Equipment: MRI: Track 2 Acquisition of an Automated High-Throughput System for Combinatorial Design and Development of Complex Polymer Systems

设备： MRI：轨道 2 获取用于复杂聚合物系统的组合设计和开发的自动化高通量系统

• 批准号: 2320276
• 负责人: Charles Schroeder
• 金额: $ 359.6万
• 依托单位: University of Illinois at Urbana-Champaign
• 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: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320276&HistoricalAwards=false
• 关键词: Equipment; MRI; Track; Acquisition; Automated

This Major Research Instrumentation (MRI) award supports the acquisition of an automated system for high-throughput formulation and characterization of complex polymer materials at the University of Illinois at Urbana-Champaign. This state-of-the-art system integrates component dispensing, mixing, and processing with high-throughput rheological, optical, and thermal characterization of materials. The automated system will enable AI-guided, closed-loop approaches for the design, discovery, and development of new polymeric materials for energy-efficient manufacturing, multi-functional polymers, and new sustainable thermoplastics and thermosets. The automated system is designed to accommodate a variety of research workflows and will include in-line characterization equipment including a rheometer and a differential scanning calorimeter (DSC). Automation will enable data-driven discovery of new materials by drastically increasing the number of samples created and analyzed (up to 50x increase) and the data usability (up to 100x increase) by systematically generating annotated datasets. Production of well-curated data will further increase the impact of the research by promoting the publishing of data to open access national repositories. The instrument will promote synergistic connections between Illinois and external academic and industrial partners while leveraging existing institutes and materials research efforts on campus. Overall, the high-throughput system will fundamentally change how advanced materials are designed and developed by enabling data-driven, closed-loop design and characterization approaches for complex formulations of polymeric materials. This research is aimed at advancing the rate of innovation in materials discovery by enabling data-driven approaches in polymer science. The proposed instrumentation will support the training of students and researchers to include high-throughput, robotic, and data-driven methodologies, which is critical for modern industry and advanced materials manufacturing. Automated instrumentation fundamentally changes how materials research is conducted by enabling high-throughput discovery campaigns, enhanced repeatability of experimental measurements, and improved accuracy of data. Automated characterization will further facilitate the use of artificial intelligence-based methods for materials discovery by greatly expanding the chemical and physical properties space that can be experimentally explored. This research will enable the rapid discovery and development of high-performance polymer materials to address the most pressing challenges in energy, sustainability, and advanced manufacturing. This instrumentation will bring unique capabilities by accelerating fundamental and applied polymer research, while further preparing the next-generation work force for future careers in materials science, chemical engineering, aerospace, data science, and related disciplines that rely on polymer materials. The instrument will support educational outreach efforts and undergraduate research laboratories while further engaging with students in the Worldwide Youth in Science and Engineering (WYSE) camps and the Beckman Institute Open House activities.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.

该主要研究仪器（MRI）奖支持在伊利诺伊大学厄巴纳-香槟分校获得高通量配方和复杂聚合物材料表征的自动化系统。这种最先进的系统集成了组件的分配，混合和加工，具有高通量流变学，光学和材料的热特性。自动化系统将为节能制造、多功能聚合物以及新型可持续热塑性塑料和热固性材料的新聚合物材料的设计、发现和开发提供人工智能引导的闭环方法。自动化系统旨在适应各种研究工作流程，并将包括在线表征设备，包括流变仪和差示扫描量热计（DSC）。自动化将通过大幅增加创建和分析的样本数量（最多增加50倍）和通过系统生成注释数据集的数据可用性（最多增加100倍）来实现数据驱动的新材料发现。通过促进向开放获取的国家存储库发布数据，生成精心整理的数据将进一步增加研究的影响。该工具将促进伊利诺伊州与外部学术和工业合作伙伴之间的协同联系，同时利用校园内现有的研究所和材料研究工作。总的来说，高通量系统将从根本上改变先进材料的设计和开发方式，为复杂的聚合物材料配方提供数据驱动、闭环设计和表征方法。这项研究旨在通过在聚合物科学中启用数据驱动的方法来提高材料发现的创新速度。拟议的仪器将支持学生和研究人员的培训，包括高通量、机器人和数据驱动的方法，这对现代工业和先进材料制造至关重要。自动化仪器从根本上改变了材料研究的开展方式，实现了高通量的发现活动，增强了实验测量的可重复性，提高了数据的准确性。自动化表征将进一步促进基于人工智能的材料发现方法的使用，大大扩展了可以通过实验探索的化学和物理性质空间。这项研究将使高性能聚合物材料的快速发现和发展，以解决能源、可持续性和先进制造方面最紧迫的挑战。该仪器将通过加速基础和应用聚合物研究带来独特的能力，同时进一步为材料科学、化学工程、航空航天、数据科学和依赖聚合物材料的相关学科的未来职业做好准备。该仪器将支持教育推广工作和本科生研究实验室，同时进一步与世界青年科学与工程（WYSE）营地和贝克曼研究所开放日活动的学生接触。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。