Lagrangian analysis of consecutive images: Quantification of mixing processes in drops moving in a microchannel

Han, Hye-Jin; Furst, Eric M.; Kim, Chongyoup

doi:10.1007/s00397-014-0769-z

Cited by 10 publications

(12 citation statements)

References 33 publications

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“…Their results also demonstrated the dependence of mixing efficiency on the initial distributions of the reagents. 28 Nowadays, numerical simulations have provided an alternative way to understand the fundamental fluid dynamics. While the majority of the numerical studies have been carried out to understand the hydrodynamics of the slug formation process, 29,30 there are limited reports discussing about the mixing process inside slugs.…”

Section: Introductionmentioning

confidence: 99%

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Computational investigations of the mixing performance inside liquid slugs generated by a microfluidic T-junction

Reddy

Kumar

et al. 2014

Biomicrofluidics

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Droplet-based microfluidics has gained extensive research interest as it overcomes several challenges confronted by conventional single-phase microfluidics. The mixing performance inside droplets/slugs is critical in many applications such as advanced material syntheses and in situ kinetic measurements. In order to understand the effects of operating conditions on the mixing performance inside liquid slugs generated by a microfluidic T-junction, we have adopted the volume of fluid method coupled with the species transport model to study and quantify the mixing efficiencies inside slugs. Our simulation results demonstrate that an efficient mixing process is achieved by the intimate collaboration of the twirling effect and the recirculating flow. Only if the reagents are distributed transversely by the twirling effect, the recirculating flow can bring in convection mechanism thus facilitating mixing. By comparing the mixing performance inside slugs at various operating conditions, we find that slug size plays the key role in influencing the mixing performance as it determines the amount of fluid to be distributed by the twirling effect. For the cases where short slugs are generated, the mixing process is governed by the fast convection mechanism because the twirling effect can distribute the fluid to the flow path of the recirculating flow effectively. For cases with long slugs, the mixing process is dominated by the slow diffusion mechanism since the twirling effect is insufficient to distribute the large amount of fluid. In addition, our results show that increasing the operating velocity has limited effects on improving the mixing performance. This study provides the insight of the mixing process and may benefit the design and operations of droplet-based microfluidics.

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Section: Introductionmentioning

confidence: 99%

“…24,28 In this study, we have adopted numerical simulations to gain deep insights of the dynamic mixing process inside slugs. The aim of this study is to understand how the normal operating conditions such as operating velocity and flow ratios influence slug sizes as well as the mixing performance.…”

Section: Introductionmentioning

confidence: 99%

Computational investigations of the mixing performance inside liquid slugs generated by a microfluidic T-junction

Reddy

Kumar

et al. 2014

Biomicrofluidics

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“…Additionally, we developed methods to quantify the distribution of cells throughout implants by adapting means for analyzing the distribution of particles within a field of view (57,58). These methods offer a quantitative means to validate AC-DC process control and repeatability and compare and potentially match the designed cellularity of printed implants to native tissue.…”

Section: Subhead 3: Cell Directionality and Distributionmentioning

confidence: 99%

“…We further implemented an additional method to quantify the distribution of cells with a uniformity measure U based on Shannon entropy (57,58). Briefly, the uniformity measure U ranges from 0 to 1, where a perfectly nonuniform distribution in which cells are present in exactly one half of a field of view scores a 0, and a perfectly uniform distribution in which cells are present exactly equally throughout scores a 1.…”

Section: Subhead 3: Cell Directionality and Distributionmentioning

confidence: 99%

Assembled Cell-Decorated Collagen (AC-DC) bioprinted implants mimic musculoskeletal tissue properties and promote functional recovery

Turner

Coughenour

et al. 2021

Preprint

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Musculoskeletal tissue injuries, including the damage and rupture of ligaments and tendons, and volumetric muscle loss (VML), are exceptionally commonplace and often lead to permanent disability and deformation. We developed an advanced biomanufacturing platform producing cellularized collagen microfiber implants to facilitate functional repair and regeneration of musculoskeletal soft tissues. This Assembled Cell-Decorated Collagen (AC-DC) bioprinting process rapidly and reproducibly forms 3D implants using clinically relevant cells and strong, microfluidic extruded collagen fibers. Quantitative analysis showed that the directionality and distribution of cells throughout AC-DC implants mimic the cellular properties of native musculoskeletal tissue. AC-DC bioprinted implants further approximate or exceed the strength and stiffness of human tendons and ligaments and exceeded the properties of commonplace collagen hydrogels by orders of magnitude. The regenerative potential of AC-DC implants was also assessed in vivo in a rodent VML model. A critically sized muscle injury in the hindlimb was created and repaired, and limb torque generation potential was measured over 12 weeks. Both acellular and cellular implants were found to promote functional recovery compared to the unrepaired group, with AC-DC implants containing therapeutic muscle progenitor cells promoting the highest degree of recovery. Histological analysis and automated image processing of explanted muscle cross-sections revealed increased total muscle fiber count, median muscle fiber size, and increased cellularization for injuries repaired with cellularized implants. These studies introduce the tremendous potential of an advanced bioprinting method for generating tissue analogs with near-native biological and biomechanical properties with the potential to repair numerous challenging musculoskeletal injuries.

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“…This effect can re-orientate the initial distribution of the dispersed phase. Han et al [20] have adopted a tracking method to quantify the mixing process inside droplets in the Lagrangian reference frame. They concluded that the mixing efficiency depends on the initial distribution of solutions.…”

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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Lagrangian analysis of consecutive images: Quantification of mixing processes in drops moving in a microchannel

Cited by 10 publications

References 33 publications

Computational investigations of the mixing performance inside liquid slugs generated by a microfluidic T-junction

Computational investigations of the mixing performance inside liquid slugs generated by a microfluidic T-junction

Assembled Cell-Decorated Collagen (AC-DC) bioprinted implants mimic musculoskeletal tissue properties and promote functional recovery

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

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