高分子基电响应形状记忆复合物（ESMC）作为一类智能材料，已被广泛用于传感、驱动、航空等高端领域，但现有研究大多停留在两重形状记忆的实现，严重制约了其用途的进一步开发。针对此，本申请项目选择多壁碳纳米管（MWCNT）填充后的热塑性聚氨酯（cTPU）为固定相，聚乳酸（PLA）/聚碳酸亚丙酯（PPC）共混物（LPB）为可逆相，利用微纳层叠共挤出技术构筑层形态、层数目、层厚比不同的cTPU/LPB交替多层复合材料，同步完成MWCNT的层状分布和三重形状记忆网络的构建，以期实现高效快速的电响应三重形状记忆性能。通过研究碳管添加量、层结构、电场强度等因素对体系电热效应和形状记忆性能的影响，探索并建立交替多层复合材料的电响应三重形状记忆机理及性能调控机制，揭示导电网络在形状记忆过程中的演变规律，为三重ESMC的高效构筑和智能调控提供新思路、新途径，对丰富和发展智能材料种类及应用具有重要意义和实用价值。

Polymeric electro-responsive shape memory composites (ESMC), as a type of intelligent materials, have been extensively applied in high-end fields such as sensor, actuator and aerospace structure. However, most of the current researches still stay at realization of dual-shape memory effect, which severely restricted the further development of application of ESMC. In view of this, this project intends to select the multi-walled carbon nanotubes (MWCNT)-filled thermoplastic polyurethane (cTPU) as fixing phase and polylactic acid (PLA)/polypropylene carbonate (PPC) blend (LPB) as switching phase, then fabricated cTPU/LPB alternating multilayer composites possessing diverse layer morphology, layer number and layer thickness ratio through micro/nano layer-multiplying coextrusion technology, which could simultaneously realize layered distribution of MWCNT and construction of triple-shape memory network, for developing efficient and rapid electro-responsive triple-shape memory effects. The electro-responsive triple-shape memory mechanism of alternating multilayer composites was systematically explored and successfully established by researching the influences of MWCNT addition amount, layer structure and electric field strength on electrothermal effect and shape memory performances. Meanwhile, the evolution rule of conductive network in the shape memory cycle was revealed. This project was believed to provide new ideas and original routes for the efficient preparation and intelligent regulation of electro-responsive triple-shape memory composites, which has important theoretical significance and practical value for enriching and developing intelligent material types and applications.