Comparison of surfactant mass transfer with drop formation times from dynamic interfacial tension measurements in microchannels

Abstract

The developed methodology measures dynamic interfacial tension down to 3ms. Surfactant adsorption times can be significant if compared to drop formation times. Dynamic interfacial tension was observed even with small-molecule surfactants. The proposed criteria ensure equilibrium interfacial tension during drop formation.

Capillary pressure-based measurement of dynamic interfacial tension in a spontaneous microfluidic sensor

“…SI 5 †), and it is found that the adsorption time (at the interface) is longer than the diffusion time (from the bulk phase to the subinterface), which is in line with literature. 10 This indicates that at these higher concentrations, surfactant mass transfer is either mixed diffusion-kinetic controlled 32 or purely kinetic-controlled. 11 Besides, as the interface gradually approaches saturation (e.g., at τ ≈ 1 s), the free energy barrier for adsorption may also slow down the adsorption rate of SDS monomers, which also gives rise to the transition from diffusion-to kinetic-controlled mass transfer mechanism.…”

Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach

“…As recently shown by Kalli and Angeli 48 , it is preferred to use the dynamic interfacial tension instead of the equilibrium value, to generate universal flow pattern maps. However, it can be difficult to obtain an accurate estimation of the dynamic interfacial tension for forming droplets because classical methods based on a fixed interface (as pendant drop tensiometry or force tensiometry) may not be representative 17 . Moreover, it was shown in the previous section that γ seems to have a small impact on the BRANN prediction.…”

“…3.9% 7.3% 6.4% 3.9% 6.1% 5.2% ED: experimental data, SD: synthetic data size data set was produced. To predict the dimensionless droplet diameter for various flow rates, surfactant type and surfactant concentration, two data-driven models (BRANN and XGBoost) were used and compared to a recent semi-empirical model 17 .…”

“…These models are based on physical parameters (e.g. capillary number, flow rates, channel size) and provide new data that can be used as droplet predictors (droplet size, formation time) [15][16][17] . Furthermore, improvements in algorithms and computational capacity now enable numerical simulations of drop formation inside microchannels in complex configurations.…”

“…with ε is a parameter dependent on the geometry of the channel, d is the droplet diameter, D the channel depth, Q d the flow rate of the dispered phase, Q c the flow rate of the continuous phase, and Ca c = µ c Q c /(γS) the capillary number for the continuous phase (where S is the cross-sectional area of the inlet junction, γ is the equilibrium interfacial tension, and µ c is the continuous phase viscosity). Recently, Kalli et al17 used Eq. 6 with α = 0.188, β = 0.161, ε = 0, and k = 0.642 to predict with good agreement the size of surfactant-laden droplets generated in a flow-focusing microchannel:…”