Journal article 110 views 24 downloads
Machine learning enhanced droplet microfluidics
,
Ashish Sonwane,
Eva C. Sonnenschein ,
Francesco Del Giudice
Physics of Fluids, Volume: 35, Issue: 9
Swansea University Authors:
Claire Barnes , Ashish Sonwane, Eva C. Sonnenschein , Francesco Del Giudice
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DOI (Published version): 10.1063/5.0163806
Abstract
Machine learning has recently been introduced in the context of droplet microfluidics to simplify the process of droplet formation, which is usually controlled by a variety of parameters. However, the studies introduced so far have mainly focused on droplet size control using water and mineral oil i...
| Published in: | Physics of Fluids |
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| ISSN: | 1070-6631 1089-7666 |
| Published: |
AIP Publishing
2023
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa66580 |
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| Abstract: |
Machine learning has recently been introduced in the context of droplet microfluidics to simplify the process of droplet formation, which is usually controlled by a variety of parameters. However, the studies introduced so far have mainly focused on droplet size control using water and mineral oil in microfluidic devices fabricated using soft lithography or rapid prototyping. This approach negated the applicability of machine learning results to other types of fluids more relevant to biomedical applications, while also preventing users that do not have access to microfluidic fabrication facilities to take advantage of previous findings. There are a number of different algorithms that could be used as part of a data driven approach, and no clear comparison has been previously offered among multiple machine learning architectures with respect to the predictions of flow rate values and generation rate. We here employed machine learning to predict the experimental parameters required for droplet generation in three commercialized microfluidic flow-focusing devices using phosphate buffer saline and biocompatible fluorinated oil as dispersed and continuous liquid phases, respectively. We compared three different machine learning architectures and established the one leading to more accurate predictions. We also compared the predictions with a new set of experiments performed at a different day to account for experimental variability. Finally, we provided a proof of concept related to algae encapsulation and designed a simple app that can be used to generate accurate predictions for a given droplet size and generation rate across the three commercial devices. |
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| Keywords: |
Artificial neural networks, Machine learning, Interfacial tension, Volumetric flow rates, Microdroplets, Microfluidics |
| College: |
Faculty of Science and Engineering |
| Funders: |
F.D.G. acknowledges support from the EPSRC New Investigator Award (Grant No. EP/S036490/1) and from the Royal Society Research Grant (Grant No. RGS/R1/221263). F.D.G also acknowledges support via the Ser Cymru programme– Enhancing Competitiveness Equipment Awards 2022–2023 (Grant No. MA/ VG/2715/22-PN47). This research contributes to the IMPACT operation, which has been part-funded by the European Regional Development Fund through the Welsh Government and Swansea University. We are grateful to Dolomite Microfluidics for providing the three microfluidic devices and the FluoSurf continuous phase free of charge. |
| Issue: |
9 |