A simulation tool to predict the morphological features and dynamics of polymeric microdroplets in a microfluidic T-junction is presented. A phase-diagram of regimes is created moving from dripping to squeezing within ranges of 10−2–10−4 and 10−1–10−3 for Reynolds and Capillary numbers, respectively. The simulations show the strong influence of the continuous phase over the droplet size, which changes two orders of magnitude -increasing from 101 to 102 μm- as the flowrate becomes higher. The phase-diagram allows to choose the optimal fluid-flow conditions to have a precise and stable dripping production of spherical drops. Indeed, a successful down-scaling of drop size up to ∼101 μm with a drop rate production of ∼40 drops/s is obtained, with a great accordance between simulative and experimental results (error < 1 %), at high monodispersity (polydispersity index<0.05). Therefore, our tool has proved to be a powerful approach to predict and regulate polymeric microdroplet production in microfluidics.

A CFD simulation tool for experimental prediction of inflow polymeric microdroplet formation in a T-junction configuration

Battista E.;
2025-01-01

Abstract

A simulation tool to predict the morphological features and dynamics of polymeric microdroplets in a microfluidic T-junction is presented. A phase-diagram of regimes is created moving from dripping to squeezing within ranges of 10−2–10−4 and 10−1–10−3 for Reynolds and Capillary numbers, respectively. The simulations show the strong influence of the continuous phase over the droplet size, which changes two orders of magnitude -increasing from 101 to 102 μm- as the flowrate becomes higher. The phase-diagram allows to choose the optimal fluid-flow conditions to have a precise and stable dripping production of spherical drops. Indeed, a successful down-scaling of drop size up to ∼101 μm with a drop rate production of ∼40 drops/s is obtained, with a great accordance between simulative and experimental results (error < 1 %), at high monodispersity (polydispersity index<0.05). Therefore, our tool has proved to be a powerful approach to predict and regulate polymeric microdroplet production in microfluidics.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/856094
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