Systematic modeling of microfluidic concentration gradient generators

Abstract

This paper presents a systematic modeling methodology for microfluidic concentration gradient generators. The generator is decomposed into a system of microfluidic elements with relatively simple geometries. Parameterized models for such elements are analytically developed and hold for general sample concentration profiles and arbitrary flow ratios at the element inlet; hence, they are valid for concentration gradient generators that rely on either complete or partial mixing. The element models are then linked through an appropriate set of parameters embedded at the element interfaces. This yields a systematic, lumped-parameter representation of the entire generator in terms of a network of gradient-generation elements. The system model is verified by numerical analysis and experimental data and accurately captures the overall effects of network topologies, element sizes, flow rates and reservoir sample concentrations on the generation of sample concentration gradient. Finally, this modeling methodology is applied to propose a novel and compact microfluidic device that is able to create concentration gradients of complex shapes by juxtaposing simple constituent profiles along the channel width.

Design synthesis and experimental validation of microfluidic concentration gradient generators

“…These discrepancies are attributable to both the model and experiments. On the modeling side, a large channel width-to-depth ratio is assumed [8], so the depthwise mass transfer is neglected. Furthermore, a uniform cross-sectional velocity profile is assumed based on the large-aspect-ratio assumption.…”

“…At all locations along the channel, stable transverse profiles are achieved as a result of the balance between convection and diffusion. To enable the efficient design of this type of devices for more complex concentration profiles, an analytical convection-diffusion model was proposed [8]. Although the calculation results from the analytical model were compared to numerical simulations, no systematic experimental data were available to verify the model.…”

“…Then, with the initial guess values of the parameters, the modeling subroutine calculates the output concentration profile at a specified location as well as its discrepancy from the desired profile, using the analytical model of Ref. [8]. Based on the discrepancy, the parameters are then adjusted and input into the modeling subroutine again for a second iteration of calculation.…”

End-to-End Propagation Noise and Memory Analysis for Molecular Communication over Microfluidic Channels

“…We also assume that due to very short distance in the order of ∼μm at transmitter and receiver chambers, propagation velocity by diffusion at chambers is comparable to propagation via flow in microfluidic channel. One-dimensional solution of the convection-diffusion equation is used to analyze molecular transport via flow in the microfluidic channels [13]- [17]. The concentration propagation in the microfluidic channel is illustrated in Fig.…”

“…In this section, for the linear signal model in (25), the noise effects on the received concentration signal are characterized using the autocorrelation of the corresponding impulse responses of the transmitter chamber (14), the microfluidic channel (2), and the receiver chamber (17). To this end, first, building blocks of noise autocorrelations are defined based on the propagation of the concentration signal by diffusion and at the junction transition, which are similar to thermal and shot noise effects, respectively, in electronic circuits.…”