Novel Inertial Self-Assembly Dynamics and Long-Range Spatial Ordering of Interacting Droplet Ensembles in Confined Microfluidic Flows

Abstract

The multiphase flow of droplets is widespread, both at the industrial and the microscale, for both biological and non-biological applications alike. But the ensemble interactions of such systems are inherently nonlinear and complex, compounded by interfacial effects, making it a difficult many-body problem for theory. In comparison, the self-assembly dynamics of solid particles in flow have long been described and successfully exploited in the field of inertial microfluidics, where particle crystals can be realized from inertial forces and hydrodynamic interactions. Here, we report novel self-assembly dynamics of liquid drops in confined microfluidic channels that contrast starkly with the established paradigm of inertial microfluidics: higher inertia leads to better spatial ordering. Instead, we find that the conventional straight wall channel geometry not only fails to achieve regular spatial ordering for drops but actually exacerbates it with increasing inertia. Conversely, an asymmetric serpentine geometry is able to achieve long-range, periodic spatial ordering over length scales that are at least 3 orders of magnitude greater than the drop diameter, particularly at low inertia. Experimentally, we are able to decouple droplet generation from ordering, enabling independent variation of number density, confinement, inertia, and surfactant concentration. We find the inertia-dependent emergence of preferred drop separations and show for the first time that Marangoni effects can influence the longitudinal ordering of multidrop arrays. These results present a largely unexplored direction for inertial microfluidics but also show the potential of its unification with droplet microfluidics. In particular, the utility of passively restoring uniform drop spacing on-chip is a key requirement for the streamlined integration of incubation and drop-by-drop interrogation capabilities.