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: 2323730
• 负责人: Andrew Ferguson
• 金额: $ 42.18万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323730&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：2020 年，热塑性塑料、热固性塑料、弹性体和凝胶等聚合物材料的全球产量达到 3.67 亿吨。然而，由于其结构是在分子水平上，当前的聚合物设计必须取得平衡介于坚硬、耐用和易于成型或模制之间。聚合物设计的最新进展利用更灵活的分子连接，为更坚固、更耐用的材料开辟了机会。尽管取得了这些成就，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决定其性能以及加工的难易程度了解有限。该合作项目旨在使用系统的、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来克服这些挑战，这些聚合物可以承受极端条件，在极端条件下，它们必须在巨大的机械力下生存并在损坏时自我修复。这项研究旨在建立一个全面、加速的材料发现循环，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化制造以及机器学习引导设计。该项目与材料基因组计划的目标一致，利用自动化、模拟、快速合成、机器学习和 3D 打印来加速高性能聚合物的设计和发现。此次合作的成功成果将导致创造出具有前所未有的机械性能和可加工性的聚合物，这些聚合物适合生产可穿戴传感器、软执行器和能量收集设备，并与未来的制造工艺兼容。 技术描述：该 DMREF 项目致力于创建自适应聚合物网络的综合实验和计算数据库，展示出卓越的拉伸性、高弹性和令人印象深刻的自愈能力。主要重点在于开发新型双螺纹滑环聚合物、具有动态共价化学键的大分子以及两者的双网络。其目的是超越聚合物设计中传统的刚性-韧性-可加工性范式，并将其编织成自动发现循环，以探索材料空间的新领域。高通量合成和自动化实验将加速聚合物的发现，并在此过程中通过分子动力学模拟获得信息。这些实验生成的数据将用于完善基于机器学习的主动学习过程，以实现属性优化。此外，该项目将 3D 打印引入设计工作流程，作为验证机械性能的独特平台，同时改进其可制造性。这项合作研究总体上希望实现几项关键进展：（1）开创性地使用双螺纹聚合物来制造滑环聚合物，以增强拉伸性； （2）将动态共价聚合物设计与基于挤出的3D打印相结合，增强从纳米到宏观的自修复性能； (3)通过使用滑环和动态共价聚合物的双网络聚合物结构实现高韧性、高刚性和高加工性能。该项目创造了跨学科学习机会，以丰富 K-12、本科生和研究生的研究经验，包括可免费访问的在线“聚合物设计播放列表”。该奖项反映了 NSF 的法定使命，并通过评估被认为值得支持利用基金会的智力优势和更广泛的影响审查标准。