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Mesoscale modeling of Controlled Degradation and Erosion of Polymer Networks

聚合物网络受控降解和侵蚀的中尺度建模

基本信息

批准号：
2110309
负责人：
Olga Kuksenok
金额：
$ 31万
依托单位：
Clemson University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-15 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110309&HistoricalAwards=false
关键词：
Mesoscale modeling Controlled Degradation Erosion

项目摘要

NONTECHNICAL SUMMARY This award supports the development of a computational framework that captures the controlled degradation of polymer networks at the mesoscale. Understanding and controlling degradation of polymer networks plays a vital role in a multitude of applications from wound dressings and tissue adhesives to the delivery of drugs and the growth regulation of neural networks. Of particular interest is light-induced degradation with its potential to spatially resolve the control over physical and chemical properties of degrading polymeric materials. Current models, often analytic and continuum approaches that inform our understanding of polymer network degradation, focus on small or large length scales. Small and large length scales reach down to the level of individual atoms and extend to and beyond visible distances, respectively. Our understanding on intermediate length scales, from a few nanometers to tens or hundreds of micrometers that are known as the mesoscale, remains limited. The development of an efficient computational approach for modeling the dynamics of degrading polymer networks on the mesoscale is expected to advance fundamental understanding and help establish guidelines for efficient design of degradable materials. The project includes ample research and educational opportunities for graduate and undergraduate students; strong emphasis will be placed on recruiting students from underrepresented groups. Some of the outcomes of the research will be incorporated into courses taught by the PI, and relevant educational materials will be made available via a science and engineering gateway that is part of the Network for Computational Nanotechnology. TECHNICAL SUMMARY This award supports the development of a computational modeling framework that captures photo-controlled degradation of polymer networks at the mesoscale. A suitable Dissipative Particle Dynamics approach capturing erosion, reverse gelation, and elasticity of the degrading network will be developed and validated. To establish a reliable model of degradation, a modified Segmental Repulsive Potential will be used to prevent unphysical topological crossings of bonded polymer chains. As a model system, hydrogels formed by the end-linking of four-arm polyethylene glycol (PEG) macromolecular precursors, often referred to as tetra-PEG gels, are chosen. The four-arm PEG precursors can be modified during their synthesis by including photocleavable functional groups to enable controlled degradation. The applicability of the proposed model can be extended to various network architectures and used to study their behavior under externally imposed conditions. To demonstrate the utility of the developed mesoscale approach, it will be used to capture the effects of controlled degradation on spreading and reconstruction of nanogel particles at liquid-liquid interfaces. An original multiscale model of early stages of gel degradation will also be developed, where the mesoscale model will be effectively scaled up into the respective continuum model. The development of an efficient computational approach for modeling the dynamics of degrading polymer networks will advance fundamental knowledge by accounting for the specifics of network architecture, diffusion of all the involved species, hydrodynamic interactions, network heterogeneities, and reaction rate constants that are locally controlled by the solvent quality. Proposed simulation methods will allow to probe the applicability of relevant network theories at the mesoscale and may establish guidelines for future design of novel degrading materials with dynamically and spatially controlled properties. This project will train graduate and undergraduate students and support diversity. Research outcomes will be incorporated into courses taught by the PI, and relevant educational materials will be made available through the NanoHub portal.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.

非技术摘要该奖项支持开发一种计算框架，该框架能够捕捉中尺度上聚合物网络的受控降解。了解和控制聚合物网络的降解在从伤口敷料和组织粘合剂到药物输送和神经网络生长调节的众多应用中起着至关重要的作用。特别令人感兴趣的是光诱导的降解，它有可能在空间上解决对降解聚合物材料的物理和化学性质的控制。目前的模型，通常是解析式和连续式的方法，告诉我们对聚合物网络降解的理解，集中在小的或大的长度尺度上。小的和大的长度尺度分别延伸到单个原子的水平和可见距离之外。我们对中等长度尺度的理解仍然有限，从几个纳米到几十或几百微米，也就是所谓的中尺度。发展一种有效的计算方法来模拟降解聚合物网络在介观尺度上的动力学，有望促进对可降解材料的基本理解，并帮助建立有效设计可降解材料的指导方针。该项目包括为研究生和本科生提供大量的研究和教育机会；重点将是从代表性不足的群体中招收学生。这项研究的一些成果将被纳入国际和平研究所教授的课程，相关的教育材料将通过作为计算纳米技术网络一部分的科学和工程门户提供。技术概述该奖项支持开发一种计算建模框架，该框架可捕捉中尺度上聚合物网络的光控制降解。将开发和验证一个合适的耗散粒子动力学方法来捕捉降解网络的侵蚀、反向凝胶化和弹性。为了建立一个可靠的降解模型，将使用改进的链段排斥势来防止键合高聚物链的非物理拓扑交叉。作为模型体系，选择了由四臂聚乙二醇大分子前驱体末端连接形成的水凝胶，通常被称为四聚乙二醇凝胶。四臂聚乙二醇酯前体可以在合成过程中通过包括可光裂解官能团来进行修饰，以实现受控降解。该模型的适用性可以扩展到各种网络体系结构，并用于研究它们在外部强加条件下的行为。为了证明发展的中尺度方法的实用性，它将被用来捕捉受控降解对纳米凝胶粒子在液-液界面的扩散和重建的影响。还将开发凝胶降解早期阶段的原始多尺度模型，其中中尺度模型将有效地放大到相应的连续介质模型中。发展一种有效的计算方法来模拟降解聚合物网络的动力学，将通过考虑网络结构的细节、所有涉及的物种的扩散、流体动力相互作用、网络的非均质性以及局部受溶剂质量控制的反应速率常数来促进基础知识的发展。所提出的模拟方法将允许探索相关网络理论在介观尺度上的适用性，并可能为未来具有动态和空间可控性质的新型降解材料的设计提供指导。该项目将培养研究生和本科生，并支持多样性。研究成果将被纳入到PI教授的课程中，相关的教育材料将通过NanoHub门户网站提供。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。