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

聚合物纳米粒子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、质量变化性和粒子功能范围狭窄的限制，导致新材料、配方和规模化使用的开发周期缓慢，往往长达十年。为了使制造多功能聚合物纳米颗粒的未来技术成为可能，有必要从根本上改变这种范例，使其能够对加工条件进行数据驱动的决策。这一独特的未来制造研究基金(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，该纳米粒子具有更大的尺寸、形状和化学功能，可在不同行业中广泛应用。所研究的聚合物纳米颗粒网络制造的新范式将通过在基于化学气相沉积的连续合成和在线表征聚合物纳米结构方面取得关键突破，利用人工智能(AI)指导的复杂工艺条件选择进行集成。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，并从总体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扩展到本科生和研究生教育以及行业参与的学术生涯培养一支多样化的劳动力队伍，该团队将建立一个框架，以理解基于身份的动机，这可能反过来导致更广泛的参与STEM。这项未来制造研究的总体目标是开发和研究一种新的范式，通过集成连续加工、在线表征和支持人工智能的加速数据分析来指导编程聚合物纳米粒子的生产，从而彻底改变聚合物纳米材料的制造。这项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，这将留下可用于精确计算机视觉图像和数据采集的制造特征(空间和时间)的光学指纹。高通量的实验数据将被用来训练卷积神经网络，以识别聚合物颗粒的尺寸、形状和化学成分。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的分析和处理决策。跨学科研究的结果将产生关于ICVD、加工监测和使用网络驱动的方法的机制的新知识。这些发现有望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，这是传统制造方法无法实现的。FMRG得到了土木工程、机械和制造创新(ENG/CMMI)、数学科学(MPS/DMS)、化学(MPS/CHE)、工程教育和中心(ENG/EEC)和本科生教育部门(EHR/DUE)的支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.