课题基金 / 基金详情

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

项目摘要

项目成果

相似基金

相关文献

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,这些聚合物在自然水体中积累。与此同时,现代生活在很大程度上依赖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。自修复聚合物是可以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尚未开发的机会,通过减少浪费来减轻塑料的环境负担,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,并且它们在现实世界中的复杂环境中的性能知之甚少。本研究项目将研究自然沃茨中常见的离子如何影响自我修复过程。所获得的基础知识将用于设计新的聚合物,这些聚合物可以在各种条件下自我修复。聚合物材料科学和智能材料的概念将被纳入莱斯大学的本科生和研究生环境工程课程。这一教育目标将加强材料科学和环境工程交叉点的跨学科技能发展。莱斯大学的现有项目将被用来招募和指导来自代表性不足群体的本科生,并为多元化STEM劳动力的发展做出贡献。该项目的总体目标是了解决定自修复聚合物在复杂水基质中性能的关键因素。最广泛使用的自愈机制依赖于损伤界面处聚合物链的扩散、重组和重新形成彼此键的能力。本项目将研究影响这种重新形成次级债券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)获得对水成分如何改变自愈行为的机械理解,以及3)建立使自愈性能最大化的综合可控参数(即,100%恢复)。将合成通过氢键和离子相互作用自愈的模型聚合物,并在复杂的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能力。动态力学分析,石英晶体微天平与耗散实验,光谱化学分析将被用来获得自我修复行为的变化的机械理解。每种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限度地自我修复的条件。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NSF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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
海外基金