Widely accessible method for 3D microflow mapping at high spatial and temporal resolutions
Abstract Advances in microfluidic technologies rely on engineered 3D flow patterns to manipulate samples at the microscale. However, current methods for mapping flows only provide limited 3D and temporal resolutions or require highly specialized optical set-ups. Here, we present a simple defocusing...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2022-07-01
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| Series: | Microsystems & Nanoengineering |
| Online Access: | https://doi.org/10.1038/s41378-022-00404-z |
| _version_ | 1811328835861348352 |
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| author | Evan Lammertse Nikhil Koditala Martin Sauzade Hongxiao Li Qiang Li Luc Anis Jun Kong Eric Brouzes |
| author_facet | Evan Lammertse Nikhil Koditala Martin Sauzade Hongxiao Li Qiang Li Luc Anis Jun Kong Eric Brouzes |
| author_sort | Evan Lammertse |
| collection | DOAJ |
| description | Abstract Advances in microfluidic technologies rely on engineered 3D flow patterns to manipulate samples at the microscale. However, current methods for mapping flows only provide limited 3D and temporal resolutions or require highly specialized optical set-ups. Here, we present a simple defocusing approach based on brightfield microscopy and open-source software to map micro-flows in 3D at high spatial and temporal resolution. Our workflow is both integrated in ImageJ and modular. We track seed particles in 2D before classifying their Z-position using a reference library. We compare the performance of a traditional cross-correlation method and a deep learning model in performing the classification step. We validate our method on three highly relevant microfluidic examples: a channel step expansion and displacement structures as single-phase flow examples, and droplet microfluidics as a two-phase flow example. First, we elucidate how displacement structures efficiently shift large particles across streamlines. Second, we reveal novel recirculation structures and folding patterns in the internal flow of microfluidic droplets. Our simple and widely accessible brightfield technique generates high-resolution flow maps and it will address the increasing demand for controlling fluids at the microscale by supporting the efficient design of novel microfluidic structures. |
| first_indexed | 2024-04-13T15:32:03Z |
| format | Article |
| id | doaj.art-4f548f89b30643e4bf0fcb581be5fc25 |
| institution | Directory Open Access Journal |
| issn | 2055-7434 |
| language | English |
| last_indexed | 2024-04-13T15:32:03Z |
| publishDate | 2022-07-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Microsystems & Nanoengineering |
| spelling | doaj.art-4f548f89b30643e4bf0fcb581be5fc252022-12-22T02:41:21ZengNature Publishing GroupMicrosystems & Nanoengineering2055-74342022-07-018111510.1038/s41378-022-00404-zWidely accessible method for 3D microflow mapping at high spatial and temporal resolutionsEvan Lammertse0Nikhil Koditala1Martin Sauzade2Hongxiao Li3Qiang Li4Luc Anis5Jun Kong6Eric Brouzes7Department of Biomedical Engineering, Stony Brook UniversityDepartment of Mathematics and Statistics, Department of Computer Science, Georgia State UniversityDepartment of Biomedical Engineering, Stony Brook UniversityDepartment of Mathematics and Statistics, Department of Computer Science, Georgia State UniversityDepartment of Mathematics and Statistics, Department of Computer Science, Georgia State UniversityDepartment of Biomedical Engineering, Stony Brook UniversityDepartment of Mathematics and Statistics, Department of Computer Science, Georgia State UniversityDepartment of Biomedical Engineering, Stony Brook UniversityAbstract Advances in microfluidic technologies rely on engineered 3D flow patterns to manipulate samples at the microscale. However, current methods for mapping flows only provide limited 3D and temporal resolutions or require highly specialized optical set-ups. Here, we present a simple defocusing approach based on brightfield microscopy and open-source software to map micro-flows in 3D at high spatial and temporal resolution. Our workflow is both integrated in ImageJ and modular. We track seed particles in 2D before classifying their Z-position using a reference library. We compare the performance of a traditional cross-correlation method and a deep learning model in performing the classification step. We validate our method on three highly relevant microfluidic examples: a channel step expansion and displacement structures as single-phase flow examples, and droplet microfluidics as a two-phase flow example. First, we elucidate how displacement structures efficiently shift large particles across streamlines. Second, we reveal novel recirculation structures and folding patterns in the internal flow of microfluidic droplets. Our simple and widely accessible brightfield technique generates high-resolution flow maps and it will address the increasing demand for controlling fluids at the microscale by supporting the efficient design of novel microfluidic structures.https://doi.org/10.1038/s41378-022-00404-z |
| spellingShingle | Evan Lammertse Nikhil Koditala Martin Sauzade Hongxiao Li Qiang Li Luc Anis Jun Kong Eric Brouzes Widely accessible method for 3D microflow mapping at high spatial and temporal resolutions Microsystems & Nanoengineering |
| title | Widely accessible method for 3D microflow mapping at high spatial and temporal resolutions |
| title_full | Widely accessible method for 3D microflow mapping at high spatial and temporal resolutions |
| title_fullStr | Widely accessible method for 3D microflow mapping at high spatial and temporal resolutions |
| title_full_unstemmed | Widely accessible method for 3D microflow mapping at high spatial and temporal resolutions |
| title_short | Widely accessible method for 3D microflow mapping at high spatial and temporal resolutions |
| title_sort | widely accessible method for 3d microflow mapping at high spatial and temporal resolutions |
| url | https://doi.org/10.1038/s41378-022-00404-z |
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