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.
