Accelerated microswimmer maze solving via spatiotemporally patterned stimuli

Understanding how active matter navigates complex and crowded environments is key to biological systems and to the development of future applications for cargo delivery. In this work, we design protocols that leverage spatiotemporally patterned stimuli to achieve the local control of the persistence length of microswimmers and accelerate the traversal of confining geometries of increasing complexity. These include static (spatially varying yet stationary) patterns, as well as dynamic (both spatially- and time-varying) patterns, the latter consisting of "traveling" regions of alternating high and low persistence length. We assess the efficiency of these protocols with nonequilibrium Brownian dynamics simulations and examine the impact of the pattern velocity, pattern width, and persistence contrast. We also interpret the results with a 1D analytical model that captures the key features of the solving process, defined as the traversal of the confining structure from the entrance to the exit. The results demonstrate that traveling persistence landscapes significantly accelerate maze traversal and suggest new strategies to achieve the efficient transport of active and living systems through complex environments.