Flocculation and Dewatering of Mature Fine Tailings Using Temperature-Responsive Cationic Polymers
Bibliographic record
Abstract
Temperature-responsive copolymer with cationic charge was prepared with N-isopropylacrylamide (NIPAm) and 2-aminoethyl methacrylamide hydrochloride (AEMA) by conventional free-radical polymerization. The flocculation performance of the copolymer, poly(AEMA-st-NIPAm), was compared to five different mixture ratios of polyNIPAm and cationic poly(acrylamide-st-diallyldimethylammonium chloride) (poly(AAm-st-DADMAC)). The effects of polymer mixture ratios, polymer dosages, and temperature on solid-liquid separation as a function of initial settling rates (ISR), supernatant turbidity, sediment solid content, and water recovery were investigated. Poly(NIPAm) can facilitate particles aggregation by bridging and hydrogen bonding under lower critical solution temperature (LCST); whereas, at temperature above LCST, the adsorption of poly(NIPAm) chains on particles can be enhanced by hydrophobic interaction. A two-step (25 °C → 50 °C → 25 °C) consolidation can further enhance the sediment solid content by polyNIPAm. While the neutral property of polyNIPAm resulted in high turbidity of supernatant, mixing with poly(AAm-st-DADMAC) increases the clarity of supernatant by neutralization of fine particles. The copolymer poly(AEMA-st-NIPAm) functions as a polyelectrolyte to enhance the polymer adsorption onto particles via electrostatic interactions, thus further improving ISR and supernatant clarity.
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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".