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

release date:2018-07-02

Droplet microfuidics has attracted signifcant interests in functional microcapsule synthesis, pharmaceuticals, fne chemicals, cosmetics and biomedical research. The low variability of performing chemical reactions inside droplets could beneft 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 microfuidic channel, (2) a stable condition in multiple microfuidic channels, (3) a single microfuidic channel with artifcial disturbances, and (4) microgel fabrication with or without artifcial disturbances. For a video clip with 5,000 frames and a spatial resolution of 512×62 pixels, droplet generation frequency up to 4,707.9Hz can be calculated in less than 1.70s with an absolute relative calculation error less than 0.08%. Artifcial disturbances in droplet generation processes can be precisely determined using the CSA method. This highly efective CSA method could be a powerful tool for further promoting the research of droplet microfuidics.

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