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

基础研究普遍获得生物组织的机会有限，从而限制了容易进行的实验的类型和数量。对于那些不具备进一步组织工程研究所需基础设施的美国高校来说，这是一个特别具有挑战性的问题。这笔赠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，这些信息将被任何机构的研究人员、教师和学生广泛获取。通过组织结构的成像(读取)、数字存储和基于激光的制造(写入)的顺序过程来获得详细信息。从这一过程中获得的数据将被上传到一个可访问的数据储存库，以便于广泛传播。该项目还将提供一个平台，招收STEM领域不同和代表性不足的群体的学生，通过与当地大学工程学分会和以科学为基础的学生亲和会建立战略伙伴关系，了解新兴的先进生物制造领域。研究方法的方方面面，以及所学到的材料，也将被纳入新的和现有的课程，以及为免费访问的NanHUB.org网络基础设施平台上的新的跨学科在线课程开发的讲座模块。该奖项利用多个学科的融合来创建一个基于双光子聚合(TPP)的数字制造平台，该平台将使基于云的读写3D胶原纤维组织中不同复杂性的支架成为可能。长波长(近红外)光脉冲和长工作距离物镜将被用于实现先前报道的大于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，这将与生物学相关的3D体外模型的规模相同。光学波前整形技术的使用分别实现了书写伪影的并行化和减少。基于机器学习的过程控制框架促进了对TPP过程可变性的基本理解，并促进了高通量、高保真的支架生物制造。这项研究通过将这些学科与生物制造中复杂的、独特的数据结构和类型联系起来，并允许以胶原蛋白为基础的机械超材料的原型，为统计学和机器学习领域做出贡献。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。

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.