Digital twin for controlled generation of water-in-oil microdroplets with required size

Gyimah, Nafisat; Scheler, Ott; Rang, Toomas; Pardy, Tamás

doi:10.1109/eurosime54907.2022.9758876

Cited by 3 publications

(4 citation statements)

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“…The generation frequency related pressure target was derived from using the error (ef) between the set point (fset) and the average measured droplet generation frequency (fm). By fixing the disperse phase pressure, the control variable relationships between "droplet size" to "disperse phase pressure" could be made as "droplet size" to "continuous phase pressure", depending on the sensitivity of the target parameter to the fluid phase type [23]. In reference to our previous work, the PID controller parameters (i.e., proportional gain, integral gain, and derivative gain) were derived using a Genetic Algorithm (GA).…”

Section: B Electronics Module 1) Flow Control Strategymentioning

confidence: 99%

“…To tune the PID gains, experimental data (from our previous works [23], [24]) demonstrating the effect of inlet pressure on droplet size were used in MATLAB to derive pumping system component transfer functions. The transfer functions were implemented in Simulink in a setup-derived closed loop feedback model.…”

Section: ) Pressure-based System Feedback Modellingmentioning

confidence: 99%

“…Based on our previous works [23], [24], the system components were mathematically represented as transfer functions in eqns. 1-3, using the collected data in MATLAB software.…”

Section: A Submodule Evaluation 1) Pressure-based System Feedback Mod...mentioning

confidence: 99%

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CogniFlow-Drop: Integrated Modular System for Automated Generation of Droplets in Microfluidic Applications

Jõemaa,

Gyimah,

Ashraf

et al. 2023

IEEE Access

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Droplet microfluidics enables studying large cell populations in chemical isolation, at a singlecell resolution. Applications include studying cellular response to drugs, cell-to-cell interaction studies. Such applications need a reliable and repeatable droplet generation with high monodispersity. Most systems used in research rely on manual tuning of flow parameters on off-the-shelf instruments. Setups are highly customized, limiting reproduction of experimental results. We propose an integrated, modular system for automated aqueous droplet generation with high monodispersity. The system provides dynamic feedback control of droplet size and input pressure. Input pressure is generated by two piezoelectric micropumps. Droplet sizes are determined via light intensity measurement in an LED-photodiode setup. The system is capable of wireless communication and has a low enough power consumption for battery-powered operation. We report on the assembly and the underlying working principle, as well as an in-depth experimental evaluation of the performance of the proof-of-concept prototype in aqueous droplet generation. Evaluation was performed on a modular as well as on a system level. During module-level evaluations, aqueous droplets were generated in a light mineral oil + Span 80 surfactant carrier medium, using 3 different flow-focusing junction geometries. The presented prototype had a significantly faster pressure stabilization time (10 s) compared to a syringe pump-based reference setup (120 s). During system-level evaluation, deionized water droplets were generated in a carrier medium of HFE7500 + PEG-PFPE triblock surfactant. Resultant droplet sizes were benchmarked with microscopy. The system was able to repeatedly generate mono-and polydisperse droplets on demand, with CVs between 5-10% in the ~50-200 m droplet diameter range.

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Section: B Electronics Module 1) Flow Control Strategymentioning

confidence: 99%

Section: ) Pressure-based System Feedback Modellingmentioning

confidence: 99%

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CogniFlow-Drop: Integrated Modular System for Automated Generation of Droplets in Microfluidic Applications

Jõemaa,

Gyimah,

Ashraf

et al. 2023

IEEE Access

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“…Specifically, we describe the construction of an empirical mathematical model for the calculation of droplet generation rates and dimensions in the water-in-oil flow-focusingtype [11] droplet generation node of a lab-on-a-chip cytometer. This study was partly based on the digital twin model developed by our group [12].…”

Section: Introductionmentioning

confidence: 99%

Compact Empirical Model for Droplet Generation in a Lab-on-Chip Cytometry System

et al. 2022

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This study describes the construction of a compact empirical mathematical model for a flowfocusing microfluidic droplet generator. The application case is a portable, low-cost flow cytometry system for microbiological applications, with water droplet sizes of 50-70 micrometer range and droplet generation rates of 500-1500 per second. In this study, we demonstrate that for the design of reliable microfluidic systems, the availability of an empirical model of droplet generation is a mandatory precondition that cannot be achieved by time-consuming simulations based on detailed physical models. When introducing the concept of a compact empirical model, we refer to a mathematical model that considers general theoretical estimates and describes experimental behavioral trends with a minimal set of easily measurable parameters. By interpreting the experimental results for different water-and oil-phase flow rates, we constructed a minimal 3-parameter droplet generation rate model for every fixed water flow rate by relying on submodels of the water droplet diameter and effective ellipticity. As a result, we obtained a compact model with an estimated 5-10% accuracy for the planned typical application modes. The main novelties of this study are the demonstration of the applicability of the linear approximation model for droplet diameter suppression by the oil flow rate, introduction of an effective ellipticity parameter to describe the droplet form transformation from a bullet-like shape to a spherical shape, and introduction of a machine learning correction function that could be used to fine-tune the model during the real-time operation of the system.

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PID Controller Tuning Optimization Using Genetic Algorithm for Droplet Size Control in Microfluidics

Gyimah

Joemaa

Pärnamets

et al. 2022

2022 18th Biennial Baltic Electronics Conference (BEC)

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Digital twin for controlled generation of water-in-oil microdroplets with required size

Cited by 3 publications

References 0 publications

CogniFlow-Drop: Integrated Modular System for Automated Generation of Droplets in Microfluidic Applications

CogniFlow-Drop: Integrated Modular System for Automated Generation of Droplets in Microfluidic Applications

Compact Empirical Model for Droplet Generation in a Lab-on-Chip Cytometry System

PID Controller Tuning Optimization Using Genetic Algorithm for Droplet Size Control in Microfluidics

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