Introduction

Nguyen, Nam‐Trung

doi:10.1016/b978-1-4377-3520-8.00001-2

Cited by 4 publications

(7 citation statements)

References 4 publications

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“…GrayIntensity n GrayIntensity 1 (6) The validation step was carried out using cross-validation, considering 5 repetitions; in each iteration, 60% were randomly selected for training and 40% for validation. The efficiency calculation results obtained by image processing from each iteration of the cross-validation was compared with those obtained by numerical models.…”

Section: Me =mentioning

confidence: 99%

“…Micromixers operate typically under a laminar flow regime; in them, viscous forces dominate over inertial forces. Mixing at the microscale is based on three basic principles: molecular diffusion, chaotic advection and Taylor dispersion [6,7]. Molecular diffusion is related to the Brownian motion of molecules from a region of high concentration to one of low concentration.…”

Section: Introductionmentioning

confidence: 99%

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Numerical and Experimental Validation of Mixing Efficiency in Periodic Disturbance Mixers

et al. 2021

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The shape and dimensions of a micromixer are key elements in the mixing process. Accurately quantifying the mixing efficiency enables the evaluation of the performance of a micromixer and the selection of the most suitable one for specific applications. In this paper, two methods are investigated to evaluate the mixing efficiency: a numerical model and an experimental model with a software image processing technique. Using two methods to calculate the mixing efficiency, in addition to corroborating the results and increasing their reliability, creates various possible approaches that can be selected depending on the circumstances, resources, amount of data to be processed and processing time. Image processing is an easy-to-implement tool, is applicable to different programming languages, is flexible, and provides a quick response that allows the calculation of the mixing efficiency using a process of filtering of images and quantifying the intensity of the color, which is associated with the percentage of mixing. The results showed high similarity between the two methods, with a difference ranging between 0 and 6% in all the evaluated points.

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Section: Me =mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Numerical and Experimental Validation of Mixing Efficiency in Periodic Disturbance Mixers

et al. 2021

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“…Micromixers are typically one of the major operational sub-units of microfluidic schemes [ 5 ] and are employed to mix fluids using active or passive [ 6 , 7 , 8 , 9 ] mixing principles. In active mixing, extra modules are required to generate external disturbance forces on the flow domain (e.g., electrical, thermal, magnetic, acoustic, and pressure [ 10 , 11 , 12 ]) which develops a complexity in terms of fabrication and integration of these components with other microchip elements [ 11 , 13 , 14 ]. On the contrary, despite offering a relatively poor mixing performance, passive micromixers are simple devices with notable structural and operational advantages.…”

Section: Introductionmentioning

confidence: 99%

“…In passive micromixers, fluid mixing arises as a challenging work due to advection-dominant transport developed in microchannels. Strictly laminar fluid flow that is usually Reynolds (Re) number << 100 [ 16 , 17 ] and very low molecular diffusion coefficients (e.g., typically in the range of 10 −9 –10 −11 m 2 /s [ 12 ]) fundamentally create tough mixing conditions. These difficulties are generally dealt with generating a so-called chaotic fluid motion in special micromixer designs that is typically achieved when Re > 10–20.…”

Section: Introductionmentioning

confidence: 99%

Toward the Next Generation of Passive Micromixers: A Novel 3-D Design Approach

Okuducu

Aral²

2021

Micromachines

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Passive micromixers are miniaturized instruments that are used to mix fluids in microfluidic systems. In microchannels, combination of laminar flows and small diffusion constants of mixing liquids produce a difficult mixing environment. In particular, in very low Reynolds number flows, e.g., Re < 10, diffusive mixing cannot be promoted unless a large interfacial area is formed between the fluids to be mixed. Therefore, the mixing distance increases substantially due to a slow diffusion process that governs fluid mixing. In this article, a novel 3-D passive micromixer design is developed to improve fluid mixing over a short distance. Computational Fluid Dynamics (CFD) simulations are used to investigate the performance of the micromixer numerically. The circular-shaped fluid overlapping (CSFO) micromixer design proposed is examined in several fluid flow, diffusivity, and injection conditions. The outcomes show that the CSFO geometry develops a large interfacial area between the fluid bodies. Thus, fluid mixing is accelerated in vertical and/or horizontal directions depending on the injection type applied. For the smallest molecular diffusion constant tested, the CSFO micromixer design provides more than 90% mixing efficiency in a distance between 260 and 470 µm. The maximum pressure drop in the micromixer is found to be less than 1.4 kPa in the highest flow conditioned examined.

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“…Typically, micromixers possess channels that are tens to hundreds of µm wide, where substances are mixed. At this scale, laminar flow commonly occurs, providing fluids with stable and uniform mixing interfaces, with mixing times in the millisecond range [11][12][13]. This mixing speed is essential for reactions where unstable intermediate states are produced as in the case of liposomes.…”

Section: Introductionmentioning

confidence: 99%

Surface Response Based Modeling of Liposome Characteristics in a Periodic Disturbance Mixer

et al. 2020

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Liposomes nanoparticles (LNPs) are vesicles that encapsulate drugs, genes, and imaging labels for advanced delivery applications. Control and tuning liposome physicochemical characteristics such as size, size distribution, and zeta potential are crucial for their functionality. Liposome production using micromixers has shown better control over liposome characteristics compared with classical approaches. In this work, we used our own designed and fabricated Periodic Disturbance Micromixer (PDM). We used Design of Experiments (DoE) and Response Surface Methodology (RSM) to statistically model the relationship between the Total Flow Rate (TFR) and Flow Rate Ratio (FRR) and the resulting liposomes physicochemical characteristics. TFR and FRR effectively control liposome size in the range from 52 nm to 200 nm. In contrast, no significant effect was observed for the TFR on the liposomes Polydispersity Index (PDI); conversely, FRR around 2.6 was found to be a threshold between highly monodisperse and low polydispersed populations. Moreover, it was shown that the zeta potential is independent of TFR and FRR. The developed model presented on the paper enables to pre-establish the experimental conditions under which LNPs would likely be produced within a specified size range. Hence, the model utility was demonstrated by showing that LNPs were produced under such conditions.

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Introduction

Cited by 4 publications

References 4 publications

Numerical and Experimental Validation of Mixing Efficiency in Periodic Disturbance Mixers

Numerical and Experimental Validation of Mixing Efficiency in Periodic Disturbance Mixers

Toward the Next Generation of Passive Micromixers: A Novel 3-D Design Approach

Surface Response Based Modeling of Liposome Characteristics in a Periodic Disturbance Mixer

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