The Fahraeus effect

Barbee, James H.; Cokelet, Giles R.

doi:10.1016/0026-2862(71)90002-1

Cited by 231 publications

(96 citation statements)

References 3 publications

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“…This pattern formation could also be used for two-dimensional self-assembly of microparticles. Besides implications for improvement of particle control in microfluidic systems, these results also have implications for blood flow near vessel walls (37,38), suggesting limits to the dimensions of "cell-free layers" (39), and cell and particle diffusion in concentrated suspensions (40).…”

Section: Resultsmentioning

confidence: 97%

Dynamic self-assembly and control of microfluidic particle crystals

Lee

Amini

Stone

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

203

228

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Engineered two-phase microfluidic systems have recently shown promise for computation, encryption, and biological processing. For many of these systems, complex control of dispersed-phase frequency and switching is enabled by nonlinearities associated with interfacial stresses. Introducing nonlinearity associated with fluid inertia has recently been identified as an easy to implement strategy to control two-phase (solid-liquid) microscale flows. By taking advantage of inertial effects we demonstrate controllable self-assembling particle systems, uncover dynamics suggesting a unique mechanism of dynamic self-assembly, and establish a framework for engineering microfluidic structures with the possibility of spatial frequency filtering. Focusing on the dynamics of the particleparticle interactions reveals a mechanism for the dynamic selfassembly process; inertial lift forces and a parabolic flow field act together to stabilize interparticle spacings that otherwise would diverge to infinity due to viscous disturbance flows. The interplay of the repulsive viscous interaction and inertial lift also allow us to design and implement microfluidic structures that irreversibly change interparticle spacing, similar to a low-pass filter. Although often not considered at the microscale, nonlinearity due to inertia can provide a platform for high-throughput passive control of particle positions in all directions, which will be useful for applications in flow cytometry, tissue engineering, and metamaterial synthesis.inertial ordering | hydrodynamic interaction | particle-laden flow | defocusing | microfluidics

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Section: Resultsmentioning

confidence: 97%

Dynamic self-assembly and control of microfluidic particle crystals

Lee

Amini

Stone

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

203

228

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“…Briefly, the bovine blood was first centrifuged at 500 Â g and the plasma was decanted. Phosphatebuffered saline (Thermo Fisher Scientific, Waltham, MA) was added to the blood to adjust it to a physiologically relevant 35% hematocrit (40,41). This process was repeated twice to ensure a thorough washing.…”

Section: Methodsmentioning

confidence: 99%

Direct Tracking of Particles and Quantification of Margination in Blood Flow

Carboni

Bognet

Bouchillon

et al. 2016

Biophysical Journal

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Margination refers to the migration of particles toward blood vessel walls during blood flow. Understanding the mechanisms that lead to margination will aid in tailoring the attributes of drug-carrying particles for effective drug delivery. Most previous studies evaluated the margination propensity of these particles via an adhesion mechanism, i.e., by measuring the number of particles that adhered to the channel wall. Although particle adhesion and margination are related, adhesion also depends on other factors. In this study, we quantified the margination propensity of particles of varying diameters (0.53, 0.84, and 2.11 mm) and apparent wall shear rates (30, 61, and 121 s À1 ) by directly tracking fluorescent particles flowing through a microfluidic channel. The margination parameter, M, is defined as the total number of particles found within the cell-free layers normalized by the total number of particles that passed through the channel. In this study, an M-value of 0.2 indicated no margination, which was observed for all particle sizes in water. In the case of blood, larger particles were found to have higher M-values and thus marginated more effectively than smaller particles. The corresponding M-values at the device outlet were 0.203, 0.223, and 0.285 for 0.53-, 0.84-, and 2.11-mm particles, respectively. At the inlet, the M-values for all particle sizes in blood were <0.2, suggesting that non-fully-developed flow and constriction may lead to demargination. For particle velocities transverse to the flow direction (v y ), all particle sizes showed a larger standard deviation of v y as well as a higher effective diffusivity when the particles were suspended in blood relative to water. These higher values are attributed to collisions between the blood cells and particles, further supporting recent simulation results. In terms of flow rates, for a given particle size, the higher the flow rate, the higher the M-value.

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“…The capillary hematocrits (Hct cap ) are lower than the large-vessel hematocrits (Hct) by the Fahraeus-Lindquist effect 3,32,43 and consequently the RBC velocity (ν rbc ) is higher than the plasma velocity (ν pl ), and the erythrocytes' mean transit times through the coronary system is shorter than that of plasma. 9 The result is a "red cell carriage effect" 33 whereby solutes carried within RBCs travel faster than plasma proteins.…”

Section: Equations For Capillary Hematocrits and Rbc And Plasma Volumesmentioning

confidence: 99%

“…One mole of Hb can bind with four moles of O 2 and one mole of Mb can bind with one mole of O 2 , so the concentrations of Hb-and Mb-bound O 2 (C HbO 2 and C MbO 2 ) can be calculated from the O 2 saturations of Hb and Mb (S HbO 2 and S MbO 2 ) using (3) where C Hbrbc and C Mbpc are the concentrations of Hb and Mb in the water spaces of RBC and parenchymal cells. With an Hb content of 0.333 g ml −1 RBC (0.15 g ml −1 blood, Hct = 0.45), an RBC water fraction of 0.72 ml ml −1 RBC, and MW Hb = 64, 458 for the tetramer, then C Hbrbc = 7.2 mM.…”

Section: Equations For C Hbo 2 and C Mbomentioning

confidence: 99%

Simultaneous Blood–Tissue Exchange of Oxygen, Carbon Dioxide, Bicarbonate, and Hydrogen Ion

Dash

Bassingthwaighte

2006

Ann Biomed Eng

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A detailed nonlinear four-region (red blood cell, plasma, interstitial fluid, and parenchymal cell) axially distributed convection-diffusion-permeation-reaction-binding computational model is developed to study the simultaneous transport and exchange of oxygen (O 2 ) and carbon dioxide (CO 2 ) in the blood-tissue exchange system of the heart. Since the pH variation in blood and tissue influences the transport and exchange of O 2 and CO 2 (Bohr and Haldane effects), and since most CO 2 is transported as (bicarbonate) via the CO 2 hydration (buffering) reaction, the transport and exchange of and H + are also simulated along with that of O 2 and CO 2 . Furthermore, the model accounts for the competitive nonlinear binding of O 2 and CO 2 with the hemoglobin inside the red blood cells (nonlinear O 2 -CO 2 interactions, Bohr and Haldane effects), and myoglobin-facilitated transport of O 2 inside the parenchymal cells. The consumption of O 2 through cytochrome-c oxidase reaction inside the parenchymal cells is based on MichaelisMenten kinetics. The corresponding production of CO 2 is determined by respiratory quotient (RQ), depending on the relative consumption of carbohydrate, protein, and fat. The model gives a physiologically realistic description of O 2 transport and metabolism in the microcirculation of the heart. Furthermore, because model solutions for tracer transients and steady states can be computed highly efficiently, this model may be the preferred vehicle for routine data analysis where repetitive solutions and parameter optimization are required, as is the case in PET imaging for estimating myocardial O 2 consumption.

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The Fahraeus effect

Cited by 231 publications

References 3 publications

Dynamic self-assembly and control of microfluidic particle crystals

Dynamic self-assembly and control of microfluidic particle crystals

Direct Tracking of Particles and Quantification of Margination in Blood Flow

Simultaneous Blood–Tissue Exchange of Oxygen, Carbon Dioxide, Bicarbonate, and Hydrogen Ion

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