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Klaus Mathwig

Publications and source records attributed to Klaus Mathwig.

3 recordsLinked to original sources

Suppressing parasitic flow in membraneless diffusion-based microfluidic gradient generators

Diffusion-based microfluidic gradient generators (DMGGs) are essential for various in-vitro studies due to their ability to provide a convection-free concentration gradient. However, these systems, often referred to as membrane-based DMGGs, exhibit delayed gradient formation due to the incorporated flow-resistant membrane. This limitation substantially hinders their application in dynamic and time-sensitive studies. Here, we accelerate the gradient response in DMGGs by removing the membrane and implementing new geometrical configurations to compensate for the membrane's role in suppressing parasitic flows. We introduce these novel configurations into two microfluidic designs: the H-junction and the Y-junction. In the H-junction design, parasitic flow is redirected through a bypass channel following the gradient region. The Y-junction design features a shared discharge channel that allows converging discharge flow streams, preventing the buildup of parasitic pressure downstream of the gradient region. Using hydraulic circuit analysis and fluid dynamics simulations, we demonstrate the effectiveness of the H-junction and Y-junction designs in suppressing parasitic pressure flows. These computational results, supported by experimental data from particle image velocimetry, confirm the capability of our designs to generate a highly stable, accurate, and convection-free gradient with rapid formation. These advantages make the H-junction and Y-junction designs ideal experimental platforms for a wide range of in-vitro studies, including drug testing, cell chemotaxis, and stem cell differentiation.

physics.flu-dyn

AI-driven random walk simulations of viscophoresis and visco-diffusiophoretic particle trapping

Viscophoresis refers to the transport of suspended nanoparticles driven by a steep viscosity gradient. This work investigates this new transport effect using a random walk simulation. By modelling position-dependent Brownian motion, viscophoresis, and diffusiophoresis in a one-dimensional geometry, the simulation yields results that align well with experimental data, demonstrating viscophoresis as a new phoretic transport mechanism. Additionally, the simulation predicts the efficient separation of nanoparticles based on size, suggesting potential applications for sorting in microfluidic systems. The Python script for the simulation was generated using ChatGPT o1, significantly accelerating model development and providing accurate physical insights and efficient equations. However, caution is advised, as ChatGPT may generate non-physical results; iterative testing and validation is important.

physics.flu-dyn

Viscophoretic particle transport

Viscosity is a fundamental property of liquids and determines the diffusivity of suspended particles. A gradient in viscosity leads to a gradient in diffusivity, yet it is unknown whether such a gradient can lead to directed transport of particles. In this work, we generate a steep, stable viscosity gradient in a microfluidic channel and image the resulting transport of suspended nanoparticles at the single-particle level using high-resolution microscopy. We observe high viscophoretic drift velocities that significantly exceed theoretical predictions. In addition, we utilize viscophoresis for a new type of particle trap. We provide a first quantification of a transport phenomenon that is of importance in any system and any application exhibiting viscosity gradients, for example in separation using membrane technology as well as in inter- and intracellular biomolecular transport.

physics.flu-dyn