Shao wins new NSF project furthering biological tissue research


MechSE assistant professor Chenhui Shao is principal investigator for a new digital manufacturing project aiming to push forward research involving biological tissue research.

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Chenhui Shao
MechSE Associate Professor Chenhui Shao

MechSE assistant professor Chenhui Shao is principal investigator for a new National Science Foundation-sponsored digital manufacturing project aiming to push forward research involving biological tissue research.

The project is titled, “Collaborative Research: A Digital Manufacturing Platform to Democratize Biological Tissue Access Using Smart Two-Photon Polymerization.” The funding comes from the NSF’s Division of Civil, Mechanical and Manufacturing Innovation (CMMI).

“Recapitulating tissue architecture is important for many fundamental studies and areas, but universal access to human and animal tissues is extremely limited,” Shao said. “To mitigate this issue, we will create a digital manufacturing platform for cloud-based reading and writing of scaffolds using a convergence research strategy.”

Shao is joined on the project by Brown University professors Kimani Toussaint and Michelle Dawson.

“The project team will bring together expertise from optical physics, biomanufacturing, nano/micromanufacturing, data science, etc.,” Shao said. “We will seek to democratize access to tissue information by broadly disseminating the new techniques, algorithms, and data through academic and industry channels. These activities could be especially transformative in their potential impact on colleges and universities that are deeply underrepresented in the landscape of tissue research.”

Shao will enrich MechSE’s ME 453 Data Science in Manufacturing Quality Control course with educational materials derived from this research. He said that various other education and outreach activities are also being planned to enable unprecedented educational opportunities for a diverse group of students.

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 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.

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This story was published March 2, 2021.