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

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.