Nanoparticle Flotation Collectors—The Influence of Particle Softness
Bibliographic record
Abstract
The ability of polymeric nanoparticles to promote glass bead and pentlandite (Pn, nickel sulfide mineral) attachment to air bubbles in flotation was measured as a function of the nanoparticle glass transition temperature using six types of nanoparticles based on styrene/N-butylacrylate copolymers. Nanoparticle size, surface charge density, and hydrophobicity were approximately constant over the series. The ability of the nanoparticles to promote air bubble attachment and perform as flotation collectors was significantly greater for softer nanoparticles. We propose that softer nanoparticles were more firmly attached to the glass beads or mineral surface because the softer particles had a greater glass/polymer contact areas and thus stronger overall adhesion. The diameters of the contact areas between polymeric nanoparticles and glass surfaces were estimated with the Young-Laplace equation for soft, liquidlike particles, whereas JKR adhesion theory was applied to the harder polystyrene particles. The diameters of the contact areas were estimated to be more than an order of magnitude greater for the soft particles compared to harder polystyrene particles.
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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) | 0.004 | 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; both teacher heads agree on what is shown here.
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".