JFM cover features Gazzola’s biobot work

10/17/2019 Amanda Maher

Written by Amanda Maher

Cover art from Mattia Gazzola's research group.
Cover art from Mattia Gazzola's research group.
Tiny robots that can be introduced into the blood stream and administer medications seem like something of science fiction, but two graduate students, Tejaswin Parthasarathy and Fan Kiat Chan, guided by Assistant Professor Mattia Gazzola, are taking steps toward making this a reality.

These drug-delivery robots, together with the prescribed medicine, will be immersed in fluid environments that are uncertain and can cause the medicine to separate from its delivery bot. Gazzola’s team seeks to potentially take advantage of fluid effects arising from the operation of these bots to help drug delivery, which led to a computational study on the effects of viscous streaming mechanisms. 

Their work was recently featured on the cover of the latest issue of the Journal of Fluid Mechanics. The image depicts the cross sections of two robots with different designs and the corresponding streamlines around them during operation. The image compares time-averaged streamlines generated by an oscillating circular bot (top half) and an oscillating bullet-shaped bot (bottom half). Fundamental fluid dynamics communicates a lot about the local velocity of the flow field from the streamline densities, and relative to the image, the more densely compressed streamlines in the posterior of the bullet-shaped bot presents an opportunity for particle entrainment and ultimately transport. By studying the different effects of body geometry (i.e. curvatures) on viscous streaming flows, Gazzola’s team is playing an important role in the development of a biocompatible robot.

Gazzola’s research lab is multidisciplinary, focusing on topics ranging from fluids to soft mechanics as well as controls of the robots. The group collaborates with experimentalists who grow cells to run the experiments for which the computational models predict the behavior. They also use deep reinforcement learning to reveal how mechanical and biological systems behave over time and to understand how to create devices that adapt to their environment.

(Click image to activate gif.) This gif demonstrates the trajectory that a particle placed in the posterior of the streaming robot can be transported with and without viscous streaming effects that arise from the body oscillations. When the bot (larger grey body) is translating linearly (without transverse oscillations and hence without viscous streaming effects), the trailing particle (smaller grey body) lags behind significantly in both cases—though the circular bot manages to bring the particle further. However, when oscillations are introduced (as indicated by the double-headed arrows on the robot body), the particle (now marked in blue) follows closer to the bot, with the bullet design performing much better, thanks to favorable fluid effects from viscous streaming. These effects result in the higher-velocity uptake in the posterior of the body, consistent with the more compressed streamlines observed in the streamline comparison image.
(Click image to activate gif.) This gif demonstrates the trajectory that a particle placed in the posterior of the streaming robot can be transported with and without viscous streaming effects that arise from the body oscillations. When the bot (larger grey body) is translating linearly (without transverse oscillations and hence without viscous streaming effects), the trailing particle (smaller grey body) lags behind significantly in both cases—though the circular bot manages to bring the particle further. However, when oscillations are introduced (as indicated by the double-headed arrows on the robot body), the particle (now marked in blue) follows closer to the bot, with the bullet design performing much better, thanks to favorable fluid effects from viscous streaming. These effects result in the higher-velocity uptake in the posterior of the body, consistent with the more compressed streamlines observed in the streamline comparison image.

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This story was published October 17, 2019.