Geometric Effect on Gas–Liquid Bubbly Flow in Capillary-Embedded T-Junction Microchannels

Microfluidic Monodispersed Microbubble Generation for Production of Cavitation Nuclei

Microbubbles, acting as cavitation nuclei, undergo cycles of expansion, contraction, and collapse. This collapse generates shockwaves, alters local shear forces, and increases local temperature. Cavitation causes severe changes in pressure and temperature, resulting in surface erosion. Shockwaves strip material from surfaces, forming pits and cracks. Prolonged cavitation reduces the mechanical strength and fatigue life of materials, potentially leading to failure. Controlling bubble size and generating monodispersed bubbles is crucial for accurately modeling cavitation phenomena. In this work, we generate monodispersed microbubbles with controllable size using a novel and low-cost microfluidic method. We created an innovative T-junction structure that controls the two-phase flow for tiny, monodispersed bubble generation. Monodisperse microbubbles with diameters below one-fifth of the channel width (W = 100 µm) are produced due to the controlled pressure gradient. This microstructure, fabricated by a CNC milling technique, produces 20 μm bubbles without requiring high-resolution equipment and cleanroom environments. Bubble size is controlled with gas and liquid pressure ratio and microgeometry. This microbubble generation method provides a controllable and reproducible way for cavitation research.

A general neural network model co-driven by mechanism and data for the reliable design of gas-liquid T-junction microdevices

In recent years, many models have been developed to describe the gas-liquid microdispersion process, which mainly rely on mechanistic analysis and may not be universally applicable. In order to provide a more comprehensive model and, most significantly, to provide a model for design, we have established a general database of microbubble generation in T-junction microdevices, including 854 data points from 12 pieces of literature. A neural network model that combines mechanistic and data modeling is developed. By transfer learning, more accurate results can be obtained. Additionally, we have proposed a design method that enables a relative deviation of less than 5% from the expected bubble size. A new device was designed and prepared to confirm the reliability of the method, which can prepare smaller bubbles than other common T-junction devices. In this way, a general and universal database and model are established and a design method for a gas-liquid T-junction microreactor is developed.