Visualizing millisecond chaotic mixing dynamics in microdroplets: A direct comparison of experiment and simulation

Jiang, Liguo; Zeng, Yan; Zhou, Hongbo; Qu, Jianan Y.; Yao, Shuhuai

doi:10.1063/1.3673254

Cited by 39 publications

(27 citation statements)

References 45 publications

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“…Especially in channels with smaller central radius, velocity variation is larger compared to the ones with larger radius. This variation has an important role on hydrodynamic mixing since velocity variation inside the droplet certifies velocity profile variation inside the droplet as indicated in Jiang et al (2012). That is to say, having more fluctuation in the magnitude of the velocity improves the mixing performance.…”

Section: Fig 2 Geometric Definitions Of Mixing Zonementioning

confidence: 99%

Numerical analysis of mixing performance in sinusoidal microchannels based on particle motion in droplets

Özkan

Erdem

2015

Microfluid Nanofluid

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This numerical study was conducted to analyze and understand the parameters that affect the mixing performance of droplet-based flow in sinusoidal microfluidic channels. Finite element analysis was used for modeling fluid flow and droplet formation inside the microchannels via tracking interface between the two heterogeneous fluids along with multiple particle trajectories inside a droplet. The solutions of multiphase fluid flow and particle trajectories were coupled with each other so that drag on every single particle changed in every time step. To solve fluid motion in multiphase flow, level set method was used. Parametric study was repeated for different channel dimensions and different sinusoidal channel profiles. These results were compared with mixing in droplets inside a straight microchannel. Additionally, tracking of multiple particles inside a droplet was performed to simulate the circulating flow profile inside the droplets. Based on the calculation of the dispersion length, particle trajectories, and velocities inside droplets, it is concluded that having smaller channel geometries increases the mixing performance inside the droplet. This also shows that droplet-based fluid flow in microchannels is very suitable for performing chemical reactions inside droplets as it will occur faster. Moreover, narrower and sinusoidal microchannels showed better dispersion length difference compared to straight and wider microchannels. © 2015, Springer-Verlag Berlin Heidelberg

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Section: Fig 2 Geometric Definitions Of Mixing Zonementioning

confidence: 99%

Numerical analysis of mixing performance in sinusoidal microchannels based on particle motion in droplets

Özkan

Erdem

2015

Microfluid Nanofluid

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“…Jiang et al . performed two‐photon fluorescence lifetime imaging and 2D numerical simulation to visualize millisecond chaotic mixing dynamics inside microdroplets moving through a serpentine channel. Another method to improve the mixing performance inside droplets is internal recirculation flow.…”

Section: Introductionmentioning

confidence: 99%

Fluid mixing in droplet‐based microfluidics with T junction and convergent–divergent sinusoidal microchannels

Yang

Liu

et al. 2018

Electrophoresis

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To explore and utilize the advantages of droplet-based microfluidics, hydrodynamics, and mixing process within droplets traveling though the T junction channel and convergent-divergent sinusoidal microchannels are studied by numerical simulations and experiments, respectively. In the T junction channel, the mixing efficiency is significantly influenced by the twirling effect, which controls the initial distributions of the mixture during the droplet formation stage. Therefore, the internal recirculating flow can create a convection mechanism, thus improving mixing. The twirling effect is noticeably influenced by the velocity of the continuous phase; in the sinusoidal channel, the Dean vortices and droplet deformation are induced by centrifugal force and alternative velocity gradient, thus enhancing the mixing efficiency. The best mixing occurred when the droplet size is comparable with the channel width. Finally, we propose a unique optimized structure, which includes a T junction inlet joined to a sinusoidal channel. In this structure, the mixing of fluids in the droplets follows two routes: One is the twirling effect and symmetric recirculation flow in the straight channel. The other is the asymmetric recirculation and droplet deformation in the winding and variable cross-section. Among the three structures, the optimized structure has the best mixing efficiency at the shortest mixing time (0.25 ms). The combination of the twirling effect, variable cross-section effect, and Dean vortices greatly intensifies the chaotic flow. This study provides the insight of the mixing process and may benefit the design and operations of droplet-based microfluidics.

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“…The formation of fluid layers enabled fast mixing and enhanced the reaction of enzymatic bioreactors. Jiang et al (2011) used an optical detection technique using twophoton fluorescence imaging microscopy to depict two-dimensional maps of mixing dynamics by chaotic advection in microdroplet. The chaotic advection inside microdroplets introduced by the periodic flow in terms of two symmetric or asymmetric vortices inside microdroplets moving through a serpentine channel was measured experimentally and matched well with theoretical and computational results.…”

Section: Introductionmentioning

confidence: 99%

Increased density and coverage uniformity of viruses on a sensor surface by using U-type, T-type, and W-type microfluidic devices

Tseng

Tsai

et al. 2012

Biomicrofluidics

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Microorganisms, molecules, or viruses in the fluidic environment are usually at considerably low Reynolds numbers because of small diameters. The viscous forces of molecules and viruses dominate at considerably low Reynolds numbers. This study developed three microfluidic devices, that is, T type, U type, and W type devices, to control the flow movement, which can increase the adhesion density of viruses on the surface of the sensor. The linker 11-mercaptoundecanoic acid (11-MUA) and Turnip yellow mosaic virus (TYMV) were used in this study and measured by a confocal microscope. Fluorescent intensity and coverage of 11-MUA and TYMV were used to identify the adhesion density quantitatively. Results indicate that 11-MUA layers and TYMV disperse randomly by the dipping method. Attachment tests for T-, U-, and W-type devices demonstrated average fluorescence intensities of 1.56, 2.18, and 2.67, respectively, and average fluorescence coverage of 1.31, 1.87, and 2.55 times those of dipping techniques, respectively. The T-type device produced the lowest fluorescence coverage uniformity (10%-80%), whereas the W-type device produced the highest fluorescence coverage uniformity (80%-90%). Fluorescence intensity correlates positively with flow within a specified flow range; however, the exact relationship between fluorescence intensity and flow requires further study. Attachment tests for TYMV virus samples indicated that the W-type device produced an average fluorescence intensity of 3.59 and average fluorescence coverage of 19.13 times greater than those achieved through dipping techniques. Traditional immersion methods achieved fluorescence coverage of 0%-10%, whereas that of the W-type device reached 70%-90%. V C 2012 American Institute of Physics. [http://dx

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Visualizing millisecond chaotic mixing dynamics in microdroplets: A direct comparison of experiment and simulation

Cited by 39 publications

References 45 publications

Numerical analysis of mixing performance in sinusoidal microchannels based on particle motion in droplets

Numerical analysis of mixing performance in sinusoidal microchannels based on particle motion in droplets

Fluid mixing in droplet‐based microfluidics with T junction and convergent–divergent sinusoidal microchannels

Increased density and coverage uniformity of viruses on a sensor surface by using U-type, T-type, and W-type microfluidic devices

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