Online Microfluidic Droplets Characterization Using Microscope Data Intelligent Analysis

Abstract

Microfluidic devices have opened new opportunities for functional material chemical synthesis in a few applications. The screening of microfluidic synthesis processes is an urgent task of the experimental process in terms of automation and intellectualization. This study proposes a methodology and software for extracting the morphological and dynamic characteristics of generated monodisperse droplets from video data streams obtained from a digital microscope. For this purpose, the paper considers an approach to generating an extended feature space characterizing the process of droplet generation using a microfluidic device based on the creation of synthetic image datasets. YOLOv7 was used as an algorithm for detecting objects in the images. When training this algorithm, the values in the test dataset mAP@0.5 0.996 were obtained. The algorithms proposed for image processing and analysis implement the basic functionality to extract the morphological and dynamic characteristics of monodisperse droplets in the synthesis process. Laboratory validation and verification of the software demonstrated high results of the identification of key characteristics of the monodisperse droplets generated by the microfluidic device with the average deviation from the real values not exceeding 8%.