Literature DB >> 26487905

Flow distribution in parallel microfluidic networks and its effect on concentration gradient.

Cyprien Guermonprez1, Sébastien Michelin1, Charles N Baroud1.   

Abstract

The architecture of microfluidic networks can significantly impact the flow distribution within its different branches and thereby influence tracer transport within the network. In this paper, we study the flow rate distribution within a network of parallel microfluidic channels with a single input and single output, using a combination of theoretical modeling and microfluidic experiments. Within the ladder network, the flow rate distribution follows a U-shaped profile, with the highest flow rate occurring in the initial and final branches. The contrast with the central branches is controlled by a single dimensionless parameter, namely, the ratio of hydrodynamic resistance between the distribution channel and the side branches. This contrast in flow rates decreases when the resistance of the side branches increases relative to the resistance of the distribution channel. When the inlet flow is composed of two parallel streams, one of which transporting a diffusing species, a concentration variation is produced within the side branches of the network. The shape of this concentration gradient is fully determined by two dimensionless parameters: the ratio of resistances, which determines the flow rate distribution, and the Péclet number, which characterizes the relative speed of diffusion and advection. Depending on the values of these two control parameters, different distribution profiles can be obtained ranging from a flat profile to a step distribution of solute, with well-distributed gradients between these two limits. Our experimental results are in agreement with our numerical model predictions, based on a simplified 2D advection-diffusion problem. Finally, two possible applications of this work are presented: the first one combines the present design with self-digitization principle to encapsulate the controlled concentration in nanoliter chambers, while the second one extends the present design to create a continuous concentration gradient within an open flow chamber.

Entities:  

Year:  2015        PMID: 26487905      PMCID: PMC4600080          DOI: 10.1063/1.4932305

Source DB:  PubMed          Journal:  Biomicrofluidics        ISSN: 1932-1058            Impact factor:   2.800


  21 in total

1.  Droplet traffic at a simple junction at low capillary numbers.

Authors:  Wilfried Engl; Matthieu Roche; Annie Colin; Pascal Panizza; Armand Ajdari
Journal:  Phys Rev Lett       Date:  2005-11-11       Impact factor: 9.161

2.  A microfluidic chemostat for experiments with bacterial and yeast cells.

Authors:  Alex Groisman; Caroline Lobo; HoJung Cho; J Kyle Campbell; Yann S Dufour; Ann M Stevens; Andre Levchenko
Journal:  Nat Methods       Date:  2005-09       Impact factor: 28.547

3.  Coding/decoding and reversibility of droplet trains in microfluidic networks.

Authors:  Michael J Fuerstman; Piotr Garstecki; George M Whitesides
Journal:  Science       Date:  2007-01-04       Impact factor: 47.728

4.  Microfluidic traps for hard-wired operations on droplets.

Authors:  Piotr M Korczyk; Ladislav Derzsi; Sławomir Jakieła; Piotr Garstecki
Journal:  Lab Chip       Date:  2013-08-22       Impact factor: 6.799

5.  Signal processing by the HOG MAP kinase pathway.

Authors:  Pascal Hersen; Megan N McClean; L Mahadevan; Sharad Ramanathan
Journal:  Proc Natl Acad Sci U S A       Date:  2008-05-14       Impact factor: 11.205

6.  A modular cell culture device for generating arrays of gradients using stacked microfluidic flows.

Authors:  Christopher G Sip; Nirveek Bhattacharjee; Albert Folch
Journal:  Biomicrofluidics       Date:  2011-06-29       Impact factor: 2.800

7.  A microdroplet dilutor for high-throughput screening.

Authors:  Xize Niu; Fabrice Gielen; Joshua B Edel; Andrew J deMello
Journal:  Nat Chem       Date:  2011-06       Impact factor: 24.427

8.  Microfluidic system for measuring neutrophil migratory responses to fast switches of chemical gradients.

Authors:  Daniel Irimia; Su-Yang Liu; William G Tharp; Azadeh Samadani; Mehmet Toner; Mark C Poznansky
Journal:  Lab Chip       Date:  2005-12-23       Impact factor: 6.799

9.  Self-digitization of sample volumes.

Authors:  Dawn E Cohen; Thomas Schneider; Michelle Wang; Daniel T Chiu
Journal:  Anal Chem       Date:  2010-07-01       Impact factor: 6.986

10.  Rails and anchors: guiding and trapping droplet microreactors in two dimensions.

Authors:  Paul Abbyad; Rémi Dangla; Antigoni Alexandrou; Charles N Baroud
Journal:  Lab Chip       Date:  2010-11-09       Impact factor: 6.799

View more
  1 in total

1.  Hybrid microchannel-solid state micropore device for fast and optical cell detection.

Authors:  Carol M Olmos; Gustavo Rosero; Tamara Fernández-Cabada; Ross Booth; Manuel Der; Juan M Cabaleiro; Alexis Debut; Luis Cumbal; Maximiliano S Pérez; Betiana Lerner
Journal:  RSC Adv       Date:  2020-02-03       Impact factor: 4.036

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.