Chemotaxis and auto-chemotaxis of self-propelling artificial droplet swimmers

TL;DR

This paper demonstrates that oil droplet swimmers in surfactant solutions can exhibit chemotaxis and auto-chemotaxis, guiding their movement through microfluidic devices and avoiding trails, modeled by diffusion and stochastic dynamics.

Contribution

It introduces a self-propelling droplet system showing chemotaxis and negative auto-chemotaxis, with quantitative analysis and modeling of trail avoidance mechanisms.

Findings

Swimmers follow micellar surfactant gradients in microfluidic mazes.

Droplet trails cause negative autochemotaxis, leading to trail avoidance.

Modeling aligns with micellar diffusion and gradient forces.

Abstract

Chemotaxis and auto-chemotaxis play an important role in many essential biological processes. We present a self-propelling artificial swimmer system which exhibits chemotaxis as well as negative auto-chemotaxis. Oil droplets in an aqueous surfactant solution are driven by interfacial Marangoni flows induced by micellar solubilization of the oil phase. We demonstrate that chemotaxis along micellar surfactant gradients can guide these swimmers through a microfluidic maze. Similarly, a depletion of empty micelles in the wake of a droplet swimmer causes negative autochemotaxis and thereby trail avoidance. We have studied autochemotaxis quantitatively in a microfluidic device of bifurcating channels: Branch choices of consecutive swimmers are anticorrelated, an effect decaying over time due to trail dispersion. We have modeled this process by a simple one-dimensional diffusion process and stochastic Langevin dynamics. Our results are consistent with a linear surfactant gradient force and diffusion constants appropriate for micellar diffusion, and provide a measure of autochemotactic feedback strength versus stochastic forces.