dDrop-Chip: disposable film-chip microfluidic device for real-time droplet feedback control

Ryu, Jae-Wook; Kim, Junhyeong; Han, Ki-Ho

doi:10.1039/d2lc01069k

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Cited by 4 publications

References 38 publications

(49 reference statements)

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Sensing area-variable electrode for wide-range droplet size detection

Ryu,

Han

2024

Sensors and Actuators B: Chemical

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Sensing area-variable electrode for wide-range droplet size detection

Ryu,

Han

2024

Sensors and Actuators B: Chemical

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Reusable EWOD-based microfluidic system for active droplet generation

Park,

Ryu,

Han

2025

Lab Chip

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Artificial intelligence-based droplet size prediction for microfluidic system

Dubey,

Vishwakarma,

Ramarao

et al. 2024

HFF

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Purpose This study aims to introduce a vision-based model to generate droplets with auto-tuned parameters. The model can auto-adjust the inherent uncertainties and errors involved with the fabrication and operating parameters in microfluidic platform, attaining precise size and frequency of droplet generation. Design/methodology/approach The photolithography method is utilized to prepare the microfluidic devices used in this study, and various experiments are conducted at various flow-rate and viscosity ratios. Data for droplet shape is collected to train the artificial intelligence (AI) models. Findings Growth phase of droplets demonstrated a unique spring back effect in droplet size. The fully developed droplet sizes in the microchannel were modeled using least absolute shrinkage and selection operators (LASSO) regression model, Gaussian support vector machine (SVM), long short term memory (LSTM) and deep neural network models. Mean absolute percentage error (MAPE) of 0.05 and R2 = 0.93 were obtained with a deep neural network model on untrained flow data. The shape parameters of the droplets are affected by several uncontrolled parameters. These parameters are instinctively captured in the model. Originality/value Experimental data set is generated for varying viscosity values and flow rates. The variation of flow rate of continuous phase is observed here instead of dispersed phase. An automated computation routine is developed to read the droplet shape parameters considering the transient growth phase of droplets. The droplet size data is used to build and compare various AI models for predicting droplet sizes. A predictive model is developed, which is ready for automated closed loop control of the droplet generation.

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dDrop-Chip: disposable film-chip microfluidic device for real-time droplet feedback control

Abstract: A cost-effective, simple to use, and automated technique that can provide real-time feedback control for droplet generation is required to obtain droplets with high-throughput, stability, and uniformity. This study introduces...

Cited by 4 publications

References 38 publications

Sensing area-variable electrode for wide-range droplet size detection

Sensing area-variable electrode for wide-range droplet size detection

Reusable EWOD-based microfluidic system for active droplet generation

Artificial intelligence-based droplet size prediction for microfluidic system

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