Droplet size prediction in a microfluidic flow focusing device using an adaptive network based fuzzy inference system

Mottaghi, Sina; Nazari, Mostafa; Fattahi, Salar; Nazari, Mohsen; Babamohammadi, Saeed

doi:10.1007/s10544-020-00513-4

Cited by 16 publications

(8 citation statements)

References 26 publications

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“…The difference between both results could possibly be caused by a number of factors that were ignored in the system model, including the compliance in the PDMS microfluidic device, fluid leakage, temperature variations, and fabrication errors. The results in Figure 5 a,b were also found to be in agreement with those reported in [ 23 , 24 , 25 , 29 ].…”

Section: Model Validationsupporting

confidence: 91%

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Modeling of Droplet Generation in a Microfluidic Flow-Focusing Junction for Droplet Size Control

2021

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In this paper, we study the parameters that affect the generation of droplets in a microfluidic flow-focusing junction. Droplets are evaluated based on the size and frequency of generation. Droplet size control is essential for microfluidic lab-on-a-chip applications in biology, chemistry, and medicine. We developed a three-dimensional numerical model that can emulate the performance of the physical system. A numerical model can help design droplet-generation chips with new junction geometries, different dispersed and continuous phase types, and different flow rates. Our model uses a conservative level-set method (LSM) to track the interface between two immiscible fluids using a fixed mesh. Water was used for the dispersed phase and mineral oil for the continuous phase. The effects of the continuous-to-dispersed flow rate ratio (Qo/Qw) and the surfactant concentration on the droplet generation were studied both using the numerical model and experimentally. The numerical model was found to render results that are in good agreement with the experimental ones, which validates the LSM model. The validated numerical model was used to study the time effect of changing Qo/Qw on the generated droplet size. Properly timing when the flow rates are changed enables control over the size of the next generated droplet, which is useful for single-droplet size modulation applications.

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Section: Model Validationsupporting

confidence: 91%

“…However, these studies were not validated. Mottaghi et al mixed artificial neural networks and fuzzy inference systems, to study the parameters that affect the droplet size, numerically and experimentally [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Droplet Generation in a Microfluidic Flow-Focusing Junction for Droplet Size Control

2021

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“…37 A similar work was conducted by Mottaghi et al, who studied the effect of four dimensionless parameters, Ca, Re, flow rate ratio, and viscosity ratio, on the droplet size. 86 In addition, other parameters, including cross-junction tilt angles, flow rates, and surfactant concentration, can also be correlated to the droplet size, and multiple droplet properties other than droplet sizes, such as generation frequency and flow regime, can be accurately predicted. [87][88][89] 90,91 To further increase the model training efficiency, Siemenn et al designed a Bayesian optimization and computer vision feedback loop to quickly discover the control parameters.…”

Section: Prediction Of Droplet Propertiesmentioning

confidence: 99%

Functions and applications of artificial intelligence in droplet microfluidics

Liu

Zhao

et al. 2023

Lab Chip

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“…According to Lopez [10], the performance of the LZ model was evaluated by R 2 , R 2 -adjusted, and R 2 -predicted. R 2 values' range is from 0 to 1, where 0 indicates that the model does not describe the process, and 1 shows that all data are on the regression line [21]. The adjusted R 2 is a variant of R 2 that has been adjusted for the number of forecasters in the model.…”

Section: Da Prediction Modelmentioning

confidence: 99%

“…Micromachines 2021, 12, 1164 2 of 14 Currently, there is no ANN-based model for the prediction of LZ; however, this technique was previously used successfully in the prediction of silver nanoparticles [19,20], droplet size in microfluidic devices [21], or the identification of operating parameters in microfluidic devices [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Artificial Neural Networks and Data Analysis in Predicting Liposome Size in a Periodic Disturbance Micromixer

et al. 2021

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Artificial neural networks (ANN) and data analysis (DA) are powerful tools for supporting decision-making. They are employed in diverse fields, and one of them is nanotechnology; for example, in predicting silver nanoparticles size. To our knowledge, we are the first to use ANN to predict liposome size (LZ). Liposomes are lipid nanoparticles used in different biomedical applications that can be produced in Dean-Forces-based microdevices such as the Periodic Disturbance Micromixer (PDM). In this work, ANN and DA techniques are used to build a LZ prediction model by using the most relevant variables in a PDM, the Flow Rate Radio (FRR), and the Total Flow Rate (TFR), and the temperature, solvents, and concentrations were kept constant. The ANN was designed in MATLAB and fed data from 60 experiments with 70% training, 15% validation, and 15% testing. For DA, a regression analysis was used. The model was evaluated; it showed a 0.98147 correlation coefficient for training and 0.97247 in total data compared with 0.882 obtained by DA.

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Droplet size prediction in a microfluidic flow focusing device using an adaptive network based fuzzy inference system

Cited by 16 publications

References 26 publications

Modeling of Droplet Generation in a Microfluidic Flow-Focusing Junction for Droplet Size Control

Modeling of Droplet Generation in a Microfluidic Flow-Focusing Junction for Droplet Size Control

Functions and applications of artificial intelligence in droplet microfluidics

Comparative Evaluation of Artificial Neural Networks and Data Analysis in Predicting Liposome Size in a Periodic Disturbance Micromixer

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