FMRG: Cyber: Scalable Precision Manufacturing of Programmable Polymer Nanoparticles Using Low-temperature Initiated Chemical Vapor Deposition Guided by Artificial Intelligence

FMRG：网络：利用人工智能引导的低温引发化学气相沉积进行可编程聚合物纳米粒子的可扩展精密制造

• 批准号: 2229092
• 负责人: Rong Yang
• 金额: $ 300万
• 依托单位: Cornell University
• 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: 2023-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229092&HistoricalAwards=false
• 关键词: FMRG; Cyber; Scalable; Precision; Manufacturing

Polymer nanoparticles have a broad range of significant applications, including drug delivery, soft robotics and nanomedicine, etc. However, existing manufacturing of such materials are limited by batch production, quality variability and a narrow range of particle functionality, resulting in a slow development cycle, often decade long, for new materials, recipes, and use at scale. To enable a future technology in manufacturing versatile polymer nanoparticles, it is necessary to radically change such a paradigm into one that enables data-driven decision-making for processing conditions. This unique future manufacturing research grant (FMRG) will advance fundamental research in the principles for continuous and highly reproducible manufacturing of polymer nanoparticles with an expanded palette of sizes, shapes and chemical functionalities for wide-ranging applications in various industries. The new paradigm for the studied cyber-manufacturing of polymer nanoparticles will be achieved through critical breakthroughs in chemical-vapor-deposition based continuous synthesis and in-line characterization of polymeric nanostructures, integrated using artificial intelligence (AI)-guided selections of complex processing conditions. This research, if successful, will unlock vast design space for future nanomedicine, with a potential to substantially impact the healthcare industry and improve the quality of life of the society by and large. Moreover, to prepare a diverse workforce for future manufacturing advancements, rigorous education research across an academic lifespan, from K-12 outreach to undergraduate and graduate education, as well as industry engagements, the team will establish a framework to understand identity-based motivation, which may in turn lead to broadened participation in STEM.The overall goal of this future manufacturing research is to develop and investigate a novel paradigm to revolutionize manufacturing of polymer nanomaterials by integrating continuous processing, in-line characterization and AI-enabled accelerated data analysis to guide the production of programmed polymer nanoparticles. The core of this future manufacturing technology, also the key innovation, is a low-temperature initiated chemical vapor deposition (iCVD) polymerization with the use of gradient-surface-templated liquid crystals, which will leave optical fingerprints of fabricated features (spatial and temporal) that can be utilized for precision computer-vision image and data acquisitions. The high-throughput experimental data will be employed to train a convolutional neural network to identify the size, shape and chemistry of polymer particles. The neural network will be further tested with separate data sets for validations to achieve AI-accelerated analytics and processing decision making. The outcome of the interdisciplinary research will generate new knowledge in iCVD, processing monitoring and mechanism using cyber-driven approaches. The findings are expected to enable the novel manufacturing technology, scalable and modular, for unprecedented polymer structures, unachievable by traditional manufacturing means.This FMRG is supported by the Divisions of Civil, Mechanical and Manufacturing Innovation (ENG/CMMI), Mathematical Sciences (MPS/DMS), Chemistry (MPS/CHE), Engineering Education and Centers (ENG/EEC), and the Division of Undergraduate Education (EHR/DUE).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.

聚合物纳米颗粒具有广泛的重要应用，包括药物输送、软机器人和纳米医学等。然而，此类材料的现有制造受到批量生产、质量变异性和颗粒功能范围窄的限制，导致开发周期缓慢，通常长达十年，用于新材料、配方和大规模使用。为了实现制造多功能聚合物纳米粒子的未来技术，有必要从根本上将这种范式转变为能够针对加工条件进行数据驱动决策的范式。这项独特的未来制造研究补助金 (FMRG) 将推进聚合物纳米粒子连续和高度可重复制造原理的基础研究，该纳米粒子具有扩展的尺寸、形状和化学功能，可在各个行业中广泛应用。聚合物纳米颗粒网络制造研究的新范式将通过基于化学气相沉积的连续合成和聚合物纳米结构在线表征的关键突破，并使用人工智能（AI）引导的复杂加工条件选择进行集成来实现。这项研究如果成功，将为未来纳米医学释放巨大的设计空间，有可能对医疗保健行业产生重大影响，并总体上改善社会的生活质量。此外，为了为未来的制造业进步、从 K-12 推广到本科和研究生教育以及行业参与的整个学术生命周期的严格教育研究做好准备，为多样化的劳动力做好准备，该团队将建立一个框架来理解基于身份的动机，这可能反过来会扩大对 STEM 的参与。这项未来制造研究的总体目标是开发和研究一种新颖的范例，通过集成连续处理、在线表征和人工智能加速数据分析来彻底改变聚合物纳米材料的制造指导程序制作聚合物纳米颗粒。这种未来制造技术的核心，也是关键的创新，是使用梯度表面模板液晶的低温引发化学气相沉积（iCVD）聚合，这将留下制造特征（空间和时间）的光学指纹。 ）可用于精确的计算机视觉图像和数据采集。高通量实验数据将用于训练卷积神经网络来识别聚合物颗粒的尺寸、形状和化学性质。神经网络将使用单独的数据集进行进一步测试以进行验证，以实现人工智能加速分析和处理决策。跨学科研究的成果将产生 iCVD、加工监控和使用网络驱动方法的机制方面的新知识。研究结果预计将为前所未有的聚合物结构提供可扩展和模块化的新型制造技术，这是传统制造手段无法实现的。该 FMRG 得到土木、机械和制造创新部门 (ENG/CMMI)、数学科学部门 (MPS) 的支持/DMS）、化学（MPS/CHE）、工程教育和中心（ENG/EEC）以及本科教育部（EHR/DUE）。该奖项反映了 NSF 的法定使命，并被认为值得通过以下方式获得支持：使用基金会的智力价值和更广泛的影响审查标准进行评估。