[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290186-105":59,"doc-detail-290186-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","machine-learning-enhanced-droplet-microfluidics-research-article","Machine learning enhanced droplet microfluidics - Research Article","","Machine learning is applied to droplet microfluidics to streamline droplet formation, traditionally governed by many parameters. Prior work emphasized droplet-size control in devices using water and mineral oil, limiting transferability to biomedically relevant fluids and restricting access for users without fabrication facilities. This study predicts droplet generation parameters using phosphate buffer saline and fluorinated oil across three commercial flow-focusing devices, compares three learning architectures, validates variability with new experiments, and provides a proof of concept plus a simple prediction app.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-enhanced-droplet-microfluidics-research-article/290186/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-enhanced-droplet-microfluidics-research-article/290186.png","ImageObject",300,407,{"name":92,"@type":93},"Himbo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why has machine learning been introduced to droplet microfluidics?","Question",{"text":113,"@type":114},"It helps identify patterns in complex droplet-formation data and rapidly deploy predictive models for droplet generation parameters.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What fluids and devices are used in this study?",{"text":118,"@type":114},"Phosphate buffer saline and biocompatible fluorinated oil are used with three commercialized microfluidic flow-focusing devices.",{"name":120,"@type":111,"acceptedAnswer":121},"How do the authors evaluate different machine learning approaches?",{"text":122,"@type":114},"They compare three machine learning architectures, select the one with the most accurate predictions, and validate results against a new set of experiments performed on a different day to capture variability.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290186,1789640895,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},687197100911,"https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697","RESEARCH ARTICLE | SEPTEMBER 08 2023  \nMachine learning enhanced droplet microfluidics   \nClaire Barnes  ; Ashish R. Sonwane (आीशर सोनवने)  ; Eva C. Sonnenschein  ; Francesco Del Giudice 􀀧   \nPhysics of Fluids 35, 092003 (2023)  \n[https://doi.org/10.1063/5.0163806](https://doi.org/10.1063/5.0163806)  \n􀀭  \nView Online  \n􀀱  \nExport Citation  \n02 August 2024 12:00:16  \nPhysics of Fluids ARTICLE  \n[pubs.aip.org/aip/pof](pubs.aip.org/aip/pof)  \nMachine learning enhanced droplet  \nmicrofluidics   \n\n| Cite as: Phys. Fluids 35, 092003 (2023); doi: 10.1063/5.0163806 Submitted: 19 June 2023 . Accepted: 17 August 2023 .\u003Cbr>Published Online: 8 September 2023 | | | |\n| --- | --- | --- | --- |\n| Claire Barnes,1  Ashish R. Sonwane (),2  Eva C. Sonnenschein,3  and Francesco Del Giudice4,a)  |  |  |  |\n| AFFILIATIONS\u003Cbr>1 Department of Biomedical Engineering, Faculty of Science and Engineering, School of Engineering and Applied Science, Swansea University, Fabian Way, Swansea SA1 8EN, United Kingdom\u003Cbr>2Computational Foundry, Faculty of Science and Engineering, Swansea University, Fabian Way, Swansea SA1 8EN, United Kingdom 3 Department of Biosciences, Faculty of Science and Engineering, School of Biosciences, Geography and Physiscs,\u003Cbr>Swansea University, Singleton Park, Swansea SA2 8PP, United Kingdom\u003Cbr>4 Department of Chemical Engineering, Faculty of Science and Engineering, School of Engineering and Applied Science, Swansea University, Fabian Way, Swansea SA1 8EN, United Kingdom\u003Cbr>a)Author to whom correspondence should be addressed: [francesco.delgiudice@swansea.ac.uk](francesco.delgiudice@swansea.ac.uk) |  |  |  |\n| ABSTRACT\u003Cbr>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.\u003Cbr>VC 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license ([http://](http://)[ ](http://)[creativecommons.org/licenses/by/4.0/](creativecommons.org/licenses/by/4.0/)). [https://doi.org/10.1063/5.0163806](https://doi.org/10.1063/5.0163806) |  |  |  |\n\nI. INTRODUCTION  \nThe formation of droplets in microfluidic devices has been the subject of significant interest in the past few decades1–3 for many applications such as single-cell analysis,4 spheroids and organoids production,5 and digital polymerase chain reaction (PCR).6 The droplet formation mechanism in micrometre-scale devices is a c","cbCaitpwbRs0Jmo0","https://ap.wps.com/l/cbCaitpwbRs0Jmo0","pdf",4481652,13,"English","# Abstract\n# I. Introduction","[{\"question\":\"Why has machine learning been introduced to droplet microfluidics?\",\"answer\":\"It helps identify patterns in complex droplet-formation data and rapidly deploy predictive models for droplet generation parameters.\"},{\"question\":\"What fluids and devices are used in this study?\",\"answer\":\"Phosphate buffer saline and biocompatible fluorinated oil are used with three commercialized microfluidic flow-focusing devices.\"},{\"question\":\"How do the authors evaluate different machine learning approaches?\",\"answer\":\"They compare three machine learning architectures, select the one with the most accurate predictions, and validate results against a new set of experiments performed on a different day to capture variability.\"}]","Machine learning enhanced droplet microfluidics - Research Article | PDF",33]