A Surfactant Prediction Model for Rising Bubbles
Lim Chi Tung James, Ivo R. Peters, Swathi Krishna

TL;DR
This paper introduces an empirical model that predicts surfactant concentration in rising bubbles by analyzing early-stage shape deformations observed through high-speed imaging.
Contribution
The study develops a novel aspect-ratio-based analysis method to estimate surfactant levels from bubble shape dynamics within the first 144 ms of ascent.
Findings
Surfactants dampen bubble shape oscillations and reduce aspect ratio amplitudes.
The model accurately estimates surfactant concentrations between 0 and 2.9 ppm.
It reliably detects surfactant presence and relative concentration in unknown samples.
Abstract
Bubbles released from a needle show shape deformations that depend on the surfactant concentration of the surrounding liquid. We develop a model that predicts the surfactant concentration based on experimental early-stage observations of these deformations. Using high-speed imaging, we examine bubbles within the first 144 ms of ascent, corresponding to a vertical rise distance of approximately 40 mm and extract the instantaneous aspect ratio (AR) and analyse its temporal evolution. In clean conditions, bubbles exhibit pronounced shape oscillations resulting from the periodic exchange between surface and kinetic energy. The presence of surfactants leads to an immediate damping of these oscillations, characterised by reduced AR amplitudes and earlier peak deformations. This damping effect intensifies with increasing surfactant concentration until a near-saturation regime is reached,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
