Quantifying Contributions of Different Repulsion to Film Drainage Time during the Bubble–Solid Surface Attachment and Implications for the Flotation of Fine Particles
Bibliographic record
Abstract
The flotation recovery of fine particles faces serious challenges due to the lack of kinetic energy required for supporting their radial displacement and attachment with bubbles. Generally, the hydrodynamic resistance and repulsive disjoining pressure successively inhibit the liquid outflow intervening between the bubble and solid surfaces. To quantitatively characterize the influence of the main repulsion on film thinning time, experiments have been designed in three different aqueous systems. Bubble surface mobility closely associated with hydrodynamic resistance was determined by the rising bubble technique, and the DLVO theory was employed to confirm the evolution of electrostatic repulsion. The film drainage process was then measured based on the high-speed microscopic interferometry. Furthermore, the influence of the main repulsion on bubble-solid surface interactions was examined by flotation recovery. Results show that the earlier buildup of hydrodynamic force ran through the whole film thinning process, and under immobile conditions, the central region gradually became dominant in film thinning due to the very limited fluid flow at the thinnest rim position. Therefore, to achieve the identical film thickness (∼100 nm), the large hydrodynamic resistance could prolong the film thinning time by about 1 order of magnitude, compared with that induced by electrostatic repulsion, which accounts for the increased flotation recovery by 10% using mobile bubbles. This study not only enhances the understanding of how typical repulsive forces work in film drainage dynamics but also opens up an avenue for enhancing flotation and avoiding wasting resources by modulating bubble surface mobility and thus micro/nanoscale fluid flow.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".