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Unusual Aspects of Supramolecular Networks: Plasticity to Elasticity, Ultrasoft Shape Memory, and Dynamic Mechanical Properties

机译:超分子网络的不寻常方面:弹性可塑性,超软形状记忆和动态力学性能

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摘要

Supramolecular bonds have been widely used for designing polymers because of their reversible nature. In contrast, utilization of their dynamic equilibrium nature to access materials of unusual mechanical properties has been poorly explored. Taking full advantage of this latter attribute requires the design of polymer networks with high contents of supramolecular bonds. In this work, polymer networks with high contents of self-complementary hydrogen bonds (ureidopyrimidinone) are synthesized using thiol-acrylate click addition. The excellent tunability of the network allows a range of intriguing mechanical properties to be achieved including the transition from plasticity to elasticity, ultrasoft shape memory polymer, strong strain rate dependence, and high mechanical damping. Materials with such versatile dynamic behaviors may open up a range of new applications.
机译:超分子键由于其可逆性而被广泛用于设计聚合物。相反,很少研究利用它们的动态平衡性质来获得具有异常机械性能的材料。充分利用后一属性需要设计具有高含量超分子键的聚合物网络。在这项工作中,使用巯基丙烯酸酯点击加成合成了具有高含量的自我互补氢键(脲基嘧啶酮)的聚合物网络。网络的出色可调性可实现一系列有趣的机械性能,包括从可塑性到弹性的过渡,超软形记忆聚合物,强应变率依赖性和高机械阻尼。具有这种通用动态行为的材料可能会开拓一系列新应用。

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  • 来源
    《Advanced Functional Materials》 |2016年第6期|931-937|共7页
  • 作者单位

    Zhejiang Univ, State Key Lab Chem Engn, Coll Chem & Biol Engn, Hangzhou 310027, Zhejiang, Peoples R China;

    Zhejiang Univ, State Key Lab Chem Engn, Coll Chem & Biol Engn, Hangzhou 310027, Zhejiang, Peoples R China;

    Zhejiang Univ, State Key Lab Chem Engn, Coll Chem & Biol Engn, Hangzhou 310027, Zhejiang, Peoples R China;

    Zhejiang Univ, State Key Lab Chem Engn, Coll Chem & Biol Engn, Hangzhou 310027, Zhejiang, Peoples R China;

    Zhejiang Univ, State Key Lab Chem Engn, Coll Chem & Biol Engn, Hangzhou 310027, Zhejiang, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    dynamic properties; elasticity; plasticity; shape memory; supramolecular networks;

    机译:动态特性;弹性;塑性;形状记忆;超分子网络;

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