Quasi-two-dimensional bacterial swimming around pillars: enhanced trapping efficiency and curvature dependence

Abstract

Microswimmers exhibit more diverse behavior in quasi-two dimensions than in three dimensions. Such behavior remains elusive due to the analytical difficulty of dealing with two parallel solid boundaries. The existence of additional obstacles in quasi-two-dimensional systems further complicates the analysis. We fabricated microscopic pillars in quasi-two dimensions by etching glass coverslips and observed the interactions between swimming bacteria and the pillars. Bacteria got trapped around the circular pillars and the trapping efficiency increased as the quasi-two-dimensionality was increased or as the curvature of the pillars was decreased. Experiments with elliptic pillars demonstrated the possibility that the local curvature of the obstacle controls the trapping efficiency.

A simple catch: thermal fluctuations enable hydrodynamic trapping of microrollers by obstacles

“…The trapping of active particles has been studied in experiments [13][14][15][16]21] and simulations [12,54], for bacteria [16,20] and spherical [12-14, 21, 54] and rodshaped [12,13,15] artificial microswimmers. The hydrodynamic trapping as reported in these studies is manifested in the orbit of the swimmers around round obstacles and along ridges above a critical relative curvature.…”

“…By manipulating the geometry of these obstacles, one can gain control over both scattering and trapping. For example, by using pillars of various sizes, approaching bacteria could be scattered at a particular angle [18], or for larger pillars, trapped in an orbit [16,20]. Similar trapping has been observed in artificial swimmers [13,14,21], and by using more complex geometries, more exotic behaviors, such as directional trapping can be achieved [15].…”

“…Thus, in order to learn how to manipulate and guide these microswimmers through more realistic environments, where they will encounter non-trivial geometries, we must develop a framework to understand how these structured environments modify the transport and propulsion of these particles. As a first step to build this understanding, it is impor-tant to study a model system: the interaction of a single swimmer with an obstacle [7,[12][13][14][15][16][17][18][19][20].…”

Steering self-organisation through confinement

“…using machine learning approaches. 77 It can also be extremely challenging to dynamically probe the emerging self-organising structures from the outside: for example, due to partial or total opacity of the boundaries, real-time imaging with light microscopy can be problematic in vivo, and the confinement itself can become a barrier to extract information; 78 in colloidal systems, interactions, while wellunderstood and measurable in bulk, are strongly affected by and less characterised at interfaces, e.g. liquid interfaces; 36 in vivo measurements can also be particularly difficult as the techniques used to probe the system can quickly become invasive enough to alter it (e.g.…”

A simple catch: Fluctuations enable hydrodynamic trapping of microrollers by obstacles

“…Thus, to learn how to manipulate and guide these microswimmers through more realistic environments, where they will encounter nontrivial geometries, we must develop a framework to understand how these structured environments modify the transport and propulsion of these particles. As a first step to build this understanding, it is important to study a model system: the interaction of a single swimmer with an obstacle (7,(12)(13)(14)(15)(16)(17)(18)(19)(20).…”