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

该创新-技术转化伙伴关系（PFI-TT）项目的更广泛影响/商业潜力包括一项技术的研究和商业化，该技术将促进对齐聚合物纤维材料和长达数百米的强力纤维纱线的卷对卷生产。对准的导电材料由于其提供对导电性和导热性的方向控制以及产生用于目标光学和表面性质的精确图案化表面的能力而对于广泛的应用是期望的。这些材料可以作为高强度可膨胀复合材料和纺织品的基本构件。尽管定向纳米纤维具有显著的前景，但由于各种技术障碍，缺乏商业规模的卷对卷定向聚合物纤维制造。该项目将一个学术研究团队和一家成熟的电子烟制造公司联系起来，以翻译克服这些障碍的技术。除了商业影响外，该项目还将开发一个新的实践教育项目，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。该项目旨在提高平行轨道卷对卷静电纺丝的效率，产量和一致性，用于对齐的纤维制造。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。生产效率将被测量为辊上对齐的聚合物输出的质量除以进入系统的聚合物进料的质量。卷积神经网络（CNN）机器学习模型也将用于将电场模型图像与实验测量的生产效率相关联。可以同时为数百个不同的参数组合生成计算电场模型，而不是仅通过实验测试几个参数组合。因此，该方法允许对与该复杂制造工艺相关联的广泛参数空间进行有效的计算机筛选，以使器件效率最大化。优化后的设备将进行缩放、原型制作和评估，以展示平行轨道卷对卷对齐静电纺丝制造在商业环境中的能力。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.