Nanoparticle Flotation Collectors II: The Role of Nanoparticle Hydrophobicity
Bibliographic record
Abstract
The ability of polystyrene nanoparticles to facilitate the froth flotation of glass beads was correlated to the hydrophobicity of the nanoparticles. Contact angle measurements were used to probe the hydrophobicity of hydrophilic glass surfaces decorated with hydrophobic nanoparticles. Both sessile water drop advancing angles, θ(a), and attached air bubble receding angle measurements, θ(r), were performed. For glass surfaces saturated with adsorbed nanoparticles, flotation recovery, a measure of flotation efficiency, increased with increasing values of each type of contact angle. As expected, the advancing water contact angle on nanoparticle-decorated, dry glass surfaces increased with surface coverage, the area fraction of glass covered with nanoparticles. However, the nanoparticles were far more effective at raising the contact angle than the Cassie-Baxter prediction, suggesting that with higher nanoparticle coverages the water did not completely wet the glass surfaces between the nanoparticles. A series of polystyrene nanoparticles was prepared to cover a range of surface energies. Water contact angle measurements, θ(np), on smooth polymer films formed from organic solutions of dissolved nanoparticles were used to rank the nanoparticles in terms of hydrophobicity. Glass spheres were saturated with adsorbed nanoparticles and were isolated by flotation. The minimum nanoparticle water contact angle to give high flotation recovery was in the range of 51° < θ(np(min)) ≤ 85°.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".