Flow switching in microfluidic networks using passive features and frequency tuning
Flow switching in microfluidic networks using passive features and frequency tuning
Manipulating fluids in microchips remains a persistent challenge in the development of inexpensive and portable point-of-care diagnostic tools. Flow in microfluidic chips can be controlled via frequency tuning, wherein the excitation frequency of a pressure source is matched with the characteristic frequencies of network branches. The characteristic frequencies of each branch arise from coupling between fluid in the channels and passive deformable features, and can be programmed by adjusting the dimensions and stiffness of the features. In contrast to quasi-static ‘on–off’ valves, such networks require only a single active element and relatively small dynamic displacements. To achieve effective flow switching between different pathways in the chip, well-separated peak frequencies and narrow bandwidths are required (such that branches are independently addressable). This paper illustrates that high selectivity can be achieved in fluidic networks that exploit fluidic inertia, with flow driven selectively at peak frequencies between 1–100 Hz with bandwidths less than 25% of the peak frequency. Precise frequency-based flow switching between two on-chip microchannels is demonstrated. A simple theoretical framework is presented that predicts the characteristic frequencies in terms of feature properties, thus facilitating the design of networks with specific activation frequencies. The approach provides a clear pathway to simplification and miniaturization of flow-control hardware for microchips with several fluidic domains.
Collino, Rachel R.
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Reilly-Shapiro, Neil
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Foresman, Bryant
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Xu, Kerui
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Utz, Marcel
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Landers, James P.
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Begley, Matthew R.
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Collino, Rachel R.
f23b3024-caa8-47a4-8220-56bbbbb1a2dd
Reilly-Shapiro, Neil
cc41be5f-685f-4421-ae84-ce96fdd8691b
Foresman, Bryant
659b03c9-8705-4d6b-b9e2-fcc140c7df20
Xu, Kerui
f3553d20-da48-4ac4-ac78-601d7db2dfb3
Utz, Marcel
c84ed64c-9e89-4051-af39-d401e423891b
Landers, James P.
f85edaa2-5c24-4f2d-be3d-c84e631833ad
Begley, Matthew R.
9f4e52bc-507a-4ef6-910f-bd11d25c2209
Collino, Rachel R., Reilly-Shapiro, Neil, Foresman, Bryant, Xu, Kerui, Utz, Marcel, Landers, James P. and Begley, Matthew R.
(2013)
Flow switching in microfluidic networks using passive features and frequency tuning.
Lab on a Chip, Summer Issue.
(doi:10.1039/C3LC50481F).
Abstract
Manipulating fluids in microchips remains a persistent challenge in the development of inexpensive and portable point-of-care diagnostic tools. Flow in microfluidic chips can be controlled via frequency tuning, wherein the excitation frequency of a pressure source is matched with the characteristic frequencies of network branches. The characteristic frequencies of each branch arise from coupling between fluid in the channels and passive deformable features, and can be programmed by adjusting the dimensions and stiffness of the features. In contrast to quasi-static ‘on–off’ valves, such networks require only a single active element and relatively small dynamic displacements. To achieve effective flow switching between different pathways in the chip, well-separated peak frequencies and narrow bandwidths are required (such that branches are independently addressable). This paper illustrates that high selectivity can be achieved in fluidic networks that exploit fluidic inertia, with flow driven selectively at peak frequencies between 1–100 Hz with bandwidths less than 25% of the peak frequency. Precise frequency-based flow switching between two on-chip microchannels is demonstrated. A simple theoretical framework is presented that predicts the characteristic frequencies in terms of feature properties, thus facilitating the design of networks with specific activation frequencies. The approach provides a clear pathway to simplification and miniaturization of flow-control hardware for microchips with several fluidic domains.
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Accepted/In Press date: 2013
e-pub ahead of print date: 26 June 2013
Organisations:
Magnetic Resonance
Identifiers
Local EPrints ID: 354752
URI: http://eprints.soton.ac.uk/id/eprint/354752
ISSN: 1473-0197
PURE UUID: 16378d27-2686-4892-90f6-1c12d1f8d8d2
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Date deposited: 22 Jul 2013 10:33
Last modified: 15 Mar 2024 03:44
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Contributors
Author:
Rachel R. Collino
Author:
Neil Reilly-Shapiro
Author:
Bryant Foresman
Author:
Kerui Xu
Author:
James P. Landers
Author:
Matthew R. Begley
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