The effect of microfluidic chip geometry on droplet clustering in a high throughput droplet incubation platform for single-cell analysis

Abstract

High-throughput droplet incubation is an integral part of various lab-on-chip platforms. Packing droplets efficiently before sending them to the incubation region is essential. For this purpose, different oil extraction mechanisms have been used. Here, we propose one such oil extraction structure and study the effect of channel parameters on droplet clustering, especially the channel height relative to droplet size. Droplets relatively smaller than channel dimensions follow continuous flow dynamics. Hence, simple hydrodynamic resistance modeling flow inside the main and side channels could estimate their flow features. When the droplet diameter exceeds the channel height, its dynamics are observed to be deviating drastically from simple isolated droplet motion and hydrodynamic resistance network model. Though accurate, Eulerian–Lagrangian formulation for modeling two-component fluid flow becomes computationally expensive when dealing with many droplets. Hence, along with hydrodynamics modeling, we adopt a simplified hindrance model to incorporate the effects of modified drag force and emulsion viscosity. This model matches well with experimental results and reduces the design time. Finally, we built an efficient oil extraction and droplet incubation platform for high throughput (200–400 droplets/s) for incubating a large number of cell-encapsulated droplets (7 × 105–8.4 × 105) for a significant amount of time (30–70 min). This study offers insights into droplet dynamics in the oil extraction region, which can be further helpful in building an efficient lab-on-chip platform.