Our team works to design hydrogels that mimic biological tissues. These tissue mimics seek to copy the mechanical and chemical properties of living tissues while also recapitulating their architecture. While these materials are often prepared by hand, additive manufacturing is becoming an increasingly important way of fabricating synthetic tissues and biomaterials from fluid bioinks. Bioprinters typically perfuse bioinks through simple nozzles (usually just a needle) to deposit materials for printing. By substituting these needles with intentionally-engineered passive microfluidic mixing nozzles, bioprinters can be designed to produce increasingly complex materials with property gradients that more accurately mimic the properties of biological tissues.
Fluid mixing at the microscale often operates in suppressed Reynolds number regimes. In these flows, viscous forces dominate, thus attenuating advection and resulting in slow, inefficient, diffusion-limited mixing. Achieving efficient microfluidic mixing through passive approaches requires designs that circumvent this limitation by leveraging geometry, operating conditions, and fluid properties to achieve enhanced mixing performance. Our goal is to mix materials across a range of properties to form biomaterials that mimic the properties of biological tissues. To accomplish this work, you will learn and use computational fluid dynamics (CFD) by using ANSYS Fluent on the NSF’s Extreme Science and Engineering Discovery Environment (XSEDE). Through this work, you will join a team that will evaluate and design microfluidic mixers with performance optimized for bioink material properties. This work extends work initiated by prior Mudders with the possibility for collaboration with students external to HMC.
Check out our recent work: https://ui.adsabs.harvard.edu/abs/2020APS..DFDU10004P/abstract
As a member of this group, you would work on projects that collectively build knowledge at the intersection of fluid mechanics, bioengineering, and material engineering. Together, our work seeks to build tools and materials for evaluating biological function in benchtop models to uncover biological function.