Droplet generation in microchannels with numerical analysis and machine learning

Le résumé fourni par la source

Droplet microfluidic technology has been extensively utilized in DNA, protein, enzyme analysis, and drug delivery, enabling precise droplet control for biomedical research. While droplet generation depends critically on microfluidic chip architecture and material properties, this study investigates structural factors (channel inlet angle and wall wettability) and fluidic parameters (dynamic viscosity, interfacial tension, and capillary number) through advanced numerical simulations. We found that increased interfacial tension enlarges droplet diameter and prolongs generation time. Larger channel angles expand droplet volume due to enhanced spatial accommodation. The hydrophobic surfaces reduce droplet size and accelerate detachment. To synthesize these complex interactions, we pioneer a physics-inspired neural network that integrates simulation-derived data with physical laws. This model achieves predictive accuracy (R2 = 0.861) for droplet diameter under multifactor variations. Our findings offer valuable insights into the droplet generation process within confined spaces, which could inform the design and optimization of microfluidic chip structures.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.