Measurement of sliding velocity and induction time of a single micro‐bubble under an inclined collector surface
Bibliographic record
Abstract
Abstract In this study, interactions between a gas bubble and a flat solid surface were investigated by determining two dynamic parameters, bubble sliding velocity underneath an inclined solid surface and induction time of the gas bubble attaching to the solid surface in aqueous solutions. A single micro‐bubble was allowed to move vertically toward an inclined solid surface. After reaching its terminal velocity, the bubble approaches the inclined solid surface and slides underneath it. Complete trajectory of the bubble movement was monitored and recorded by a high‐speed CCD video imaging system. Various types of gas bubbles (CO2, air, H2, and O2) and solid surfaces such as bitumen‐coated Teflon, hydrophobized and hydrophilic silica were used in sliding velocity and induction time measurements. The effect of water chemistry (industrial process water and de‐ionized water) and surface heterogeneity on bubble sliding velocity and induction time was investigated. The results showed that the sliding velocity of micro‐bubbles under an inclined solid surface is a strong function of water chemistry, gas type, temperature and hydrophobicity of the solid surface. This study provides relevant information on bubble–solid interactions that would assist in the understanding of bubble–solid attachment under diverse conditions.
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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 | 0.000 | 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".