Flocculating and dewatering of kaolin suspensions with different forms of poly(acrylamide‐co‐diallyl dimethylammonium chloride)
Bibliographic record
Abstract
Abstract Recently, polymeric nanofibres have been considered for the rapid flocculation and dewatering of oil sands tailings. Apparently due to their fast initial interaction with the suspended fine particles, polymer nanofibres performed better than their parent water‐soluble polymer solutions. To further understand this mechanism, a model study was conducted using kaolin suspensions and poly(acrylamide‐co‐diallyl dimethylammonium chloride) nanofibres and powders. The initial settling rate, supernatant turbidity, water recovery, capillary suction time, and solids content were measured to determine the effect of using nanofibres on solid‐liquid separation. Nanofibres performed similarly to, or better than, equivalent polymer solutions in terms of initial settling rate and supernatant clarity at higher dosages and kaolin loadings. The kaolin studies showed that polymer nanofibres could absorb onto clay particles faster, and produce bigger flocs more rapidly, supposedly due to their high surface area to volume ratio. Our results indicate that polymer nanofibres, as well as polymer powders, may be successfully used to treat mature fine tailings produced from oil sands.
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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) | 0.000 | 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 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".