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Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images
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 mor...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503175/ https://www.ncbi.nlm.nih.gov/pubmed/36146247 http://dx.doi.org/10.3390/s22186900 |
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author | Mudugamuwa, Amith Hettiarachchi, Samith Melroy, Gehan Dodampegama, Shanuka Konara, Menaka Roshan, Uditha Amarasinghe, Ranjith Jayathilaka, Dumith Wang, Peihong |
author_facet | Mudugamuwa, Amith Hettiarachchi, Samith Melroy, Gehan Dodampegama, Shanuka Konara, Menaka Roshan, Uditha Amarasinghe, Ranjith Jayathilaka, Dumith Wang, Peihong |
author_sort | Mudugamuwa, Amith |
collection | PubMed |
description | 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 ~3.05 mm 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. |
format | Online Article Text |
id | pubmed-9503175 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95031752022-09-24 Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images Mudugamuwa, Amith Hettiarachchi, Samith Melroy, Gehan Dodampegama, Shanuka Konara, Menaka Roshan, Uditha Amarasinghe, Ranjith Jayathilaka, Dumith Wang, Peihong Sensors (Basel) Article 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 ~3.05 mm 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. MDPI 2022-09-13 /pmc/articles/PMC9503175/ /pubmed/36146247 http://dx.doi.org/10.3390/s22186900 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mudugamuwa, Amith Hettiarachchi, Samith Melroy, Gehan Dodampegama, Shanuka Konara, Menaka Roshan, Uditha Amarasinghe, Ranjith Jayathilaka, Dumith Wang, Peihong Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images |
title | Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images |
title_full | Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images |
title_fullStr | Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images |
title_full_unstemmed | Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images |
title_short | Vision-Based Performance Analysis of an Active Microfluidic Droplet Generation System Using Droplet Images |
title_sort | vision-based performance analysis of an active microfluidic droplet generation system using droplet images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9503175/ https://www.ncbi.nlm.nih.gov/pubmed/36146247 http://dx.doi.org/10.3390/s22186900 |
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