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具有主动变形功能的形状记忆聚氨酯微胶囊

Shape Memory Polyurethane Microcapsules with Active Deformation.

作者信息

Zhang Fenghua, Zhao Tianheng, Ruiz-Molina Daniel, Liu Yanju, Roscini Claudio, Leng Jinsong, Smoukov Stoyan K

机构信息

Centre for Composite Materials and Structures, Harbin Institute of Technology (HIT), No. 2 YiKuang Street, P.O. Box 3011, Harbin 150080, People's Republic of China.

Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, U.K.

出版信息

ACS Appl Mater Interfaces. 2020 Oct 14;12(41):47059-47064. doi: 10.1021/acsami.0c14882. Epub 2020 Sep 29.

Abstract

From smart self-tightening sutures and expandable stents to morphing airplane wings, shape memory structures are increasingly present in our daily life. The lack of methods for synthesizing intricate structures from them on the micron and submicron level, however, is stopping the field from developing. In particular, the methods for the synthesis of shape memory polymers (SMPs) and structures at this scale and the effect of new geometries remain unexplored. Here, we describe the synthesis of shape memory polyurethane (PU) capsules accomplished by interfacial polymerization of emulsified droplets. The emulsified droplets contain the monomers for the hard segments, while the continuous aqueous phase contains the soft segments. A trifunctional chemical cross-linker for shape memory PU synthesis was utilized to eliminate creep and improve the recovery ratios of the final capsules. We observe an anomalous dependence of the recovery ratio with the amount of programmed strain compared to previous SMPs. We develop quantitative characterization methods and theory to show that when dealing with thin-shell objects, alternative parameters to quantify recovery ratios are needed. We show that while achieving 94-99% area recovery ratios, the linear capsule recovery ratios can be as low as 70%. This quantification method allows us to convert from observed linear aspect ratios in capsules to find out unrecovered area strain and stress. The hollow structure of the capsules grants high internal volume for some applications (e.g., drug delivery), which benefit from much higher loading of active ingredients than polymeric particles. The methods we developed for capsule synthesis and programming could be easily scaled up for larger volume applications.

摘要

从智能自收紧缝线、可扩展支架到可变形飞机机翼,形状记忆结构在我们的日常生活中越来越常见。然而,缺乏在微米和亚微米尺度上由它们合成复杂结构的方法,阻碍了该领域的发展。特别是,在这个尺度上合成形状记忆聚合物(SMP)和结构的方法以及新几何形状的影响仍未得到探索。在此,我们描述了通过乳化液滴的界面聚合来合成形状记忆聚氨酯(PU)胶囊。乳化液滴包含硬段的单体,而连续水相包含软段。一种用于形状记忆PU合成的三官能团化学交联剂被用来消除蠕变并提高最终胶囊的回复率。与之前的SMP相比,我们观察到回复率与编程应变的量之间存在异常的依赖关系。我们开发了定量表征方法和理论,以表明在处理薄壳物体时,需要用替代参数来量化回复率。我们表明,虽然面积回复率达到94 - 99%,但胶囊的线性回复率可能低至70%。这种量化方法使我们能够从胶囊中观察到的线性长宽比转换,以找出未恢复的面积应变和应力。胶囊的中空结构为一些应用(如药物递送)提供了高内部体积,这得益于比聚合物颗粒更高的活性成分负载量。我们开发的胶囊合成和编程方法可以很容易地扩大规模以用于更大体积的应用。

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