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，现有的此类材料的制造受到批量生产，质量变化和颗粒功能范围狭窄的限制，导致新材料，配方和规模使用的开发周期缓慢，通常长达十年。为了实现制造多功能聚合物纳米颗粒的未来技术，有必要从根本上改变这种范式，使其能够对加工条件进行数据驱动的决策。这项独特的未来制造研究资助（FMRG）将推进聚合物纳米颗粒连续和高度可重复制造原理的基础研究，这些纳米颗粒具有扩展的尺寸，形状和化学功能，可用于各种行业的广泛应用。聚合物纳米粒子的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的在线表征的关键突破来实现，并使用人工智能（AI）指导选择复杂的加工条件。这项研究如果成功，将为未来的纳米医学打开巨大的设计空间，有可能对医疗保健行业产生重大影响，并在很大程度上提高社会的生活质量。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学术生命周期中严格的教育研究以及行业参与准备多元化的劳动力，该团队将建立一个框架来理解基于身份的动机，这可能反过来导致STEM的参与范围扩大。这项未来制造研究的总体目标是开发和研究一种新的通过集成连续加工、在线表征和人工智能加速数据分析来指导编程聚合物纳米颗粒的生产，从而彻底改变聚合物纳米材料的制造模式。这种未来制造技术的核心，也是关键创新，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，以识别聚合物颗粒的大小，形状和化学性质。神经网络将进一步使用单独的数据集进行验证，以实现AI加速分析和处理决策。跨学科研究的成果将产生新的知识，在iCVD，处理监测和机制使用网络驱动的方法。这些发现有望使新型制造技术，可扩展和模块化，用于前所未有的聚合物结构，这是传统制造手段无法实现的。（ENG/CMMI），数学科学（MPS/DMS），化学（MPS/CHE），工程教育和中心（ENG/EEC），该奖项反映了NSF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。