Self-avoidant memory effects on enhanced diffusion in a stochastic model of environmentally responsive swimming...

Area of Science:

• Physics
• Chemical Engineering
• Materials Science

Background:

• Enhanced diffusion is common in microswimmer systems, driven by ballistic motion and reorientations.
• Autophoretic droplet swimmers exhibit self-avoidance or attraction via chemical gradients.
• Understanding these interactions is key to controlling microswimmer behavior.

Purpose of the Study:

• To model and numerically study the impact of self-avoidant memory on enhanced diffusion in swimming droplets.
• To differentiate the effects of self-avoidance from random reorientations.
• To investigate novel mechanisms influencing microswimmer collective dynamics.

Main Methods:

• Developed a mathematical model incorporating experimentally observed self-avoidant memory.
• Utilized numerical simulations to analyze droplet behavior.
• Compared results to the active Brownian model to isolate memory effects.

Main Results:

• Self-avoidant memory significantly suppresses enhanced diffusion compared to models with only reorientation persistence.
• Introduced the concept of 'self-caging' as a novel mechanism for this suppression.
• Identified a parameter domain where enhanced diffusion is observable despite memory effects.

Conclusions:

• Self-avoidant memory in microswimmers can lead to counterintuitive reductions in diffusion.
• Self-caging is a critical factor in understanding the dynamics of interacting swimming droplets.
• The findings provide a framework for designing and controlling microswimmer systems with specific collective behaviors.