A “twisted” microfluidic mixer suitable for a wide range of flow rate applications

Sivashankar, Shilpa; Agambayev, Sumeyra; Mashraei, Yousof; Li, Erqiang; Thoroddsen, Sigurður T.; Salama, Khaled N.

doi:10.1063/1.4954812

Cited by 50 publications

(38 citation statements)

References 52 publications

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“…In practice, the actual constant value of I * does not necessarily equal 0.5, depending instead on the measured pixel intensity of a premixed solution. To incorporate this into the calculation of mixing index, the following equation has been proposed for HSI images:

italicMixing index = 1 - \sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(\frac{((), {\overset{false¯}{I}}_{italicHSI, i} - {\overset{false¯}{I}}_{italicHSI, italicmix})}{({\overset{I}{true}}_{HSI, unmix} - {\overset{I}{true}}_{HSI, mix})})}^{2}},

where

\overset{I}{true}

HSI ,mix is the normalized premixed pixel intensity,

\overset{I}{true}

HSI ,unmix is the normalized pixel intensity prior to mixing, and

\overset{I}{true}

HSI ,i is

{\overset{false¯}{I}}_{italicHSI, normali} = \frac{((), I_{italicHSI, i} - I_{italicHSI, italicmin})}{((), I_{italicHSI, italicmax} - I_{italicHSI, italicmin})},

where

\overset{I}{true}

HSI ,i is the normalized pixel intensity inside the image, whereas I HSI, min and I gray, max are the minimum and maximum local pixel intensity, respectively, and I HSI, i is

I_{HSI, normali} = ((), normalR + normalG + normalB) / 3 .

…”

Section: Introductionmentioning

confidence: 99%

“…4 The mixing performance of these micromixers is generally characterized as a function of mixing index. On the basis of color changes, the mixing index has been employed to characterize mixing between dye colored liquids [5][6][7] , reaction between chemical that produces different colored species 8 , reaction between pH indicators [9][10][11] , and dilution of fluorescent liquids. 12,13 Several quantitative mixing indices have been defined based on intensity images recorded using typical digital video microscopes.…”

Section: Introductionmentioning

confidence: 99%

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Method for determining mixing index in microfluidics by RGB color model

Mahmud

Tamrin

2020

Asia-Pacific J Chem Eng

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Microfluidic mixing is a key process in miniaturized analysis system. Achieving adequate mixing performance is considerably difficult in microfluidic micromixer, as the flow is always associated with unfavorable laminar flow and dominated by molecular diffusion. The mixing performance of these micromixers are generally characterized as a function of mixing index based on dispersion (homogeneity) information, leading to either an overestimated or underestimated mixing index. This paper presents a new method for determining the mixing index of micromixers based on RGB color model by decoding mixing images to their respective red, green, and blue pixel intensities. Several digital composite images were used to perform initial benchmarking, and the proposed method accurately quantified the mixing index significantly better than previously adopted methods. The practicality of the method was further demonstrated by characterizing mixing of well‐known micromixers, namely T‐ and Y‐ micromixers, at varying Reynolds numbers of 5 ≤ Re ≤ 100. The results show that the mixing index decreases with the increase in Reynolds number, whereas the mixing index of the T‐micromixer was superior to that of Y‐micromixer, both agreeing well with the literature. The mixing index of these micromixers at varying Reynolds numbers of 5 ≤ Re ≤ 100 calculated using other methods were also compared and discussed. The proposed method is foreseen handy and robust in characterizing mixing in real time for gradient mixing in networked microchannels and multivortex mixing for the manipulation of fluids, particles, and biological substances.

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italicMixing index = 1 - \sqrt{\frac{1}{N} \sum_{i = 1}^{N} {(\frac{((), {\overset{false¯}{I}}_{italicHSI, i} - {\overset{false¯}{I}}_{italicHSI, italicmix})}{({\overset{I}{true}}_{HSI, unmix} - {\overset{I}{true}}_{HSI, mix})})}^{2}},

where

\overset{I}{true}

HSI ,mix is the normalized premixed pixel intensity,

\overset{I}{true}

HSI ,unmix is the normalized pixel intensity prior to mixing, and

\overset{I}{true}

HSI ,i is

{\overset{false¯}{I}}_{italicHSI, normali} = \frac{((), I_{italicHSI, i} - I_{italicHSI, italicmin})}{((), I_{italicHSI, italicmax} - I_{italicHSI, italicmin})},

where

\overset{I}{true}

HSI ,i is the normalized pixel intensity inside the image, whereas I HSI, min and I gray, max are the minimum and maximum local pixel intensity, respectively, and I HSI, i is

I_{HSI, normali} = ((), normalR + normalG + normalB) / 3 .

…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Method for determining mixing index in microfluidics by RGB color model

Mahmud

Tamrin

2020

Asia-Pacific J Chem Eng

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“…Such vortex shedding, whose frequency f —as characterized by a Strouhal number

S r \equiv f L / U

, in which

L

is the characteristic length scale of the posts and

U

the mean velocity of the flow between them—increases with a decrease in the feature aspect ratio and increasing longitudinal confinement of the flow between the posts, i.e., smaller pitch‐to‐post diameter ratios, and was shown to be effective not only to sufficiently permeabilize cell membranes in order to deliver an exogenous payload comprising various therapeutic molecules into the cell to aid transfection, but to also lyse the cells . Additionally, topology has also been combined with geometrical features to enhance vorticity generation in microchannels …”

Section: Passive Actuationmentioning

confidence: 99%

“…• Wall protrusions [49] • Grooves [160,161] • Posts -Flow deformation [50] -Vortex shedding [51,52] Combined with geometrical features [53][54][55] Simple, effective, and potentially low-cost Considerable pressure drop necessitates large benchtop pumps to drive the flow Not possible to tune centrifugation intensity independent of flow for a given device Dead volumes around corners in cavities, chambers, and sharp bends/turns can lead to particle deposition and fouling…”

Section: Geometrymentioning

confidence: 99%

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On‐Chip Generation of Vortical Flows for Microfluidic Centrifugation

Ahmed

Ramesan

Lee

et al. 2019

Small

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Microcentrifugation constitutes an important part of the microfluidic toolkit in a similar way that centrifugation is crucial to many macroscopic procedures, given that micromixing, sample preconcentration, particle separation, component fractionation, and cell agglomeration are essential operations in small scale processes. Yet, the dominance of capillary and viscous effects, which typically tend to retard flow, over inertial and gravitational forces, which are often useful for actuating flows and hence centrifugation, at microscopic scales makes it difficult to generate rotational flows at these dimensions, let alone with sufficient vorticity to support efficient mixing, separation, concentration, or aggregation. Herein, the various technologies—both passive and active—that have been developed to date for vortex generation in microfluidic devices are reviewed. Various advantages or limitations associated with each are outlined, in addition to highlighting the challenges that need to be overcome for their incorporation into integrated microfluidic devices.

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Subtractive Manufacturing of Microfluidic 3D Braid Mixers

2018

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Subtractive manufacturing using the thread template approach is an emerging technique for rapid prototyping of complex, true 3D microfluidic systems, offering advantages to photolithography in terms of process complexity, and achieving excellent structure fidelity compared to 3D printing and micro-milling. This paper explores subtractive manufacturing for novel high-performance microfluidic 3D braid mixers. The broad freedom of design with subtractive manufacturing, that is achievable with minimal process complexity is exploited to study the impact of design parameters of such braid mixers on mixing performance. The braid mixer is compared by computational fluid dynamics and experimentally with a number of existing mixer designs using colorimetric chemical reactions. The use of a braid mixer in a streamlined production of nanomaterials in a multistep procedure, shows highly competitive performance compared to other microfluidic designs and nanoparticle assembly methods.

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A “twisted” microfluidic mixer suitable for a wide range of flow rate applications

Cited by 50 publications

References 52 publications

Method for determining mixing index in microfluidics by RGB color model

Method for determining mixing index in microfluidics by RGB color model

On‐Chip Generation of Vortical Flows for Microfluidic Centrifugation

Subtractive Manufacturing of Microfluidic 3D Braid Mixers

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