Collaborative Research: A Digital Manufacturing Platform to Democratize Biological Tissue Access Using Smart Two-Photon Polymerization

用于基础研究的生物组织的普遍获取是有限的，从而限制了可以容易地进行的实验的类型和数量。这对于那些不具备必要基础设施来进一步开展组织工程研究的美国学院和大学来说是一个特别具有挑战性的问题。该拨款支持通过提取和存储组织结构信息来缓解这一挑战的研究，这些信息将广泛供任何机构的研究人员、教师和学生使用。通过成像（阅读）、数字存储和基于激光的组织结构制造（写入）的顺序过程获得详细信息。从这一进程中获得的数据将上载到一个可查阅的数据储存库，以便利广泛传播。该项目还将提供一个平台，从STEM领域的多元化和代表性不足的群体中招募学生，通过与当地大学工程分会和科学学生亲和团体的战略伙伴关系，了解先进生物制造的新兴领域。研究方法的各个方面，以及所学到的材料，也将被纳入新的和现有的课程，并在免费访问的nanoHUB.org网络基础设施平台上为新的跨学科在线课程开发的讲座模块。该奖项利用学科的融合来创建基于双光子聚合（TPP）的数字制造平台，该平台将实现基于云的阅读和写入3D胶原纤维组织中具有不同复杂性的支架。长波长（近红外）光脉冲和长工作距离物镜将用于实现大于先前报道的5倍的穿透深度，从而产生1 mm x 1 mm x 0.5 mm的打印支架体积，这将与生物相关的3D体外模型具有相同的规模。光学波前整形技术的使用分别实现了写入伪影的并行化和减少。基于机器学习的过程控制框架推进了对TPP过程可变性的基本理解，并促进了支架的高通量，高保真度生物制造。该研究通过将这些学科与生物制造中复杂、独特的数据结构和类型联系起来，为统计学和机器学习领域做出了贡献，并允许胶原基机械超材料的原型制作。该奖项反映了NSF的法定使命，并通过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，被认为值得支持。

Universal access to biological tissues for fundamental studies is limited, thereby constraining both the type and number of experiments that can be readily carried out. This is a particularly challenging problem for U.S. colleges and universities that do not possess the necessary infrastructure to further their tissue engineering research. This grant supports research to mitigate this challenge by extracting and storing tissue-structure information, which will be made broadly accessible to researchers, teachers, and students at any institution. The detailed information is obtained through the sequential process of imaging (reading), digitally storing, and laser-based manufacturing (writing) of the tissue architecture. Data obtained from this process will be uploaded onto an accessible data repository to facilitate broad dissemination. The project will also provide a platform to recruit students from diverse and underrepresented groups in STEM fields to learn about the emerging field of advanced biomanufacturing through strategic partnerships with local university chapters of engineering and science-based student affinity groups. Aspects of the research methods, as well as materials learned, will also be incorporated into both new and existing courses, and lecture modules developed for a new interdisciplinary online course on the freely accessible nanoHUB.org cyberinfrastructure platform. This award utilizes a convergence of disciplines to create a digital manufacturing platform, based on two-photon polymerization (TPP), that will enable cloud-based reading and writing of scaffolds with varying complexity in 3D collagen-fiber organization. Long-wavelength (near-infrared) optical pulses and long-working distance objectives will be used to enable penetration depths greater than 5x that has previously been reported, resulting in printed scaffolds volumes of 1 mm x 1 mm x 0.5 mm, which would be on the same scale as biologically relevant 3D in vitro models. The use of optical wavefront-shaping technology enables parallelization and reduction of writing artifacts, respectively. The machine-learning-based process control framework advances the fundamental understanding of TPP process variability, and facilitate high-throughput, high-fidelity biomanufacturing of scaffolds. This research contributes to the fields of statistics and machine learning by linking these disciplines to complex, unique data structures and types in biomanufacturing, as well as permit prototyping of collagen-based mechanical metamaterials.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.