Computer design of microfluidic mixers for protein/RNA folding studies
UMass Chan AffiliationsDepartment of Biochemistry and Molecular Pharmacology
Simulation and modeling
Amino Acids, Peptides, and Proteins
Biochemistry, Biophysics, and Structural Biology
Nucleic Acids, Nucleotides, and Nucleosides
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AbstractKinetic studies of biological macromolecules increasingly use microfluidic mixers to initiate and monitor reaction progress. A motivation for using microfluidic mixers is to reduce sample consumption and decrease mixing time to microseconds. Some applications, such as small-angle x-ray scattering, also require large ( > 10 micron) sampling areas to ensure high signal-to-noise ratios and to minimize parasitic scattering. Chaotic to marginally turbulent mixers are well suited for these applications because this class of mixers provides a good middle ground between existing laminar and turbulent mixers. In this study, we model various chaotic to marginally turbulent mixing concepts such as flow turning, flow splitting, and vortex generation using computational fluid dynamics for optimization of mixing efficiency and observation volume. Design iterations show flow turning to be the best candidate for chaotic/marginally turbulent mixing. A qualitative experimental test is performed on the finalized design with mixing of 10 M urea and water to validate the flow turning unsteady mixing concept as a viable option for RNA and protein folding studies. A comparison of direct numerical simulations (DNS) and turbulence models suggests that the applicability of turbulence models to these flow regimes may be limited.
PLoS One. 2018 Jun 20;13(6):e0198534. doi: 10.1371/journal.pone.0198534. eCollection 2018. Link to article on publisher's site
Permanent Link to this Itemhttp://hdl.handle.net/20.500.14038/40703
RightsCopyright: © 2018 Inguva et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Except where otherwise noted, this item's license is described as Copyright: © 2018 Inguva et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.