Machine learning enhanced droplet microfluidics

Author:

Barnes Claire1ORCID,Sonwane Ashish R.ORCID,Sonnenschein Eva C.2ORCID,Del Giudice Francesco3ORCID

Affiliation:

1. Department of Biomedical Engineering, Faculty of Science and Engineering, School of Engineering and Applied Science, Swansea University 1 , Fabian Way, Swansea SA1 8EN, United Kingdom

2. Department of Biosciences, Faculty of Science and Engineering, School of Biosciences, Geography and Physiscs, Swansea University 3 , Singleton Park, Swansea SA2 8PP, United Kingdom

3. Department of Chemical Engineering, Faculty of Science and Engineering, School of Engineering and Applied Science, Swansea University 4 , Fabian Way, Swansea SA1 8EN, United Kingdom

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.

Funder

Engineering and Physical Sciences Research Council

Royal Society

Llywodraeth Cymru

Publisher

AIP Publishing

Subject

Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3