A real-time cosine similarity algorithm method for continuous monitoring of dynamic droplet generation processes
Xiurui Zhu,
Shisheng Su,
Baoxia Liu,
Lingxiang Zhu,
Wenjun Yang,
Na Gao,
Gaoshan Jing,
Yong Guo
Affiliations
Xiurui Zhu
Department of Biomedical Engineering, School of Medicine, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Tsinghua University, Beijing 100084, China
Shisheng Su
Department of Biomedical Engineering, School of Medicine, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Tsinghua University, Beijing 100084, China
Baoxia Liu
Department of Biomedical Engineering, School of Medicine, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Tsinghua University, Beijing 100084, China
Lingxiang Zhu
National Research Institute for Family Planning, Beijing 100090, China
Wenjun Yang
Department of Biomedical Engineering, School of Medicine, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Tsinghua University, Beijing 100084, China
Na Gao
TargetingOne Corporation, Beijing 100081, China
Gaoshan Jing
Department of Precision Instrument, School of Mechanical Engineering, State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing 100084, China
Yong Guo
Department of Biomedical Engineering, School of Medicine, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Tsinghua University, Beijing 100084, China
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.
