Effect of solid material and surfactant presence on interactions of bubbles with horizontal solid surface
Bibliographic record
Abstract
Abstract The interaction of a bubble with an immersed horizontal solid surface is studied experimentally. The effect of presence of a surfactant (limited to a specific nonionic surfactant, α‐terpineol, of various concentrations) and of surface material (cleaned glass, polypropylene, polyethylene, and Teflon) is investigated. The study focuses on two particular stages of the interaction, (i) the process of bubble bounce, which includes a collision and subsequent rebound from the solid surface, and (ii) the bubble attachment, which occurs after collision in time much longer than the duration of bounce and also after disappearance of all visible bubble movement. It is observed that the effect of the surface material on the bouncing is minor, possibly due to the liquid film separating the bubble and the solid surface. The presence of surfactant significantly affects the bouncing process. It not only decreases the initial bubble velocity, but also diminishes the bubble deformation after the collision and suppresses the bubble rebound from the surface. No rebound from the surface is observed in the most concentrated (1 × 10−3 mol/L) α‐terpineol solution. The adhesion time depends both on the solid material and α‐terpineol concentration. If the α‐terpineol concentration is increased, the adhesion time increases in the case of polypropylene surface, while it decreases in the case of polyethylene and Teflon surfaces. Results of this study are relevant for the description of attachment mechanism and to determine the proper conditions for selective flotation of plastics.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".