Multifunctional CO<sub>2</sub>‐switchable polymers for the flocculation of oil sands tailings
Bibliographic record
Abstract
ABSTRACT A series of multifunctional homopolymer and copolymer of 2‐dimethylamino ethyl methacrylate (DMAEMA) and N‐isopropyl acrylamide (NIPAM) were designed and used to flocculate oil sands mature fine tailings (MFTs). Carbon dioxide (CO2) protonated the tertiary amine groups of P(DMAEMA) and P(DMAEMA–NIPAM) making their chains positively charged. The pH sensitivity of these polymers favored the flocculation of the negatively charged clays in MFT due to charge neutralization. Three different polymers, P(DMAEMA), P(NIPAM33–DMAEMA67), and P(NIPAM67–DMAEMA33) were synthesized via aqueous free‐radical polymerization and used to flocculate MFT in the presence of CO2. Experimentally, CO2 was introduced in the system in three different ways: (1) CO2 was first bubbled into polymer solution, then the polymer solution was added to MFT, (2) CO2 was first bubbled into MFT and then the CO2‐free polymer solution was added to MFT, and (3) both polymers and MFT were bubbled with CO2 separately, then mixed together. We compared the effects of the method of CO2 addition, copolymer composition, and polymer molecular weight on MFT flocculation performance. Our results indicate that CO2‐switchable polymers can be employed to enhance the dewatering of challenging wastewaters such as oil sands tailings. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47578.
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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) | 0.001 | 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".