Early Time Spreading Dynamics of Nanobubble-Laden Drops
Bibliographic record
Abstract
Nanobubbles, when dispersed in a liquid phase, may enhance mass transport, adsorption, and reactions in many industrial applications, such as fabrication of functional materials, drug delivery, water treatment, carbon dioxide capture, and surface decontamination. Here, we experimentally study the early time spreading dynamics of nanobubble-laden surfactant drops on a hydrophilic solid surface submerged in an oil phase. Along with recovering the retarding effects of surfactants on the early time wetting dynamics, we report that nanobubbles can weaken Marangoni stresses and consequently reduce the duration of the retardation regime. Remarkably, we find that the duration of this retardation regime ( t r ) exponentially decays with the nanobubble concentration in the dispersion ( N b ) according to N b ∼ log(1/ t r ). The micro-particle imaging velocimetry analysis of the flow field inside the drop indicates a large reduction in the magnitude of velocities in the presence of surface-active materials, confirming the existence of Marangoni flow that opposes droplet spreading. Our research introduces a simple approach to calculate the nanobubble concentrations in liquids and offers guidelines for controlling wetting dynamics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".