Simulations and Control of Artificial Microswimmers in Blood

Artificial microswimmers are micron-sized devices that can propel in viscous fluids. Their potential applications are numerous, including targeted drug delivery, imaging, microsurgery, micro-sensing, assisted fertilization and micro-manipulation. To achieve these tasks, artificial microswimmers must reach regions of interest in a non-intrusive way by navigating through the complex blood circulatory system. Despite recent advances, reliable remote control of microswimmers in-vivo remains challenging because of the strong blood flows, the presence of blood cells, biocompatibility, noisy feedback and limited propelling velocities. A promising design of microswimmers, called artificial bacterial flagella (ABFs), are helical micro-robots that are propelled via external rotating magnetic fields. The swimming properties of ABFs in blood remain largely unexplored and no numerical model has been proposed to simulate such configurations. In this thesis, we study the control mechanisms and swimming properties of ABFs in blood flows, requiring two main components, red blood cells (RBCs) and ABFs, interacting hydrodynamically through the blood plasma. First, the RBC model is calibrated through hierarchical Bayesian inference on single-cell experiments, revealing the oblate stress-free state shape of the membrane cytoskeleton. We show that the calibrated model is transferable to more complex situations. Second, in the simplified case of ABFs swimming in free space, we derive control mechanisms and optimal path planning strategies to stir independently multiple ABFs with uniform magnetic fields. The method relies on reinforcement learning and is robust to thermal noise and background flow perturbations. We then consider ABFs swimming through suspensions of RBCs, revealing that ABFs swim faster in blood than in pure solvent. Finally, we simulate swarms of ABFs in confined geometries, showing interesting collective behavior that can be tuned by changing the external magnetic field.

Grantor dc:publisher

ETH Zurich

Year dc:date

2022

Author dc:creator

• Amoudruz, Lucas

Contributors dc:contributor

• Koumoutsakos, Petros
• Schürle-Finke, Simone
• Arampatzis, Georgios

• info:eu-repo/classification/ddc/570
• info:eu-repo/classification/ddc/004
• Life sciences
• Data processing, computer science

Statement dc:rights

• info:eu-repo/semantics/openAccess
• Creative Commons Attribution 4.0 International

Licence

• http://creativecommons.org/licenses/by/4.0/

Language dc:language

en

Identifier

https://doi.org/10.3929/ethz-b-000550202

OAI identifier oai:identifier

oai:www.research-collection.ethz.ch:20.500.11850/550202