仿生水响应形状记忆高分子材料的结构设计及其性能研究

Study on biomimetic materials inspired by natural spider silk has been a hot topic. However, the existing biomimetic design and research mainly focuses on its excellent mechanical properties but ignores the unique water sensitive characteristics of spider silks. We discovered that the supercontraction of spider silks is a typical shape-memory function induced by water. Based on the current findings in supercontraction mechanism, plus our experiences and understanding in shape-memory polymers, we proposed a structural model to disclose the mechanism of water sensitive shape-memory behavior of spider silks. In the model, hydrogen bonding within the amorphous regions of silks can be interpreted as a switch that is responsible for strain fixation, while β-sheet crystals act as netpoints to stabilize the permanent shape. Moreover, β-sheet crystals have been proven effective as netpoints while semi-crystalline polycaprolactone as the switch, to successfully form biomimetic thermo-sensitive shape-memory polymers. In this project, we propose to further mimic the structure and water sensitive characteristics of spider silks, and develop a series of biomimetic water sensitive shape-memory polymers with β-sheet crystal structure as netpoints and various reversible hydrogen bonds as switches. The relationship among components, structure, water sensitive characteristics and shape-memory effects will be systematically studied, which will render the controllability of water triggered shape-memory function. Furthermore, combined with molecular dynamics and quantum chemistry, theoretical models for water sensitive shape-memory functions will be established. The work being proposed in this project will provide a novel approach for designing intelligent materials, promote the development of interdisplinary subjects of bionics and intelligent materials. Besides, it will lead to the establishment of water sensitive shape-memory polymer system, which can lay solid foundation for further development of smart materials.

天然蜘蛛丝仿生材料的研究一直是仿生学研究热点。然而，目前其设计理念集中模仿蜘蛛丝优异机械性能，往往忽略其独特水响应特性。研究表明，蜘蛛丝超收缩是一种典型的水响应形状记忆功能。基于现有超收缩机理研究，结合对形状记忆高分子的理解，我们提出蜘蛛丝无定形区氢键为开关、β-折叠结构为节点的形状记忆结构模型，并实验证实了β-折叠结构作为节点有效性。本项目，拟进一步模仿蜘蛛丝结构和水响应智能特性，设计合成以β-折叠为节点，不同氢键为开关的仿生水响应形状记忆高分子材料，并深入研究材料组成、结构对水响应性及形状记忆功能的作用规律，实现水响应形状记忆功能的可控性，同时结合分子动力学及量子化学方法，构建出仿生水响应形状记忆机理的理论模型。本项目研究不仅为智能材料提供新的设计思路，拓展仿生学和智能材料交叉领域发展的方向，还有助于完善形状记忆理论体系的建立，为形状记忆高分子材料进一步发展奠定扎实理论基础。

天然仿生材料的研究一直是仿生学研究热点。本项目首先研究了天然动物毛纤维的水响应形状记忆，进而仿生制备出水响应形状记忆高分子材料。经过四年的系统探索，我们取得的主要亮点包括：1）系统研究了天然动物毛纤维的水响应形状记忆，明确了其结构与水响应性和形状记忆功能间相互关系和规律；2）仿生蜘蛛丝制备出SMP材料，明确了材料组成、结构与形状记忆之间的关系；3）制备出了快速水响应SMP杂化材料，通过DMA和应力-应变循环测试，阐明了水响应的形状记忆机理，提出了理论模型。相关研究成果包括：发表SCI收录论文10篇；申请国家发明专利2项；参加国际学术会议6人次；培养研究生3名、中青年研究骨干3名。