Photoresponsive Bond Exchange in Liquid Crystalline Polymer Networks: A Route to Complex and Controllable Shape Shifting Materials

非技术总结：智能材料是那些能够对其条件做出反应，从而以期望的方式改变其行为的材料。其中一类是变形材料，它有能力以期望的方式改变它们的形状。这种可变形材料在人造肌肉、软机器人、自展开设备、智能窗户、自修复材料、粘合剂和许多其他领域具有应用潜力。这里开发的材料结合了(a)液晶弹性体（这是一种高度可变形的聚合物材料，在加热时可逆地改变形状）和(b)材料中的光激活化学变化，使其能够随意重塑，愈合和重新排列。该项目的总体范围旨在了解材料的分子结构与其卓越的特性和性能之间的潜在关系，并随后使用这些知识来设计改进的方法和这种智能材料的分子结构。除了技术进步之外，该项目还将有助于科罗拉多大学最近成立的材料科学与工程博士学位项目的“训练营”的发展，该项目的明确目标是吸引来自不同背景的学生，否则他们不会考虑或准备进入材料科学博士课程。该计划旨在招募和培养多样化的人才，包括大量的本科生和研究生，将化学合成，材料科学和聚合物化学强有力地结合起来。技术总结：尽管在这一领域有大量有前途的工作，但对完全可逆的、可变形的聚合物进行编程仍然是一个特殊的挑战。液晶网络（lcn）是最有能力的聚合物材料之一，因为它们提供了热可逆的应变，只需通过引导分子尺度的排列即可编程。尽管聚合物网络可以实现分子顺序的转换，但当试图编程新的或复杂的排列时，它也具有固有的局限性。在这里，光将被用作自由基介导的加成-碎片链转移（AFT）的刺激，从而导致网络内发生级联的动态共价键交换反应。利用这种化学，易于重新编程的变形材料将探索独立控制LC相形状，无序形状和开关温度。这项工作的总体目标是通过与编程条件相协调的化学设计来进一步理解、获取和实现基于aft的lcn，以开发通用的、易于重新编程的变形材料。这项工作将侧重于对这些智能材料及其宏观和纳米尺度设计的基本理解，包括：i)确定基本的形成-结构-属性-编程关系，用于开发可光聚合、可编程lcnb库；ii)结合宏观材料中不同的材料成分、排列和结构，以了解成分和结构的界面和梯度对材料驱动和响应的影响；iii)通过印迹和全息技术形成纳米级结构，以确定和理解特征尺寸与LC畴尺寸对键交换过程、有效性和LC对齐的影响。每个科学方向都与不同群体的本科生和研究生的教育和培训相结合。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

NON-TECHNICAL SUMMARY:Smart materials are those that are capable of responding to their conditions so as to change their behavior in a desired manner. One class of these is shape-shifting materials that have the capacity to alter their shape in a desired manner. Such shape-shifting materials have the potential for implementation in the fields of artificial muscles, soft robotics, self-deploying devices, smart windows, self-healing materials, adhesives, and many other areas. The materials developed here combine the benefits of (a) liquid crystalline elastomers (which are highly deformable polymeric materials that reversibly change shape upon heating) and (b)light-activated chemical changes in the material that enable it to be reshaped, healed, and realigned at will. The overall scope of the project seeks to understand the underlying relationships between the molecular structure of the materials and their remarkable properties and performance, and subsequently use that knowledge to design improved approaches and molecular structures for such smart materials. In addition to advances in technology, this project will also contribute to the development of a "bootcamp" in the recently formed PhD degree program in Materials Science and Engineering at the University of Colorado with the express goal of bringing in students from diverse backgrounds that would not otherwise consider or be ready to enter a PhD program in materials science. This program aims to recruit and train diverse personnel, comprising a large number of undergraduate and graduate students, in a powerful combination of chemical synthesis, materials science, and polymer chemistry.TECHNICAL SUMMARY:Programming fully reversible, shape-shifting polymers presents a special challenge despite a large amount of promising work in this area. Liquid crystalline networks (LCNs) represent one of the most capable polymeric materials because they offer thermoreversible strain, programmable simply by directing molecular-scale alignment. Although the polymer network enables the translation of molecular order to shape, it is also inherently limiting when trying to program new or complex alignment. Here, light will be used as a stimulus for radical-mediated addition-fragmentation chain transfer (AFT) that causes a cascading dynamic covalent-bond exchange reaction to occur within the network. Using this chemistry, readily reprogrammable shape-shifting materials will be explored with independent control over the LC phase shape, disordered shape, and the switching temperature. The overall objective of this work is to further the understanding, accessibility, and implementation of AFT-based LCNs through chemical design in coordination with programming conditions to develop versatile, readily reprogrammable shape-shifting materials. This work will focus on fundamental understanding of these smart materials and their design at both the macro- and nano-scale, including: i) determining fundamental formation-structure-property-programming relationships that will be used to develop a library of photopolymerizable, programmable LCNs, ii) combining disparate material compositions, alignments and structures in macroscale materials to understand the impact of interfaces and gradients in composition and structure on the material actuation and response, and iii) forming nano-scale structures through imprinting and holography to determine and understand the effects of feature size as compared to LC domain size on the bond-exchange process, its effectiveness, and the LC alignment. Each scientific direction is coupled to education and training of a diverse group of undergraduate and graduate students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.