOI:10.1002/anie.202423584
摘要
In nature, organisms adapt to environmental changes through training to learn new abilities, offering valuable insights for developing intelligent materials. However, replicating this adaptive learning in synthetic materials presents a significant challenge. This study introduces a feasible approach to train liquid crystal elastomers (LCEs) by integrating a mechanophore tetraarylsuccinonitrile (TASN) into their main chain, addressing the challenge of enabling synthetic materials to exchange substances with their environment. Inspired by biological training, the LCEs can self‐strengthen and acquire new functionalities through mechanical stress‐induced radical polymerization. The research not only enhances the mechanical performance of LCEs, but also endows them with the ability to learn properties such as flexibility, light responsiveness, and fluorescence. These advancements are crucial for overcoming the limitations of current materials, paving the way for the creation of advanced intelligent soft materials with autonomous self‐improvement, akin to the adaptive skills of living organisms.
科研通智能强力驱动
Strongly Powered by AbleSci AI