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

普遍获得用于基础研究的生物组织是有限的，从而限制了可以随时进行的实验的类型和数量。对于没有必要的基础设施来进一步开展组织工程研究的美国高校来说，这是一个特别具有挑战性的问题。该基金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，这些信息将广泛地提供给任何机构的研究人员、教师和学生。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）的顺序过程获得详细信息。从这一过程中获得的数据将上载到一个可访问的数据储存库，以促进广泛传播。该项目还将提供一个平台，招收来自STEM领域不同和代表性不足群体的学生，通过与当地大学工程和科学学生亲和团体的战略合作伙伴关系，了解新兴的先进生物制造领域。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，以及在nanoHUB.org网络基础设施平台上为一个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一个基于双光子聚合（TPP）的数字制造平台，这将使基于云的3D胶原纤维组织中具有不同复杂性的支架的读写成为可能。将使用长波（近红外）光脉冲和长工作距离物镜，使穿透深度超过先前报道的5倍，从而打印出1mm x 1mm x 0.5 mm的支架体积，这将与生物学相关的体外3D模型具有相同的规模。使用光波前整形技术可以分别实现并行化和减少写入伪影。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。这项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，以及允许基于胶原蛋白的机械超材料的原型制作，为统计学和机器学习领域做出了贡献。该奖项反映了美国国家科学基金会的法定使命，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。

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.