Mixing characterization of binary-coalesced droplets in microchannels using deep neural network
Mixing characterization of binary-coalesced droplets in microchannels using deep neural network
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DOI：
10.1063/5.0008461
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发表时间：
2020-05-01
期刊：
影响因子：
3.2
通讯作者：
Kumar Ranjith, S.
中科院分区：
文献类型：
--
作者：
Arjun, A.;Ajith, R. R.;Kumar Ranjith, S.
实时目标识别和分类在许多微流控应用中是必不可少的，特别是在微滴微流控中。本文讨论了应用卷积神经网络检测流场中合并的微液滴，并根据混合程度对其进行动态分类。液滴在采用流动聚焦和交叉流动配置的PMMA微流体装置中产生。利用附着在显微镜上的CCD相机对液滴的二元聚并进行可视化，并记录图像序列。不同的实时目标定位和分类网络，如You Only Look Once和single - shot Multibox Detector，用于液滴检测和表征。从捕获的图像中创建一个自定义数据集来训练这些深度神经网络进行检测和分类，并手动标记。根据混合程度，将合并后的液滴分为低混合、中混合和高混合三类。经过训练的模型在不同环境条件、液滴形状、液滴大小和二元流体组合下拍摄的图像中进行了测试，结果表明该模型在预测方面确实表现出了很高的准确性和精度。此外，实验还证明了这些方法可以有效地定位视频或图像中合并的二元液滴，并在不考虑实验条件的情况下实时地根据混合等级对它们进行分类。
Real-time object identification and classification are essential in many microfluidic applications especially in the droplet microfluidics. This paper discusses the application of convolutional neural networks to detect the merged microdroplet in the flow field and classify them in an on-the-go manner based on the extent of mixing. The droplets are generated in PMMA microfluidic devices employing flow-focusing and cross-flow configurations. The visualization of binary coalescence of droplets is performed by a CCD camera attached to a microscope, and the sequence of images is recorded. Different real-time object localization and classification networks such as You Only Look Once and Singleshot Multibox Detector are deployed for droplet detection and characterization. A custom dataset to train these deep neural networks to detect and classify is created from the captured images and labeled manually. The merged droplets are segregated based on the degree of mixing into three categories: low mixing, intermediate mixing, and high mixing. The trained model is tested against images taken at different ambient conditions, droplet shapes, droplet sizes, and binary-fluid combinations, which indeed exhibited high accuracy and precision in predictions. In addition, it is demonstrated that these schemes are efficient in localization of coalesced binary droplets from the recorded video or image and classify them based on grade of mixing irrespective of experimental conditions in real time.