Size and velocity of jet drops produced by bursting bubbles at the interface of a water jet
Bibliographic record
Abstract
Bursting bubbles at the free surface of aerated faucet water jets may spread pathogens through the released droplets. Many studies focused on the production of jet drops from bursting bubbles at a planar interface, particularly for the first jet drop. The extent to which previous findings apply to bubbles in aerated jets remains unknown. In this study, we produce a wide range of bubble size distributions within different jet diameters and characterize the diameter and velocity of jet drops released from individually bursting bubbles. Several similarities with the planar case are recovered, such as the overall dependence of the jet drop diameter and bursting dynamics on the bubble diameters and the formation of secondary jet drops. However, we observe asymmetries in the ejection of the droplets, and droplets ejected horizontally appear to have the highest ejection velocity among all jet drops. By modeling the evolution of the ejected drops for the different bubble size distributions, we find that for a mean Laplace number Labub=ρwσwRbubμw2≲6×104, a fraction of the drops ejected can become airborne. Droplets deposit within 9 cm for a mean Labub≲2.1×104 and within 33 cm for a mean 2.1×104≲Labub≲1.8×105 from a faucet jet, assuming a countertop situated 20 cm below the faucet outlet. A bubble size distribution with a mean Labub of 6×104 would minimize both the risk of airborne pathogen transmission and that resulting from surface contamination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".