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Self-Healing Polymers in Complex Water Matrices: Towards Smart Materials for the Environment

Self-Healing Polymers in Complex Water Matrices: Towards Smart Materials for the Environment
复杂水基质中的自修复聚合物:迈向环境智能材料
批准号:
2201361
负责人:
Bezawit Getachew
金额:
$37.49万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-08-15 至 2025-07-31

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中文摘要
翻译
负责任的环境管理要求减少合成聚合物的环境负担,这些聚合物正在自然水体中积累。与此同时,现代生活在很大程度上依赖于合成聚合物在许多行业中执行越来越复杂的功能,包括纺织、运输、医疗、水处理和电子行业。自修复聚合物是一种能够从物理或化学损伤中自动恢复的材料。这些聚合物提供了一个未开发的机会,通过减少废物来减轻塑料对环境的负担,同时也超过了目前合成聚合物的能力。然而,大多数自修复材料被设计为在惰性、无水分的环境中工作,而它们在现实世界、复杂环境中的性能却鲜为人知。这个研究项目将研究天然水中常见的离子如何影响自我修复过程。所获得的基本知识将用于设计可以在各种条件下自我修复的新型聚合物。高分子材料科学和智能材料的概念将被纳入莱斯大学的本科和研究生环境工程课程。这一教育目标将加强材料科学与环境工程交叉领域的跨学科技能发展。莱斯大学现有的项目将被用来从代表性不足的群体中招募和指导本科生,并为发展多元化的STEM劳动力做出贡献。该项目的总体目标是了解决定复杂水基质中自愈聚合物性能的关键因素。最广泛使用的自愈机制依赖于损伤界面上聚合物链扩散、重组和重新形成彼此键的能力。该项目将研究影响这种重新形成二级键的内在能力的主要变量。具体目标是:1)确定水质参数(硬度、盐度和总有机碳)对内在自愈机制的影响;2)从机理上理解水成分如何改变自愈行为;3)建立综合可控参数,在复杂环境中最大化自愈性能(即100%回收率)。将合成通过氢键和离子相互作用自愈的模型聚合物,并在复杂水基质存在下进行大量机械测试和微观分析,以量化不同水成分的自愈功能。动态力学分析、石英晶体微平衡耗散实验和光谱化学分析将用于获得自愈行为变化的机制理解。每个模型聚合物的侧链长度和交联密度将有所不同,以确定在复杂环境中最大化自我修复的条件。该项目的成功完成将有助于建立智能和响应性聚合物材料的设计原则,以应对紧迫的环境挑战。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。
英文摘要
Responsible environmental stewardship requires reducing the environmental burden of synthetic polymers, which are accumulating in natural water bodies. At the same time, modern life relies heavily on synthetic polymers to perform increasingly complex functions across many industries, including textiles, transportation, medical, water treatment, and electronics industries. Self-healing polymers are materials that can recover from physical or chemical damage autonomously. These polymers offer an untapped opportunity to mitigate the environmental burden of plastics by reducing waste, while also exceeding the capabilities of current synthetic polymers. However, most self-healing materials are designed to operate in inert, moisture-free environments, and their performance in real-world, complex environments is poorly understood. This research project will examine how common ions in natural waters impact the self-healing process. The fundamental knowledge that is gained will be used to engineer new polymers that can self-heal under a wide range of conditions. Concepts in polymer materials science and smart materials will be incorporated into the undergraduate and graduate environmental engineering curriculum at Rice University. This educational aim will fortify interdisciplinary skills development at the intersection of materials science and environmental engineering. Existing programs at Rice University will be leveraged to recruit and mentor undergraduate students from underrepresented groups and contribute to the development of a diverse STEM workforce. The overarching goal of this project is to understand the key factors that determine the performance of self-healing polymers in complex water matrices. The most widely used mechanisms for self-healing rely on the capacity of polymeric chains at a damage interface to diffuse, reorganize, and re-form bonds with one another. This project will investigate the primary variables that impact this intrinsic capacity to re-form secondary bonds. The specific objectives are to 1) determine the impact of water quality parameters – hardness, salinity, and total organic carbon – on intrinsic self-healing mechanisms, 2) derive mechanistic understanding of how water constituents alter self-healing behavior, and 3) establish synthetically controllable parameters that maximize self-healing performance (i.e., 100% recovery) in complex environments. Model polymers that self-heal via hydrogen bonding and ionic interactions will be synthesized, and bulk mechanical testing and microscopic profiling in the presence of complex water matrices will be applied to quantify self-healing as a function of different water constituents. Dynamic mechanical analysis, quartz crystal microbalance with dissipation experiments, and spectroscopic chemical analysis will be used to gain mechanistic understanding of changes in self-healing behavior. Sidechain length and crosslinking density for each model polymer will be varied to identify conditions that maximize self-healing in complex environments. The successful completion of this project will help establish design principles for smart and responsive polymeric materials that address pressing environmental challenges.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.
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