A machine learning-based framework to design capillary-driven networks.

We present a novel approach for the design of capillary-driven microfluidic networks using a machine learning genetic algorithm (ML-GA). This strategy relies on a user-friendly 1D numerical tool specifically developed to generate the necessary data to train the ML-GA. This 1D model was validated using analytical results issued from a -shaped capillary network and experimental data.

Capillary Flow Dynamics in Composite Rectangular Microchannels with Rough Walls.

In this article, we consider rectangular microchannels composed of glass and thin polymeric walls with different roughness in which opposed walls are of the same material but adjacent walls are not. We propose a model for fluid capillary transport into these rectangular microchannels when horizontally positioned and focus our research on how the microchannel aspect ratio modifies the motion during the initial viscous regimes. The model relies on an effective static contact angle and an effective friction coefficient that averages local magnitudes in the cross section.

Three-dimensional confocal Raman temperature characterization of electrokinetically pumped microchannels.

A novel method for noninvasive, three-dimensional temperature characterization in microfluidic devices is presented. A specially designed confocal microscope was built and used to measure water temperature by sensing the Raman spectrum variations of the liquid. This is achieved by splitting the spectrum in the isosbestic point and detecting it with two photon counters.