Self-propulsion of chemically active swimmers has been widely studied owing to their promising potential in biomedical and bioengineering applications. However, most research overlooks the influence of their shape and the non-Newtonian rheology of ambient fluids on swimming dynamics. Inspired by experimentally observed prolate composite droplets and elliptical camphor disks, we theoretically and numerically explore the self-diffusiophoretic propulsion of phoretic particles. The underlying mechanisms behind this propulsion are revealed. We then shift our attention to the manipulation of these micro-swimmers. By exploiting a framework that combines deep reinforcement learning with a CFD solver, we identify a globally optimal predatory strategy.
 

Self-propulsion,Active drop,Janus particle,RL,Linear stability analysis