Journal article 248 views 49 downloads
Machine learning enhanced droplet microfluidics
Claire Barnes 
,
Ashish Sonwane,
Eva C. Sonnenschein ,
Francesco Del Giudice
,
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 |
|---|---|
| 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 |
| first_indexed |
2024-08-02T12:01:04Z |
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| last_indexed |
2024-11-25T14:18:27Z |
| id |
cronfa66580 |
| recordtype |
SURis |
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Sonnenschein</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>742d483071479b44d7888e16166b1309</sid><ORCID>0000-0002-9414-6937</ORCID><firstname>Francesco</firstname><surname>Del Giudice</surname><name>Francesco Del Giudice</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2024-06-03</date><deptcode>EAAS</deptcode><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.</abstract><type>Journal Article</type><journal>Physics of Fluids</journal><volume>35</volume><journalNumber>9</journalNumber><paginationStart/><paginationEnd/><publisher>AIP Publishing</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1070-6631</issnPrint><issnElectronic>1089-7666</issnElectronic><keywords>Artificial neural networks, Machine learning, Interfacial tension, Volumetric flow rates, Microdroplets, Microfluidics</keywords><publishedDay>8</publishedDay><publishedMonth>9</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-09-08</publishedDate><doi>10.1063/5.0163806</doi><url/><notes/><college>COLLEGE NANME</college><department>Engineering and Applied Sciences School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>EAAS</DepartmentCode><institution>Swansea University</institution><apcterm/><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.</funders><projectreference/><lastEdited>2024-08-02T13:03:12.5503147</lastEdited><Created>2024-06-03T14:14:54.5185554</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Chemical Engineering</level></path><authors><author><firstname>Claire</firstname><surname>Barnes</surname><orcid>0000-0003-1031-7127</orcid><order>1</order></author><author><firstname>Ashish</firstname><surname>Sonwane</surname><order>2</order></author><author><firstname>Eva C.</firstname><surname>Sonnenschein</surname><orcid>0000-0001-6959-5100</orcid><order>3</order></author><author><firstname>Francesco</firstname><surname>Del Giudice</surname><orcid>0000-0002-9414-6937</orcid><order>4</order></author></authors><documents><document><filename>66580__31037__fda20e06c3d1478587d86cc0886f3d7f.pdf</filename><originalFilename>66580.VoR.pdf</originalFilename><uploaded>2024-08-02T13:01:42.7191463</uploaded><type>Output</type><contentLength>4481652</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Copyright: 2023 Author(s). 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| spelling |
2024-08-02T13:03:12.5503147 v2 66580 2024-06-03 Machine learning enhanced droplet microfluidics 024232879fc13d5ceac584360af8742c 0000-0003-1031-7127 Claire Barnes Claire Barnes true false efdcccffe63b3d9e7a70a37c8db1ca14 Ashish Sonwane Ashish Sonwane true false f6a4027578a15ea3e6453a54b849c686 0000-0001-6959-5100 Eva C. Sonnenschein Eva C. Sonnenschein true false 742d483071479b44d7888e16166b1309 0000-0002-9414-6937 Francesco Del Giudice Francesco Del Giudice true false 2024-06-03 EAAS 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. Journal Article Physics of Fluids 35 9 AIP Publishing 1070-6631 1089-7666 Artificial neural networks, Machine learning, Interfacial tension, Volumetric flow rates, Microdroplets, Microfluidics 8 9 2023 2023-09-08 10.1063/5.0163806 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University 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. 2024-08-02T13:03:12.5503147 2024-06-03T14:14:54.5185554 Faculty of Science and Engineering School of Engineering and Applied Sciences - Chemical Engineering Claire Barnes 0000-0003-1031-7127 1 Ashish Sonwane 2 Eva C. Sonnenschein 0000-0001-6959-5100 3 Francesco Del Giudice 0000-0002-9414-6937 4 66580__31037__fda20e06c3d1478587d86cc0886f3d7f.pdf 66580.VoR.pdf 2024-08-02T13:01:42.7191463 Output 4481652 application/pdf Version of Record true Copyright: 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license. true eng http://creativecommons.org/licenses/by/4.0/ |
| title |
Machine learning enhanced droplet microfluidics |
| spellingShingle |
Machine learning enhanced droplet microfluidics Claire Barnes Ashish Sonwane Eva C. Sonnenschein Francesco Del Giudice |
| title_short |
Machine learning enhanced droplet microfluidics |
| title_full |
Machine learning enhanced droplet microfluidics |
| title_fullStr |
Machine learning enhanced droplet microfluidics |
| title_full_unstemmed |
Machine learning enhanced droplet microfluidics |
| title_sort |
Machine learning enhanced droplet microfluidics |
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024232879fc13d5ceac584360af8742c efdcccffe63b3d9e7a70a37c8db1ca14 f6a4027578a15ea3e6453a54b849c686 742d483071479b44d7888e16166b1309 |
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024232879fc13d5ceac584360af8742c_***_Claire Barnes efdcccffe63b3d9e7a70a37c8db1ca14_***_Ashish Sonwane f6a4027578a15ea3e6453a54b849c686_***_Eva C. Sonnenschein 742d483071479b44d7888e16166b1309_***_Francesco Del Giudice |
| author |
Claire Barnes Ashish Sonwane Eva C. Sonnenschein Francesco Del Giudice |
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Claire Barnes Ashish Sonwane Eva C. Sonnenschein Francesco Del Giudice |
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Physics of Fluids |
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1070-6631 1089-7666 |
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10.1063/5.0163806 |
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AIP Publishing |
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Faculty of Science and Engineering |
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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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2023-09-08T08:17:43Z |
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11.0583515 |