Experiment and prediction of droplet formation in microfluidic cross-junctions with different bifurcation angles

Yu, Wei; Liu, Xiangdong; Li, Bo; Chen, Yongping

doi:10.1016/j.ijmultiphaseflow.2022.103973

Cited by 24 publications

(7 citation statements)

References 37 publications

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“…The droplet size is typically over 10 µm and is mainly influenced by the channel structure, while the C.V. is below 2%. The angle between the continuous phase and the dispersed phase is variable in cross-junction flow [120][121][122].…”

Section: Conventional Microfluidic Generation Of Single-phasementioning

confidence: 99%

High-throughput microfluidic production of carbon capture microcapsules: fundamentals, applications, and perspectives

Liu,

Gao,

et al. 2024

Int. J. Extrem. Manuf.

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In the last three decades, carbon dioxide (CO2) emissions have shown a significant increase from various sources. To address this pressing issue, the importance of reducing CO2 emissions has grown, leading to increased attention toward carbon capture, utilization, and storage (CCUS) strategies. Among these strategies, monodisperse microcapsules, produced using droplet microfluidics, have emerged as promising tools for carbon capture, offering a potential solution to mitigate CO2 emissions. However, the limited yield of microcapsules due to the inherent low flow rate in droplet microfluidics remains a challenge. In this comprehensive review, the high-throughput production of carbon capture microcapsules using droplet microfluidics is focused on. Specifically, the detailed insights into microfluidic chip fabrication technologies, the microfluidic generation of emulsion droplets, along with the associated hydrodynamic considerations, and the generation of carbon capture microcapsules through droplet microfluidics are provided. This review highlights the substantial potential of droplet microfluidics as a promising technique for large-scale carbon capture microcapsule production, which could play a significant role in achieving carbon neutralization and emission reduction goals.

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Section: Conventional Microfluidic Generation Of Single-phasementioning

confidence: 99%

High-throughput microfluidic production of carbon capture microcapsules: fundamentals, applications, and perspectives

Liu,

Gao,

et al. 2024

Int. J. Extrem. Manuf.

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“…[10][11][12] In this kind of microfluidics, the shear force is critical in the evolution and breakup of interfaces. 13,14 The droplet size is mainly influenced by the change in flow rates of the fluids, and thus a small flow rate fluctuation can lead to the deterioration in monodispersity of droplets. 15 As another droplet generation approach, step emulsification (SE) is different from the shear-based microfluidics, which is dominated by the interfacial tension.…”

Section: Introductionmentioning

confidence: 99%

“…Shear‐based microfluidic approach is one of the most common way to produce monodispersed droplets, such as co‐flow, 7 T‐junction, 8,9 and flow‐focusing 10–12 . In this kind of microfluidics, the shear force is critical in the evolution and breakup of interfaces 13,14 . The droplet size is mainly influenced by the change in flow rates of the fluids, and thus a small flow rate fluctuation can lead to the deterioration in monodispersity of droplets 15 .…”

Section: Introductionmentioning

confidence: 99%

Robust generation of monodisperse droplets using a microfluidic step emulsification device with triangular nozzle

Liu

et al. 2023

AIChE Journal

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In this work, the droplet generation process in the microfluidic step emulsification chip with a triangular nozzle (SE‐T) was investigated in the combination of visualization experiment and numerical simulation, through a comparison with a rectangular nozzle (SE‐R). The flow regimes, including dripping, dripping‐jetting transition, and jetting, were observed in the SE‐T, among which the dripping is the preferred flow regime to generate monodispersed droplet with corresponding C.V. (coefficient of variation) of the droplet size smaller than 1.9%. Compared with the SE‐R, the larger space and expanding structure of the triangular nozzle in the SE‐T enhance the wall wetting effects, which induces earlier appearance and accelerates shrinking of the neck. As a result, the SE‐T exhibits more robust droplet performance under the dripping regime, which produces the droplets with nearly unchanged size and higher monodispersity, especially little related to the variations of surfactant concentrations and dispersed phase flow rates.

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“…The authors demonstrated that the surfactant concentration influenced the size of droplets and observed that smaller droplets were generated at higher concentrations of surfactants. Yu et al 29 analyzed the effect of bifurcation angle on droplet formation behavior. The authors introduced a power-law equation for a qualitative description of boundaries between different droplet formation regimes.…”

Section: ■ Introductionmentioning

confidence: 99%

Machine Learning-Aided Microdroplets Breakup Characteristic Prediction in Flow-Focusing Microdevices by Incorporating Variations of Cross-Flow Tilt Angles

et al. 2022

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Controlling droplet breakup characteristics such as size, frequency, regime, and droplet quality within flow-focusing microfluidic devices is critical for different biomedical applications of droplet microfluidics such as drug delivery, biosensing, and nanomaterial preparation. The development of a prediction platform capable of forecasting droplet breakup characteristics can significantly improve the iterative design and fabrication processes required for achieving desired performance. The present study aims to develop a multipurpose platform capable of predicting the working conditions of user-specific droplet size and frequency and reporting the quality of the generated droplets, regime, and hydrodynamical breakup characteristics in flow-focusing microdevices with different cross-junction tilt angles. Four different neural network-based prediction platforms were compared to accurately estimate capsule size, generation rate, uniformity, and circle metric. The trained capsule size and frequency networks were optimized using the heuristic optimization approach for establishing the Pareto optimal solution plot. To investigate the transition of the droplet generation regime (i.e., squeezing, dripping, and jetting), two different classification models (LDA and MLP) were developed and compared in terms of their prediction accuracy. The MLP model outperformed the LDA model with a crossvalidation measure evaluated as 97.85%, demonstrating that the droplet quality and regime prediction models can provide an engineering judgment for the decision maker to choose between the suggested solutions on the Pareto front. The study followed a comprehensive hydrodynamical analysis of the junction angle effect on the dispersed thread formation, pressure, and velocity domains in the orifice.

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Experiment and prediction of droplet formation in microfluidic cross-junctions with different bifurcation angles

Cited by 24 publications

References 37 publications

High-throughput microfluidic production of carbon capture microcapsules: fundamentals, applications, and perspectives

High-throughput microfluidic production of carbon capture microcapsules: fundamentals, applications, and perspectives

Robust generation of monodisperse droplets using a microfluidic step emulsification device with triangular nozzle

Machine Learning-Aided Microdroplets Breakup Characteristic Prediction in Flow-Focusing Microdevices by Incorporating Variations of Cross-Flow Tilt Angles

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