2015
DOI: 10.1063/1.4932305
|View full text |Cite
|
Sign up to set email alerts
|

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

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 … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
11
0

Year Published

2017
2017
2025
2025

Publication Types

Select...
8
1

Relationship

0
9

Authors

Journals

citations
Cited by 18 publications
(11 citation statements)
references
References 25 publications
0
11
0
Order By: Relevance
“…17 The geometry, architecture and shape of the walls that make up the pore can signicantly impact in microuidics especially in the ow distribution and thereby inuence cells transport within the network through of micropore. 43 The quality of etched surfaces was examined by Scanning Electron Microscopy (SEM). Fig.…”
Section: Resultsmentioning
confidence: 99%
“…17 The geometry, architecture and shape of the walls that make up the pore can signicantly impact in microuidics especially in the ow distribution and thereby inuence cells transport within the network through of micropore. 43 The quality of etched surfaces was examined by Scanning Electron Microscopy (SEM). Fig.…”
Section: Resultsmentioning
confidence: 99%
“…12 Sun et al developed a method which does not require active valving and instead relies on sequential dilution to form discrete gradients, 9 followed by compartmentalization using oil. Guermonprez et al 8 leveraged the concentration gradient formed by a Yjunction 13 as the source for a network of chambers which were also compartmentalized using oil. Using a computational model, they demonstrated that the concentration gradient can be controlled by flow parameters.…”
mentioning
confidence: 99%
“…In this class, the chemical gradient of a single reagent can be generated in several micron areas in a continuous manner. So, all of the possible values of the chemical gradient can be generated, and their corresponding effects on the sample are observable [28,29,24].…”
Section: Introductionmentioning
confidence: 99%