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Accurate flow in augmented networks (AFAN): an approach to generating three-dimensional biomimetic microfluidic networks with controlled flow

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Abstract

In vivo, microvasculature provides oxygen, nutrients, and soluble factors necessary for cell survival and function. The highly tortuous, densely-packed, and interconnected three-dimensional (3D) architecture of microvasculature ensures that cells receive these crucial components. The ability to duplicate microvascular architecture in tissue-engineered models could provide a means to generate large-volume constructs as well as advanced microphysiological systems. Similarly, the ability to induce realistic flow in engineered microvasculature is crucial to recapitulating in vivo-like flow and transport. Advanced biofabrication techniques are capable of generating 3D, biomimetic microfluidic networks in hydrogels, however, these models can exhibit systemic aberrations in flow due to incorrect boundary conditions. To overcome this problem, we developed an automated method for generating synthetic augmented channels that induce the desired flow properties within three-dimensional microfluidic networks. These augmented inlets and outlets enforce the appropriate boundary conditions for achieving specified flow properties and create a three-dimensional output useful for image-guided fabrication techniques to create biomimetic microvascular networks.

Graphical abstract: Accurate flow in augmented networks (AFAN): an approach to generating three-dimensional biomimetic microfluidic networks with controlled flow

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Publication details

The article was received on 13 Aug 2018, accepted on 30 Aug 2018 and first published on 03 Dec 2018


Article type: Paper
DOI: 10.1039/C8AY01798K
Citation: Anal. Methods, 2019, Advance Article
  • Open access: Creative Commons BY license
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    Accurate flow in augmented networks (AFAN): an approach to generating three-dimensional biomimetic microfluidic networks with controlled flow

    J. Guo, K. A. Keller, P. Govyadinov, P. Ruchhoeft, J. H. Slater and D. Mayerich, Anal. Methods, 2019, Advance Article , DOI: 10.1039/C8AY01798K

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