Autonomous control of droplet generator for single and double droplets using Bayesian optimization

Droplet microfluidics is widely used in diverse applications, including functional particle fabrication, and biological assay. To achieve the desired result in application of droplet microfluidics, it is necessary to set the optimal flow rate. However, the optimal flow rate depends on multiple variables such as viscosity, channel dimension. Identifying optimal flow rate considering above factors is time-consuming and labor-intensive process. Previous studies have employed scaling laws or machine learning in an attempt to identify an optimal flow rate. However, these methods require a lot of experiment results. To overcome these limitations, we developed an autonomous control system which can control droplet generators for single and double droplets using Bayesian optimization. This system does not require huge training dataset and is applicable to droplet generating with various channel geometries and working fluids. Furthermore, we confirmed that it is applicable to not only single droplet generating but also double droplet generating. We believe these results can enhance accessibility of droplet microfluidics.