Role of colloidal interactions in oil sand tailings treatment
Bibliographic record
Abstract
Abstract To provide fundamental insights into the treatment of oil sand tailings, the forces between a clay fine or silica particle and a silica wafer in aqueous solutions were measured using an atomic force microscope. A high molecular weight, partially hydrolyzed polyacrylamide (HPAM) was used as the flocculant. The effect of polymer dosage, solution pH, and addition of calcium and magnesium ions on the interaction and adhesion forces was studied. Tailings settling tests were carried out to link the measured forces with tailings treatment. The results showed that the addition of the polymer at low dosages or divalent ions at low concentrations resulted in adhesion interactions. The adhesion force increased with increasing polymer dosage or cation concentration until an optimum dosage or concentration was reached. Higher polymer dosage and ion concentration resulted in a weaker adhesion or even purely repulsive force profiles. The synergy of the polymer and divalent ions significantly enhanced the adhesion between fine solids. The measured adhesion forces correlated well with settling characteristics: the stronger the adhesion, the higher the initial settling rate. This study suggests a potential new technology for oil sand tailings treatment using the synergic effect of polymers and divalent cations. © 2005 American Institute of Chemical Engineers AIChE J, 2006
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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 | 0.000 |
| 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) | 0.002 | 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".