4.6 Article

Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images

期刊

SENSORS
卷 22, 期 18, 页码 -

出版社

MDPI
DOI: 10.3390/s22186900

关键词

active droplet generation; droplet microfluidics; performance analysis; computer vision; image processing; lab on a chip

资金

  1. Accelerating Higher Education Expansion and Development (AHEAD)-Development Oriented Research (DOR) grant of the Centre for Advanced Mechatronic Systems (CFAMS), University of Moratuwa, Sri Lanka [6026-LK/8743-LK]

向作者/读者索取更多资源

This paper discusses an active droplet generation system that successfully generates droplets using two fluid phases. The system is able to control, sense, and analyse the droplet generation, and the droplet morphology is analysed using vision sensing and digital image processing. The proposed system includes a droplet generator, camera module with image pre-processing and identification algorithm, and a controller and control algorithm with a workstation computer. The average droplet diameter varies with the droplet generation time in a second-order polynomial relationship.
This paper discusses an active droplet generation system, and the presented droplet generator successfully performs droplet generation using two fluid phases: continuous phase fluid and dispersed phase fluid. The performance of an active droplet generation system is analysed based on the droplet morphology using vision sensing and digital image processing. The proposed system in the study includes a droplet generator, camera module with image pre-processing and identification algorithm, and controller and control algorithm with a workstation computer. The overall system is able to control, sense, and analyse the generation of droplets. The main controller consists of a microcontroller, motor controller, voltage regulator, and power supply. Among the morphological features of droplets, the diameter is extracted from the images to observe the system performance. The MATLAB-based image processing algorithm consists of image acquisition, image enhancement, droplet identification, feature extraction, and analysis. RGB band filtering, thresholding, and opening are used in image pre-processing. After the image enhancement, droplet identification is performed by tracing the boundary of the droplets. The average droplet diameter varied from similar to 3.05 mm to similar to 4.04 mm in the experiments, and the average droplet diameter decrement presented a relationship of a second-order polynomial with the droplet generation time.

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