Enhanced Settling and Dewatering of Oil Sands Mature Fine Tailings with Titanomagnetite Nanoparticles Grafted with Polyacrylamide and Lauryl Sulfate
Bibliographic record
Abstract
In this study, a three-in-one nanoflocculent that consists of nanoparticles of titanomagnetite (NTM) grafted with multiple segments of hydrophobically modified chains of polyacrylamide (PAM) with sodium laurel sulfate (SLS) was developed for enhanced settling and dewatering of oil sands mature fine tailings (MFT). The existence of NTM allowed more than one segment to graft/functionalize (i.e., hydrophobic and hydrophilic) on the same surface. This three-in-one nanoflocculent not only provides better exposure for the functionalized species, but it also increases the nanoflocculant specific gravity and dispersibility for better interactions. The NTM was initially synthesized by the coprecipitation method and subsequently grafted with PAM and SLS at room conditions. The flocculation and dewatering performance of the as-synthesized nanoflocculants was evaluated using the initial settling rate (ISR), supernatant turbidity, sludge volume index, capillary suction time (CST), and specific resistance to filtration (SRF). Flocculation of the MFT suspension using the novel optimized nanoflocculants at 3000 ppm had 15 times faster ISR, and the supernatant turbidity, CST, and SRF had stronger outcomes than the traditionally used 20,000 ppm commercial anionic PAM. This study demonstrates the potential of designing nanoflocculants to effectively enhance settling and dewatering of oil sands tailings, while reducing the required amount of coagulants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".