Experimental and model investigation of the time-dependent 2-dimensional distribution of binding in a herringbone microchannel.

A microfluidic device known to mix bulk solutions, the herringbone microchannel, was incorporated into a surface-binding assay to determine if the recirculation of solution altered the binding of a model protein (streptavidin) to the surface. Streptavidin solutions were pumped over surfaces functionalized with its ligand, biotin, and the binding of streptavidin to those surfaces was monitored using surface plasmon resonance imaging. Surface binding was compared between a straight microchannel and herringbone microchannels in which the chevrons were oriented with and against the flow direction. A 3-dimensional finite-element model of the surface binding reaction was developed for each of the geometries and showed strong qualitative agreement with the experimental results. Experimental and model results indicated that the forward and reverse herringbone microchannels substantially altered the distribution of protein binding (2-dimensional binding profile) as a function of time when compared to a straight microchannel. Over short distances (less than 1.5 mm) down the length of the microchannel, the model predicted no additional protein binding in the herringbone microchannel compared to the straight microchannel, consistent with previous findings in the literature.

Experiment and simulation of laminar and turbulent ferrofluid pipe flow in an oscillating magnetic field.

Laminar and turbulent pipe flow of a ferrofluid with an imposed linearly polarized, oscillating, magnetic field is examined here. Experimental results show a fractional pressure drop dependence on flow rate, magnetic field strength, and oscillation frequency. Calculations are presented, which show that ferrofluid theory can explain the flow phenomena in laminar and turbulent pipe flow. The model requires an initial fit of key parameters but then shows predictive capability in both laminar and turbulent flow. Simulation results are found to be essentially independent of the spin boundary condition due to an approximation of spin viscosity that is very small. A low Reynolds number k-epsilon model is used to model the turbulent pipe flow.

Turbulence in ferrofluids in channel flow with steady and oscillating magnetic fields.

The turbulent flow of a ferrofluid in channel flow is studied using direct numerical simulation. The method of analysis is an extension of that used for Newtonian fluids, with additional features necessary to model the ferrofluid. The analysis is applied to low Reynolds number turbulence in the range of existing experimental data in a capillary. For steady and oscillating magnetic fields, comparisons are made between a Newtonian fluid and a ferrofluid by comparing the pressure drop, turbulent Reynolds number, turbulent kinetic energy (k), Reynolds stress, velocity, and spin profiles. The results are also compared with predictions of a k-ɛ model to show the accuracy of that model when applied to ferrofluids, where ɛ is the rate of viscous dissipation of turbulent kinetic energy.

Effects of an oscillating magnetic field on homogeneous ferrofluid turbulence.

This paper presents the results from direct numerical simulations of homogeneous ferrofluid turbulence with a spatially uniform, applied oscillating magnetic field. Due to the strong coupling that exists between the magnetic field and the ferrofluid, we find that the oscillating field can affect the characteristics of the turbulent flow. The magnetic field does work on the turbulent flow and typically leads to an increased rate of energy loss via two dissipation modes specific to ferrofluids. However, under certain conditions this magnetic work results in injection, or a forcing, of turbulent kinetic energy into the flow. For the cases considered here, there is no mean shear and the mean components of velocity, vorticity, and particle spin rate are all zero. Thus, the effects shown are entirely due to the interactions between the turbulent fluctuations of the ferrofluid and the magnetic field. In addition to the effects of the oscillation frequency, we also investigate the effects of the choice of magnetization equation. The calculations focus on the approximate centerline conditions of the relatively low Reynolds number turbulent ferrofluid pipe flow experiments described previously [K. R. Schumacher, Phys. Rev. E 67, 026308 (2003)].

Concentration gradient immunoassay. 2. Computational modeling for analysis and optimization.

A novel microfluidic surface-based competition immunoassay, termed the concentration gradient immunoassay (described in detail in a companion paper (Nelson, K.; Foley, J.; Yager, P. Anal. Chem. 2007, 79, 3542-3548.) uses surface plasmon resonance (SPR) imaging to rapidly measure the concentration of small molecules. To conduct this assay, antibody and analyte are introduced into the two inlets of a T-sensor (Weigl, B. H.; Yager, P. Science 1999, 283, 346-347. Kamholz, A. E.; Weigl, B. H.; Finlayson, B. A.; Yager, P. Anal. Chem. 1999, 71, 5340-5347). Several millimeters downstream, antibody molecules with open binding sites can bind to a surface functionalized with immobilized antigen. This space- and time-dependent binding can be sensitively observed using SPR imaging. In this paper, we describe a complex three-dimensional finite element model developed to better understand the dynamic processes occurring with this assay. The model shows strong qualitative agreement with experimental results for small-molecule detection. The model confirms the experimental finding that the position within the microchannel at which the antibody binds to the immobilized analyte may be used to quantify the concentration of analyte in the sample. In addition, the model was used to explore the sensitivity of assay performance to parameters such as antibody and analyte concentrations, thereby giving insight into ways to optimize analysis speed and accuracy. Given the experimental verification of the computational results, this model serves as an efficient method to explore the influence of the flow rate, microchannel dimensions, and antibody concentration on the sensitivity of the assay.