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受含羞草启发的体温响应形状记忆聚合物网络:高能量密度与多重可回收性

Mimosa-Inspired Body Temperature-Responsive Shape Memory Polymer Networks: High Energy Densities and Multi-Recyclability.

作者信息

Kong Qingming, Tan Yu, Zhang Haiyang, Zhu Tengyang, Li Yitan, Xing Yongzheng, Wang Xu

机构信息

National Engineering Research Center for Colloidal Materials, School of Chemistry and Chemical Engineering, Shandong University, Jinan, Shandong, 250100, China.

出版信息

Adv Sci (Weinh). 2024 Oct;11(39):e2407596. doi: 10.1002/advs.202407596. Epub 2024 Aug 14.

Abstract

Inspired by the Mimosa plant, this study herein develops a unique dynamic shape memory polymer (SMP) network capable of transitioning from hard to pliable with heat, featuring reversible actuation, self-healing, recyclability, and degradability. This material is adept at simulating the functionalities of artificial muscles for a variety of tasks, with a remarkable specific energy density of 1.8 J g-≈46 times higher than that of human skeletal muscle. As an intelligent manipulator, it demonstrates remarkable proficiency in identifying and handling items at high temperatures. Its suitable rate of shape recovery around human body temperature indicates its promising utility as an implant material for addressing acute obstructions. The dynamic covalent bonding within the network structure not only provides excellent resistance to solvents but also bestows remarkable abilities for self-healing, reprocessing, and degradation. These attributes significantly boost its practicality and environmental sustainability. Anticipated to promote advancements in the sectors of biomedical devices, soft robotics, and smart actuators, this SMP network represents a forward leap in simulating artificial muscles, marking a stride toward the future of adaptive and sustainable technology.

摘要

受含羞草植物的启发,本研究开发了一种独特的动态形状记忆聚合物(SMP)网络,它能够随着温度变化从硬态转变为柔韧态,具有可逆驱动、自我修复、可回收和可降解的特性。这种材料擅长模拟人造肌肉的功能以执行各种任务,其具有1.8 J g⁻¹ 的显著比能量密度,约为人类骨骼肌的46倍。作为一种智能操纵器,它在高温下识别和处理物品方面表现出卓越的能力。其在人体温度附近合适的形状恢复速率表明它作为解决急性梗阻的植入材料具有广阔的应用前景。网络结构中的动态共价键不仅赋予了出色的耐溶剂性,还赋予了显著的自我修复、再加工和降解能力。这些特性显著提高了其实用性和环境可持续性。预计该SMP网络将推动生物医学设备、软机器人技术和智能驱动器等领域的发展,它代表了在模拟人造肌肉方面的一次重大飞跃,朝着自适应和可持续技术的未来迈出了坚实的一步。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ee1/11497007/1a494a091460/ADVS-11-2407596-g003.jpg

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