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In this study, we experimentally investigate the stress field around a bubble rising in a dilute surfactant solution (20 < Re < 220, high Peclet numbers) whose surface gradually becomes contaminated, and compare it with that around a sphere free from surface contamination. We employ a newly developed polarization measurement technique, highly sensitive to stress fields near interfaces. First, we validate this method by measuring the flow around a solid sphere settling at Re = 120 and comparing results with numerical predictions, confirming its accuracy. We then measure the stress field around a bubble whose drag force transitions from that of a clean interface to that of a rigid interface within the observation region. The stress near the bubble's front resembles that of a clean bubble, while the rear behaves like a solid sphere. Between these regions, a discontinuous phase retardation near the cap angle indicates a transition from slip to no-slip boundary conditions. Axisymmetric stress reconstruction reveals localized stress spike at the cap angle, which shifts as surfactant accumulates and increases the drag. Remarkably, the measured cap angle versus normalized drag coefficient agrees well with numerical simulations at Re = 100 (Cuenot et al. 1997) and shows only a slight deviation from the creeping-flow stagnant cap model (Sadhal and Johnson 1983). This work demonstrates that polarization-based stress field measurements effectively capture the interplay between surface contamination and hydrodynamics at intermediate Reynolds numbers., Comment: 21 pages, 10 figures

A needle-free injector using a highly focused microjet has the potential to minimize the invasiveness of drug delivery. In this study, the jet penetration depth in a soft material-which is a critical parameter for practical needle-free injections-was investigated. We conducted jet penetration experiments by varying the inner diameter of the injection tube and the standoff distance between the meniscus surface and the soft material. Interestingly, the results showed that the penetration depths peaked at certain distances from the meniscus, and the positions shifted further away as the inner diameter was increased. By analyzing the velocity distribution of the microjet, the peak positions of the penetration depth and the maximum velocities were inconsistent due to the effects of the jet shape. To account for this, we introduce the concept of the 'jet pressure impulse', a physical quantity that unifies the velocity and jet shape. However, direct estimation of this parameter from experimental data is challenging due to limitations in spatiotemporal resolution. Therefore, we used numerical simulations to replicate the experimental conditions and calculate the jet pressure impulse. Remarkably, the results show that the jet pressure impulse has peak values, which is consistent with the penetration depth. In addition, there is a correlation between the magnitude of the jet pressure impulse and the penetration depth, highlighting its importance as a key parameter. This study underlines the importance of the jet pressure impulse in controlling the penetration depth of a focused microjet, providing valuable insights for the practical use of needle-free injection techniques., Comment: 11 pages, 12 figures

The control of the velocity of a high-speed laser-induced microjet is crucial in applications such as needle-free injection. Previous studies have indicated that the jet velocity is heavily influenced by the volumes of secondary cavitation bubbles generated through laser absorption. However, there has been a lack of investigation of the relationship between the positions of cavitation bubbles and the jet velocity. In this study, we investigate the effects of cavitation bubbles on the jet velocity of laser-induced microjets extracted using explainable artificial intelligence (XAI). An XAI is used to classify the jet velocity from images of cavitation bubbles and to extract features from the images through visualization of the classification process. For this purpose, we run 1000 experiments and collect the corresponding images. The XAI model, which is a feedforward neural network (FNN), is trained to classify the jet velocity from the images of cavitation bubbles. After achieving a high classification accuracy, we analyze the classification process of the FNN. The predictions of the FNN, when considering the cavitation positions, show a higher correlation with the jet velocity than the results considering only cavitation volumes. Further investigation suggested that cavitation that occurs closer to the laser focus position has a higher acceleration effect. These results suggest that the velocity of a high-speed microjet is also affected by the cavitation position., Comment: 11 pages, 13 figures, 4 tables

In this study, we systematically investigate the behaviors of viscoelastic liquid jets using an impulsive force, particularly in the high velocity and high elasticity regimes. The resulting jets are categorized into two types: (i) pinch-off jets, which break up during elongation after ejection, and (ii) no-pinch-off jets, which either retract to the nozzle after maximum elongation, known as `bungee-jumper jets' or return without elongation after ejection. We then propose criteria to delineate these regions using Reynolds number $Re$ and Weissenberg number $Wi$, reflecting the initial conditions at the jet ejection and the solution's rheological properties, respectively. We find that pinch-off jets occur at $Re \gtrsim 23.4Wi$ in high elasticity regimes ($Wi \gtrsim 10$), and at $Re \gtrsim 250$ in low elasticity regimes ($Wi \lesssim 10$). In addition, we demonstrate that the phase diagram of these behaviors can be rationalized through the focused jet modeling using the finitely extensible non-linear elastic dumbbell model with the Chilcott-Rallison closure approximation (FENE-CR).