PFI-TT: Commercial scale production of aligned polymer nanofiber materials and yarns

PFI-TT：定向聚合物纳米纤维材料和纱线的商业规模生产

• 批准号: 2345785
• 负责人: Vince Beachley
• 金额: $ 51.19万
• 依托单位: Rowan University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2345785&HistoricalAwards=false
• 关键词: PFI; TT; Commercial; scale; production

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project includes research and commercialization of a technology that will facilitate roll-to-roll production of aligned polymer nanofiber materials and strong nanofiber yarns up to hundreds of meters long. Aligned nanofiber materials are desirable for a wide spectrum of applications due to their ability to provide directional control over electrical and thermal conductivity and creation of precisely-patterned surfaces for targeted optical and surface properties. These materials can serve as essential building blocks for high-strength nanofiber composites and textiles. Despite the remarkable promise of aligned nanofibers, there is an absence of commercial-scale roll-to-roll aligned polymer nanofiber manufacturing due to diverse technical hurdles. This project connects an academic research team and an established nanofiber manufacturing company to translate technologies that will overcome these hurdles. Beyond its commercial impacts, this project will lead to the development of a new hands-on educational program and provide students with valuable experience and training opportunities in entrepreneurship, technology translation, and innovation.The proposed project aims to enhance the efficiency, throughput, and consistency of parallel track roll-to-roll electrospinning for aligned nanofiber manufacturing. It is hypothesized that computational electrical field models can predict production efficiency. Computational electrical field models will be generated based on manufacturing process parameters. The production efficiency will be measured as the mass of aligned nanofiber output on a roll, divided by the mass of polymer feed into the system. A convolutional neural network (CNN) machine learning model will also be utilized to correlate electrical field model images with experimentally measured production efficiency. Computational electrical field models can be generated for hundreds of different parameter combinations at the same time instead of testing only a few parameter combinations experimentally. Therefore, this approach allows for efficient in silico screening of the extensive parameter space associated with this complex manufacturing process to maximize the device efficiency. Optimized devices will be scaled, prototyped, and evaluated, demonstrating the capability of parallel track roll-to-roll aligned nanofiber electrospinning manufacture in a commercial context.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.

该创新合作伙伴关系 - 技术转化 (PFI-TT) 项目具有更广泛的影响/商业潜力，包括一项技术的研究和商业化，该技术将促进卷对卷生产排列聚合物纳米纤维材料和长达数百米的强力纳米纤维纱线长的。对齐的纳米纤维材料因其能够提供对电导率和导热率的方向控制以及为目标光学和表面特性创建精确图案化表面的能力而被广泛应用。这些材料可以作为高强度纳米纤维复合材料和纺织品的重要组成部分。尽管定向纳米纤维有着非凡的前景，但由于存在多种技术障碍，目前还缺乏商业规模的卷对卷定向聚合物纳米纤维制造。该项目将一个学术研究团队和一家成熟的纳米纤维制造公司联系起来，以转化克服这些障碍的技术。除了商业影响之外，该项目还将促进新的实践教育计划的开发，并为学生提供创业、技术转化和创新方面的宝贵经验和培训机会。该项目旨在提高效率、吞吐量和用于对齐纳米纤维制造的平行轨道卷对卷静电纺丝的一致性。假设计算电场模型可以预测生产效率。将根据制造工艺参数生成计算电场模型。生产效率将通过辊上输出的排列纳米纤维的质量除以进入系统的聚合物进料的质量来测量。卷积神经网络 (CNN) 机器学习模型还将用于将电场模型图像与实验测量的生产效率相关联。可以同时生成数百种不同参数组合的计算电场模型，而不是仅通过实验测试少数参数组合。因此，这种方法可以对与复杂制造工艺相关的广泛参数空间进行有效的计算机筛选，以最大限度地提高器件效率。优化的设备将进行规模化、原型设计和评估，展示在商业环境中平行轨道卷对卷对齐纳米纤维静电纺丝制造的能力。该奖项反映了 NSF 的法定使命，并通过使用基金会的知识产权进行评估，被认为值得支持。优点和更广泛的影响审查标准。