Artificial Intelligence-Empowered Automated Double Emulsion Droplet Library Generation

Abstract

Double emulsions with core-shell structures are versatile materials used in applications such as cell culture, drug delivery, and materials synthesis. A droplet library with precisely controlled dimensions and properties would streamline screening and optimization for specific applications. While microfluidic droplet generation offers high precision, it is typically labor-intensive and sensitive to disturbances, requiring continuous operator intervention. To address these limitations, we present an artificial intelligence (AI)-empowered automated double emulsion droplet library generator. This system integrates a convolutional neural network (CNN)-based object detection model, decision-making, and feedback control algorithms to automate droplet generation and collection. The system monitors droplet generation every 171 ms—faster than a Formula 1 driver's reaction time—ensuring rapid response to disturbances and consistent production of single-core double emulsions. It autonomously generates libraries of 25 distinct monodisperse droplets with user-defined properties. This automation reduces labor and waste, enhances precision, and supports rapid and reliable droplet library generation. We anticipate that this platform will accelerate discovery and optimization in biomedical, biological, and materials research.