Decision-making at a T-junction by gradient-sensing microscopic agents

Abstract

Active navigation relies on effectively extracting information from the surrounding environment, and often features the tracking of gradients of a relevant signal-such as the concentration of molecules. Microfluidic networks of closed pathways pose the challenge of determining the shortest exit pathway, which involves the proper local decision-making at each bifurcating junction. Here, we focus on the basic decision faced at a T-junction by a microscopic particle, which orients among possible paths via its sensing of a diffusible substance's concentration. We study experimentally the navigation of colloidal particles following concentration gradients by diffusiophoresis. We treat the situation as a mean first passage time (MFPT) problem that unveils the important role of a separatrix in the concentration field to determine the statistics of path taking. Further, we use numerical experiments to study different strategies, including biomimetic ones such as run and tumble or Markovian chemotactic migration. The discontinuity in the MFPT at the junction makes it remarkably difficult for microscopic agents to follow the shortest path, irrespective of adopted navigation strategy. In contrast, increasing the size of the sensing agents improves the efficiency of short-path taking by harvesting information on a larger scale. It inspires the development of a run-and-whirl dynamics that takes advantage of the mathematical properties of harmonic functions to emulate particles beyond their own size.

Diffusioosmosis-driven dispersion of colloids: a Taylor dispersion analysis with experimental validation

“…An accurate calculation of dispersion in the presence of charged sidewalls can be exploited for various applications. For instance, lab-on-a-chip applications such as directed delivery of particles (Banerjee et al 2016;Shi et al 2016;Lee et al 2018;Gandhi et al 2020;Seo et al 2020;Shin 2020) and zeta-potential measurement (Shin et al 2017) rely on accurate prediction of particle concentration, which in turn is closely associated with colloidal dispersion. In physical systems such as energy storage and desalination devices (Biesheuvel & Bazant 2010;Florea et al 2014;Bone et al 2020;Gupta et al 2020a,c;Henrique, Zuk & Gupta 2022) it is common to observe ion concentration gradients inside charged pores, where one can expect colloidal transport and dispersion to be important.…”

“…2016; Gandhi et al. 2020), ion-exchange membranes (Florea et al. 2014), zeta potential measurement (Shin et al.…”

“…To enable widespread use of diffusiophoresis in lab-on-a-chip applications such as energy storage and desalination devices (Bone, Steinrück & Toney 2020;Gupta et al 2020a;Gupta, Zuk & Stone 2020c), particle separation (Lee et al 2018;Seo et al 2020;Shin 2020), colloidal focusing and delivery (Banerjee et al 2016;Shi et al 2016;Gandhi et al 2020), ion-exchange membranes (Florea et al 2014), zeta potential measurement (Shin et al 2017) and macromolecule transport in biological systems (Bruno et al 2018;Hartman, Božič & Derganc 2018;Yang et al 2018), understanding and quantifying dispersion is crucial. Recently, studies have modelled the dispersion of a patch of particles that spread due to diffusiophoresis (Raynal & Volk 2019;Chu et al 2020Chu et al , 2021Gupta et al 2020b;Migacz & Ault 2022).…”

Diffusiophoresis, Diffusioosmosis, and Microfluidics: Surface-Flow-Driven Phenomena in the Presence of Flow

“…Theoretically, colloids with a nonzero surface potential put under concentration gradients of electrolytes can obtain a diffusiophoretic mobility. Polystyrene (PS) and silica microspheres were popular and appeared in most experimental studies that aimed for both fundamental understanding and applications ,,,,,− ,− ,− ,,,,,− (Figure ). Polystyrene microspheres have a well-defined surface potential (it is still a function of surface charge density) and sizes.…”

Characterization of micro-capsules deformation in branching channels

“…Such phenomena need to be accurately accounted for describing the nonlinear behavior of microfluidic devices, such as those used in particle sorting lab-on-chip [2,3]. For example, for moderate Reynolds numbers, the path selected during the journey can be predicted for simple channel geometries as a function of the particle releasing position and inertia [4][5][6].…”