Chemotactic droplet swimmers in complex geometries

Area of Science:

• Soft Matter Physics
• Chemical Engineering
• Microfluidics

Background:

• Chemotaxis and auto-chemotaxis are crucial for microbial nutrient acquisition and communication.
• Studying these phenomena in natural environments is analytically challenging due to complexity.
• Artificial models offer a controlled approach to investigate micro-organism behavior.

Purpose of the Study:

• To develop and utilize a tunable artificial model for studying chemotaxis and autochemotaxis.
• To investigate micro-swimmer behavior in complex microfluidic geometries.
• To analyze the influence of chemical signaling and environmental interactions on collective dynamics.

Main Methods:

• Utilized self-propelling oil droplets in aqueous surfactant solutions as an artificial model.
• Employed microfluidic assays with varying geometries, including mazes and bifurcating channels.
• Developed an analytical Langevin model to match experimental observations.

Main Results:

• Droplets successfully navigated mazes, guided by chemical gradients.
• Anticorrelations in branch choices of consecutive auto-chemotactic droplet swimmers were observed.
• Interactions with pillar arrays demonstrated behaviors dependent on wall curvature and negative autochemotaxis, including attachment, trapping, and detachment.

Conclusions:

• The artificial droplet model effectively replicates complex chemotactic and auto-chemotactic behaviors.
• Microfluidic systems provide a powerful platform for dissecting micro-swimmer dynamics.
• The interplay between self-propulsion, chemical signaling, and geometry dictates emergent collective behaviors.