A Cosine Similarity Algorithm Method for Fast and Accurate Monitoring of Dynamic Droplet Generation Processes

Xiurui Zhu,Lingxiang Zhu,Gaoshan Jing,Junyuan Liu,Shisheng Su,Mingzhu Fu,Wenjun Yang,Yong Guo

doi:10.1038/s41598-018-28270-8

Abstract

Droplet microfluidics has attracted significant interests in functional microcapsule synthesis, pharmaceuticals, fine chemicals, cosmetics and biomedical research. The low variability of performing chemical reactions inside droplets could benefit from improved homogeneity and reproducibility. Therefore, accurate and convenient methods are needed to monitor dynamic droplet generation processes. Here, a novel Cosine Similarity Algorithm (CSA) method was developed to monitor the droplet generation frequency accurately and rapidly. With a microscopic droplet generation video clip captured with a high-speed camera, droplet generation frequency can be computed accurately by calculating the cosine similarities between the frames in the video clip. Four kinds of dynamic droplet generation processes were investigated including (1) a stable condition in a single microfluidic channel, (2) a stable condition in multiple microfluidic channels, (3) a single microfluidic channel with artificial disturbances, and (4) microgel fabrication with or without artificial disturbances. For a video clip with 5,000 frames and a spatial resolution of 512 × 62 pixels, droplet generation frequency up to 4,707.9 Hz can be calculated in less than 1.70 s with an absolute relative calculation error less than 0.08%. Artificial disturbances in droplet generation processes can be precisely determined using the CSA method. This highly effective CSA method could be a powerful tool for further promoting the research of droplet microfluidics.

Highlights

We proposed a novel cosine similarity algorithm (CSA) method for automatic monitoring of dynamic droplet generation processes
Cyclic auto-spectrum is calculated with fast Fourier transform (FFT) to obtain the frequency distribution of the similarity vector, through which the mean value and coefficient of variation (CV) of the droplet generation frequency can be calculated from the further spectral analysis
With a microscopic droplet generation video clip at a constant frame rate captured by a high-speed camera, four kinds of dynamic droplet generation processes were monitored

Summary

Method for Fast and Accurate

Xiurui Zhu[1], Shisheng Su1, Mingzhu Fu1, Junyuan Liu[1], Lingxiang Zhu[2,3], Wenjun Yang[1,3], Gaoshan Jing4 & Yong Guo[1]. These electrical methods were capable of monitoring both single and multi-channel droplet generation processes, the complexity of chip fabrication and system setup was increased by the requirement of embedded microelectrodes and specialized detection systems Another kind of approach to monitoring dynamic droplet generation process is microscopic imaging. Through acquiring microscopic video clips of droplet generation processes, the CSA method was able to calculate droplet generation frequency distributions from the periodic changes of cosine similarities between video frames. Both differences among multiple channels and changes along the timeline could be closely monitored by the CSA method in a fast, convenient and accurate manner with differences among frequency peaks and changes in frequency distributions. To the best of our knowledge, this is the first time that cosine similarity has been applied to monitor dynamic droplet generation processes in droplet microfluidics, and this highly effective method could be a powerful tool for further promoting the research and development of droplet microfluidics

Results and Discussion

Conclusions

Materials and Methods