Fig. 7: Experimental and numerical simulation setup for capturing geometry changes in microfluidic devices due to transport and chemical reactions. | Communications Physics

Fig. 7: Experimental and numerical simulation setup for capturing geometry changes in microfluidic devices due to transport and chemical reactions.

From: Physics-based machine learning toolbox for probing concentration under diffusive regime in microfluidics devices

Fig. 7: Experimental and numerical simulation setup for capturing geometry changes in microfluidic devices due to transport and chemical reactions.The alternative text for this image may have been generated using AI.

Red arrows denote the flow of SrCl2, blue arrows denote the flow of Na2SO4, and purple arrows indicate the observation process. a Experimental setup consisting of an optical microscope, a syringe pump injecting 50 mM SrCl2 and 50 mM Na2SO4 at 50 μL/min for the chip with rock pattern and 1000 nL/min for the chip with uniform pattern, the microfluidic devices, and the outflow3. b Transport of SrCl2 and Na2SO4 within the borosilicate glass chip, referred to as the chip with rock pattern. The dashed box marks the region where SrCl2 diffuses from the top and Na2SO4 from the bottom. c Transport of SrCl2 and Na2SO4 within the polydimethylsiloxane (PDMS) chip, referred to as the chip with uniform pattern. The dashed box marks the region where SrCl2 diffuses from the top and Na2SO4 from the bottom. d Preparation for Lattice–Boltzmann simulations, in which images of the snapshot region are acquired at multiple time steps and subsequently segmented. e For each segmented image, a multi-component Lattice–Boltzmann simulation is performed using boundary conditions consistent with the experimental setup, and f A Lattice–Boltzmann simulation with a conservative tracer is also performed using fixed boundary values of 1.0 mM at the top and 0.0 mM at the bottom, from which effective diffusivity is derived. The same numerical setup is applied to the PDMS experiments.

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