Development of Robust On-Demand Droplet Generation System Using 3-D Image Reconstruction as Feedback

Abstract

Droplet-based microfluidics is an emerging interdisciplinary field with broad applications. On-demand droplet generation with precise size allows performing sensitive detection and quantitative analysis with high throughput and separate units, while the essential precondition is characterizing the volume of droplet accurately. Generally, fluorescence or bright-field imaging can be adopted to provide the length or width of the droplet from 2-D images, and then derive an idealized model to approximately estimate the droplet volume, which would always lead to steady-state errors with ignoring the irregular curvature of droplet surface in the depth direction. In this work, we propose the usage of real-time quantitative phase imaging (QPI) technique for the 3-D detection of flowing droplets to provide a precise measurement of droplet volume which can serve as feedback signal. Comparative experiments with a passive droplet generation system confirm that our proposed method can offer higher accuracy and monodispersity of on-demand droplet generation, together with enhanced stability and anti-interference to resist internal or external perturbations, which could promise a predictable outcome in many microfluidics-based systems.