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DDC: 570 × Subject: Microfluidics ×

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Now showing 1 - 3 of 3
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    Switchable Adhesion Surfaces with Enhanced Performance Against Rough Counterfaces
    (Basel : MDPI, 2016) Prieto-López, Lizbeth; Williams, John
    In a recent study, we demonstrated that the pressurization of micro-fluidic features introduced in the subsurface of a soft polymer can be used to actively modify the magnitude of the adhesion to a harder counterface by changing its waviness or long wavelength undulations. In that case, both contacting surfaces had very smooth finishes with root-mean-square roughnesses of less than 20 nm. These values are far from those of many engineering surfaces, which usually have a naturally occurring roughness of between ten and a hundred times this value. In this work, we demonstrate that appropriate surface features, specifically relatively slender “fibrils”, can enhance the ability of a such a soft surface to adhere to a hard, but macroscopically rough, counterface, while still maintaining the possibility of switching the adhesion force from one level to another. Conversely, stiffer more conical surface features can suppress adhesion even against a smooth counterface. Examples of each form of topography can be found in the natural world.
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    A customizable microfluidic platform for medium-throughput modeling of neuromuscular circuits
    (Amsterdam [u.a.] : Elsevier Science, 2019) Bellmann, Jessica; Goswami, Ruchi Y.; Girardo, Salvatore; Rein, Nelly; Hosseinzadeh, Zohreh; Hicks, Michael R.; Busskamp, Volker; Pyle, April D.; Werner, Carsten; Sterneckert, Jared
    Neuromuscular circuits (NMCs) are vital for voluntary movement, and effective models of NMCs are needed to understand the pathogenesis of, as well as to identify effective treatments for, multiple diseases, including Duchenne's muscular dystrophy and amyotrophic lateral sclerosis. Microfluidics are ideal for recapitulating the central and peripheral compartments of NMCs, but myotubes often detach before functional NMCs are formed. In addition, microfluidic systems are often limited to a single experimental unit, which significantly limits their application in disease modeling and drug discovery. Here, we developed a microfluidic platform (MFP) containing over 100 experimental units, making it suitable for medium-throughput applications. To overcome detachment, we incorporated a reactive polymer surface allowing customization of the environment to culture different cell types. Using this approach, we identified conditions that enable long-term co-culture of human motor neurons and myotubes differentiated from human induced pluripotent stem cells inside our MFP. Optogenetics demonstrated the formation of functional NMCs. Furthermore, we developed a novel application of the rabies tracing assay to efficiently identify NMCs in our MFP. Therefore, our MFP enables large-scale generation and quantification of functional NMCs for disease modeling and pharmacological drug targeting. © 2019 The Authors
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    Microfluidic Network Simulations Enable On-Demand Prediction of Control Parameters for Operating Lab-on-a-Chip-Devices
    (Basel : MDPI AG, 2021) Böke, Julia Sophie; Kraus, Daniel; Henkel, Thomas
    Reliable operation of lab-on-a-chip systems depends on user-friendly, precise, and predictable fluid management tailored to particular sub-tasks of the microfluidic process protocol and their required sample fluids. Pressure-driven flow control, where the sample fluids are delivered to the chip from pressurized feed vessels, simplifies the fluid management even for multiple fluids. The achieved flow rates depend on the pressure settings, fluid properties, and pressure-throughput characteristics of the complete microfluidic system composed of the chip and the interconnecting tubing. The prediction of the required pressure settings for achieving given flow rates simplifies the control tasks and enables opportunities for automation. In our work, we utilize a fast-running, Kirchhoff-based microfluidic network simulation that solves the complete microfluidic system for in-line prediction of the required pressure settings within less than 200 ms. The appropriateness of and benefits from this approach are demonstrated as exemplary for creating multi-component laminar co-flow and the creation of droplets with variable composition. Image-based methods were combined with chemometric approaches for the readout and correlation of the created multi-component flow patterns with the predictions obtained from the solver.