Machine Learning-Aided Microdroplets Breakup Characteristic Prediction in Flow-Focusing Microdevices by Incorporating Variations of Cross-Flow Tilt Angles

Deep learning-augmented T-junction droplet generation

Droplet generation technology has become increasingly important in a wide range of applications, including biotechnology and chemical synthesis. T-junction channels are commonly used for droplet generation due to their integration capability of a larger number of droplet generators in a compact space. In this study, a finite element analysis (FEA) approach is employed to simulate droplet production and its dynamic regimes in a T-junction configuration and collect data for post-processing analysis. Next, image analysis was performed to calculate the droplet length and determine the droplet generation regime. Furthermore, machine learning (ML) and deep learning (DL) algorithms were applied to estimate outputs through examination of input parameters within the simulation range. At the end, a graphical user interface (GUI) was developed for estimation of the droplet characteristics based on inputs, enabling the users to preselect their designs with comparable microfluidic configurations within the studied range.

Sunset Yellow Confined in Curved Geometry: A Microfluidic Approach

The behavior of lyotropic chromonic liquid crystals (LCLCs) in confined environments is an interesting research field that still awaits exploration, with multiple key variables to be uncovered and understood. Microfluidics is a highly versatile technique that allows us to confine LCLCs in micrometric spheres. As microscale networks offer distinct interplays between the surface effects, geometric confinement, and viscosity parameters, rich and unique interactions emerging at the LCLC-microfluidic channel interfaces are expected. Here, we report on the behavior of pure and chiral doped nematic Sunset Yellow (SSY) chromonic microdroplets produced through a microfluidic flow-focusing device. The continuous production of SSY microdroplets with controllable size gives the possibility to systematically study their topological textures as the function of their diameters. Indeed, doped SSY microdroplets produced via microfluidics, show topologies that are typical of common chiral thermotropic liquid crystals. Furthermore, few droplets exhibit a peculiar texture never observed for chiral chromonic liquid crystals. Finally, the achieved precise control of the produced LCLC microdroplets is a crucial step for technological applications in biosensing and anticounterfeiting.