Next-Gen Microfluidics: Reinforcement Learning Driven Droplet Size Precision System

This study introduces a novel device with control, prediction and characterization feature designed for precise droplet size control using AI algorithms. The device employs a pressure by-pass valve to regulate the droplet size, ensuring droplet generation of the desired size. The system also employs an IOT-based droplet size training and prediction system, which predicts droplet sizes and builds a dataset based on the database uploaded by users. The device has a RaspberryPi-5 for AI code execution and image processing, an Arduino mini for in-built syringe pump control, and a digital microscope lens for droplet observation. The prediction system in the device helps the users to ascertain which droplet sizes can be achieved under particular microfluidic channel sizes, flow, viscosity and temperature conditions. This feature also allows to characterize biochemical processes which lead to abnormal drop size change. The device operates iteratively, running RL algorithms to achieve a user-defined target droplet size. After set iterations, the device prompts the user for permission to increase the iteration count or alter flow conditions as per the prediction algorithm. The effectiveness and versatility of the device in achieving precise droplet size is evaluated using experiments conducted under various experimental conditions. The device will contribute to the advancement of microfluidic droplet development technologies for various applications, including drug delivery, diagnostics, and chemical analysis.