仿生天然纤维的多重响应高性能形状记忆聚氨酯复合材料的基础研究

Materials from nature, having evolved over millions of years, often exhibits extraordinary properties due to their unique structures. These are excellent sources to inspire us for developing advanced artificial matters. Spider silk may be the toughest and lightest fiber in the world so far with biocompatibility and biodegradability. Such high performance of spider silk is commonly understood mainly due to nano-scale alanine-rich Beta-sheet crystals adequately distributed in its morphological structure. Wool fiber has a composite structure, is the most favored material for apparel because of its elasticity and sensational nature such as hand feeling. More amazingly, both of these fibers have been discovered to have smart behavior, particularly shape memory effects (SMEs). In previous studies, Beta-sheet concept was applied to bio-mimick the spider silk structural network for designing thermal-responsive shape memory polyurethane (SMPU) using alanine peptide as netpoints and found that such netpoints are quite difficult to obtain due to alanine’s strong crystalline nature and insolubility for synthesis. More recently, in wool or general animal hairs, it is discovered that hydrogen bonding and disulfide bonds are the switches responsible for its remarkable multi-responsiveness to water, heat, Redox and UV stimuli in shape programming and recovery. In addition, comparing to physically blended nanocomposites, it has also been reported that chemically-incorporated nanoparticles for polymer composites can lead to much higher performance in multiple aspects simultaneously, especially shape memory and mechanical properties. Inspired from these two types of natural smart fibers, in this project we will design and fabricate high performance SMPU by adapting a chemically-crosslinked-nanoparticle composite approach with Beta-sheet nanocrystals as net-points, disulfide bonding and hydrogen bonds as switches in its structure network. This can deftly and effectively utilize the nature of insolvable Beta-sheet crystals for high performance smart polymers. Relationships between structures, properties and shape memory behavior will be investigated systematically. A general model will be established to reveal the mechanisms of such smart materials. This novel structure will bring an extraordinary material having multi-responsive SMEs with high performance including excellent SME, high modulus and large toughness, large extensibility, biodegradability and biocompatibility, which will have applications in many fields including apparel and biomedical devices. The work proposed here will provide fresh thoughts for designing smart materials and expand the frontier of bionics to smart materials. In addition, this work can enhance the theoretical system of shape memory polymers, which can establish the foundation of further development of advanced materials.

蜘蛛丝是生物界迄今为止最强韧的纤维，其高性能主要来自于丙胺酸等形成的Beta折叠纳米晶区及其分布。羊毛因其良好的弹性和感性而成为最好的服装材料。而它们都被发现具有智能特点特别是形状记忆性能。与目前用丙胺酸多肽作为嵌段聚合物的组成部分不同，本项目将直接把受蜘蛛丝启发制作多肽过程中形成的Beta折叠晶粒用化学制作复合材料方法引为网络节点，引入羊毛里具有可逆性的二硫键和氢键为开关，得到仿生的有水、热、光和氧化还原多重响应形状记忆高分子材料，并深入研究材料组成、结构和形状记忆性能的关系和作用规律以及构建仿生材料的机理模型。这种结构巧妙而高效地利用多肽超强结晶和超难溶性并避免了其在合成中的困难。除了多重智能，预期达到优良形状记忆、超高韧性以及轻质生物相容可降解的目标。本项目为智能材料提供新的设计思路和拓展交叉领域发展的方向，同时也为形状记忆高分子材料向高性能全方位智能的发展和多领域应用奠定理论基础。

天然蛋白质材料的刺激响应行为是仿生学研究的一个热点问题。本项目首先研究了天然蛋白质材料在不同的环境因子刺激下都会产生形状记忆行为的机理，然后制备了多种仿生材料。经过四年的研究与探索，我们取得得主要亮点有：1）深入认识与总结了蜘蛛丝、羊毛等天然材料的形状记忆效果及其产生的机制，并用于新型仿生SMP材料的设计；2）通过对天然材料结构(蜘蛛丝的二级结构，肌肉中的二硫键等)的模仿，设计制备了仿蜘蛛丝、肌肉等新型材料，如可量产的超韧人工蛛丝纤维、人工肌肉等；3）确定了仿生材料组成、结构和力学性能以及形状记忆的关系，并建立了理论模型。这些新的发现将有助于为智能材料提供新的设计思路和拓展交叉领域发展的方向，同时也为形状记忆高分子材料向高性能全方位智能的发展和多领域应用奠定理论基础。