Collaborative Research: DMREF: Closed-Loop Design of Polymers with Adaptive Networks for Extreme Mechanics

合作研究：DMREF：采用自适应网络进行极限力学的聚合物闭环设计

• 批准号: 2413579
• 负责人: Chenfeng Ke
• 金额: $ 42.11万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2413579&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Closed; Loop

Non-technical Description: Polymer materials such as thermoplastics, thermosets, elastomers, and gels, were produced on a massive scale of 367 million tons globally in 2020. However, due to their construction at the molecular level, current polymer designs must strike a balance between being hard, durable, and easy to shape or mold. Recent advancements in polymer design take advantage of more flexible molecular connections to open up opportunities for more robust, long-lasting materials. Despite these achievements, making polymers with advanced properties remains a grand challenge because current designs largely depend on the researcher's intuition, and there is limited understanding of how the structure of a polymer determines its properties and how easy it is to process. This collaborative project seeks to use a systematic, data-driven approach to overcome these challenges by developing polymers with adaptive molecular structures that can withstand extreme conditions where they must survive exposure to large mechanical forces and repair themselves when damaged. This research aims to establish a comprehensive, accelerated materials discovery loop that includes multiscale computational simulations, rapid polymer synthesis, automated fabrication with tandem mechanical characterization, and machine learning-guided design. This project aligns with the objectives of the Materials Genome Initiative, using automation, simulations, rapid synthesis, machine learning, and 3D printing to speed up the design and discovery of high-performance polymers. The successful outcome of this collaboration will lead to the creation of polymers with unprecedented mechanical properties and processibility that are suitable for producing wearable sensors, soft actuators, and energy harvesting devices and be compatible with future manufacturing processes.Technical Description: This DMREF project endeavors to create an integrated experimental and computational database of adaptive polymer networks, showcasing exceptional stretchability, high resilience, and impressive self-healing abilities. The primary focus lies in the development of novel double-threaded slide-ring polymers, macromolecules with dynamic covalent chemical linkages, and double networks with both. The aim is to transcend the conventional rigidity-toughness-processibility paradigm in polymer design and weave this into an automated discovery loop to explore new areas of materials space. High-throughput synthesis and automated experiments will accelerate the discovery of polymers and be informed by molecular dynamics simulations along the way. The data generated from these experiments will be harnessed to refine a machine learning-based active learning process for property optimization. Moreover, the project introduces 3D printing into the design workflow as a unique platform to validate mechanical properties while refining their manufacturability. This collaborative research overall aspires to deliver several key advancements: (1) pioneering the use of double-threaded polymers to fabricate slide-ring polymers for enhanced stretchability; (2) integrating dynamic covalent polymer design with extrusion-based 3D printing to enhance self-healing properties from the nano-to-macroscales; and (3) realizing high toughness, high rigidity, and high processibility via double network polymers construction using both slide-ring and dynamic covalent polymers. This project creates cross-discipline learning opportunities to enrich the research experiences of K-12, undergraduate, and graduate students, including a freely accessible online "polymer design playlist."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.

非技术描述：聚合物材料，例如热塑性材料，热塑性，弹性体和凝胶，在2020年全球范围为3.67亿吨的大规模生产。但是，由于它们在分子水平上的构造，当前的聚合物设计必须在坚硬，耐用，易于形状或霉菌之间达到平衡，且易于形状。聚合物设计的最新进展利用了更灵活的分子连接，为更健壮，持久的材料打开机会。尽管取得了这些成就，但制造具有先进性能的聚合物仍然是一个巨大的挑战，因为当前的设计很大程度上取决于研究人员的直觉，并且对聚合物的结构如何确定其特性以及处理方式的理解有限。该协作项目试图使用一种系统的，数据驱动的方法来克服这些挑战，通过开发具有适应性分子结构的聚合物，可以承受极端条件，在这种情况下，它们必须在暴露于大型机械力的情况下幸存下来并在损坏时进行修复。这项研究旨在建立一个全面的，加速的材料发现循环，其中包括多尺度计算模拟，快速聚合物合成，具有串联机械表征的自动制造以及机器学习指导的设计。该项目与材料基因组计划的目标相吻合，使用自动化，模拟，快速合成，机器学习和3D打印，以加快高性能聚合物的设计和发现。这项合作的成功结果将导致创建具有前所未有的机械性能和过程的聚合物，适合生产可穿戴的传感器，柔软的执行器和能量收集设备，并与未来的制造过程相吻合，并与未来的制造过程相吻合。技术描述：这种DMREF促进了促进的远程延伸性，以实验性的实验性和计算性数据库，适用于适应性的Prolimentival ofstrimential offormentional network soptive of网络。韧性和令人印象深刻的自我修复能力。主要重点是开发新型的双线幻灯片聚合物，具有动态价值化学连接的大分子以及两者都具有双重网络。目的是超越聚合物设计中常规的刚性 - 围场实践范式，并将其编织到自动发现循环中，以探索材料空间的新领域。高通量合成和自动实验将加速聚合物的发现，并通过一路上的分子动力学模拟告知。从这些实验中生成的数据将得到利用，以完善基于机器学习的主动学习过程以进行属性优化。此外，该项目将3D打印引入了设计工作流程中，作为一个独特的平台，以验证机械性能，同时可提高其可制造性。这项合作研究的整体渴望提供几个关键的进步：（1）开创使用双线程聚合物来制造滑环聚合物以增强可伸展性的方法； （2）将动态共价聚合物设计与基于挤出的3D打印相结合，以增强纳米到宏观的自我修复特性； （3）使用滑环和动态共价聚合物，通过双网络聚合物构建实现高韧性，高刚度和高处理性。该项目创造了跨学科的学习机会，以丰富K-12，本科生和研究生的研究经验，包括可自由访问的在线“ Polymer Design Playlist”。该奖项反映了NSF的法定任务，并被视为值得通过基金会的知识分子和更广泛影响的评估来通过评估来获得的支持。