Dewatering Oil Sands Tailings with Degradable Polymer Flocculants
Bibliographic record
Abstract
Abstract We synthesized hydrolytically degradable cationic polymers by micellar radical polymerization of a short-chain polyester macromonomer, polycaprolactone choline iodide ester methacrylate (PCL2ChMA) with two polyester units, and used them to flocculate oil sands mature fine tailings (MFT). We evaluated the flocculation performance of the homopolymer and copolymers with 30 mol % acrylamide (AM) by measuring initial settling rate (ISR), supernatant turbidity, and capillary suction time (CST) of the sediments. Flocculants made with trimethylaminoethyl methacrylate chloride (TMAEMC), the monomer corresponding to PCLnChMA with n = 0, have improved performance over poly(PCL2ChMA) at equivalent loadings due to their higher charge density per gram of polymer. However, MFT sediments flocculated using the PCL2ChMA-based polymers are easier to dewater (up to an 85% reduction in CST) after accelerated hydrolytic degradation of the polyester side chains. This study demonstrates the potential of designing cationic polymers that effectively flocculate oil sands tailings ponds, and also further dewater the resulting solids through polymer degradation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".