A real-time cosine similarity algorithm method for continuous monitoring of dynamic droplet generation processes

Xiurui Zhu,Yong Guo,Lingxiang Zhu,Baoxia Liu,Gaoshan Jing,Shisheng Su,Na Gao,Wenjun Yang

doi:10.1063/1.5102131

Abstract

Droplet microfluidics is becoming an enabling technology for synthesizing microscale particles and an effective real-time method is essential to monitor the variations in a dynamic droplet generation process. Here, a novel real-time cosine similarity algorithm (RT-CSA) method was developed to investigate the droplet generation process by measuring the droplet generation frequency continuously. The RT-CSA method uses a first-in-first-out (FIFO) similarity vector buffer to store calculated cosine similarities, so that these cosine similarities are reused to update the calculation results once a new frame is captured and stored. For the first time, the RT-CSA method achieved real-time monitoring of dynamic droplet generation processes by updating calculation results over 2,000 times per second, and two pre-microgel droplet generation processes with or without artificial disturbances were monitored closely and continuously. With the RT-CSA method, the disturbances in dynamic droplet generation processes were precisely determined, and following changes were monitored and recorded in real time. This highly effective RT-CSA method could be a powerful tool for further promoting research of droplet microfluidics.

Highlights

We propose a real-time cosine similarity algorithm (RT-CSA) method as a novel real-time image-based monitoring method that can keep pace with the recording of droplet generation processes in real time
Droplets are generated with a flow-focusing microfluidic chip, and the dynamic droplet generation process is captured with a high-speed camera
Once the buffer was full, the RT-CSA method was able to update calculation results in a FIFO way once a new frame was captured by the high-speed camera at 2,000 fps

Summary

Introduction

Droplet microfluidics has become a powerful technique and has been applied in a wide range of applications, especially in developing new materials (Günther and Jensen, 2006; Liu and Jiang, 2017; and Wang et al, 2011), such as nanoparticles (Baruah et al, 2018; Li and Lin, 2018), microspheres (Nisisako and Torii, 2008; Xu et al, 2005) and microgels (Headen et al, 2018; Priest et al, 2006; and Rossow et al, 2012). Nguyen et al installed two optical fibers in a microfluidic chip to detect the changes in forward scattering light (Nguyen et al, 2006) Another method is to embed microelectrodes or radio frequency devices in microfluidic devices to monitor the droplet generation process by investigating dielectric property variations of the by-passing droplets. Conchouso et al used RF devices to monitor dynamic droplet generation processes by measuring subtle changes in resonant frequencies when the processes were disturbed (Conchouso et al, 2016) These methods were capable of monitoring dynamic droplet generation processes in real time, the complexity of chip fabrication and system configuration was increased due to the requirement of specialized instruments and microstructures. This method may require a sizeable computational resource, and the monitoring may not keep pace with the droplet generation processes

Methods

Results

Conclusion