New insights into antibubble formation by single drop impact on a same-liquid pool

Abstract

Hypothesis: Secondary drops (SDs) generated when falling drops impact a same-liquid bath can potentially generate antibubbles. Different mechanisms of antibubble formation can be identified and their size and formation probability (P_Ab) can be predicted.

Experiments: Surfactant solutions were dropped from various heights using a highly stable pulseless microfluidic pump in a same-liquid bath. The impact was recorded using a high-speed camera. The formation of SDs and antibubbles as well as their sizes were evaluated considering the falling-drop height (H_FD) and dimensionless parameters.

Findings: This study reports new mechanisms for antibubble formation from SDs. A decrease in the surface tension yielded a thinner central jet, thereby yielding more SDs. Larger values of the H_FD, impact velocity (U), and Weber number (We) increased the SD size and decreased the SD count; the increase in size increased the antibubble size. The number of SDs correlated with the formation of two distinct antibubbles or a single (coalesced) antibubble. The plots for P_Ab versus H_FD, U, and We exhibited two distinct peaks. A moderate increase in the surfactant concentration enhanced P_Ab in the first regime, whereas an excessive concentration limited antibubble formation. Artificial neural modeling can successfully predict antibubble formation. These findings provide valuable insights for the research on controlled antibubble generation.

Keywords: Air–liquid interface; Antibubble; Artificial neural network (ANN); Central jet; Critical micelle concentration (CMC); Impact velocity; Secondary drop; Surfactant; Weber number.