Binary coalescence of air bubbles in viscous liquids in presence of non‐ionic surfactant
Bibliographic record
Abstract
Abstract Coalescence of air bubbles is important in gas–liquid reactors and food processing operations. Bubbles can be stabilized by using non‐ionic surfactants. Binary coalescence of air bubbles in ethylene glycol and aqueous glycerol solutions were studied in this work in presence of Span 80. A novel set‐up was developed to study long coalescence times. Coalescence time was observed to follow broad stochastic distributions in all systems. The distributions were fitted with a stochastic model developed earlier. The surface tension of ethylene glycol and glycerol solutions was measured at various concentrations of Span 80. These data were fitted using a surface equation of state derived from the Langmuir isotherm. The effect of surfactant concentration on coalescence time was explained in terms of the surface excess of the surfactant and the repulsive force generated at the air–liquid interface. The results from this work illustrate the stochastic nature of bubble coalescence in viscous liquids. This work also demonstrates how non‐ionic surfactants can stabilize bubbles in such liquids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".