Using surface geopolymerization reactions to strengthen Athabasca oil sands mature fine tailings
Bibliographic record
Abstract
Abstract This paper describes a novel method to address the oil sands tailings reclamation problem using surface geopolymerization reactions. Geopolymerization involves dissolution of aluminosilicate minerals and re‐solidification reactions that result in the formation of three‐dimensional inorganic polymers of significant strengths. Since the fines present in oil sands mature fine tailings (MFT) mainly consist of aluminosilicate minerals, or clay minerals, it is hypothesized that by adding appropriate reagents, the surface of the clay minerals can be activated and go through geopolymerization reaction under ambient conditions. The resulting geopolymeric species formed on the clay minerals will bind the clay particles together and strengthen the tailings even without further dewatering. Geopolymerization was tested on 0.51 g/g (51 wt%) solids centrifuged MFT and its shear strength was observed to increase from 115 to 4880 Pa in 90 days when treated with 40 kg of sodium hydroxide and 60 kg sodium silicate per tonne of dry MFT solids. Similarly, the shear strength of 0.48 g/g (48 wt%) solids polymer flocculated MFT increased from 395 to 3950 Pa 90 days after the addition of the same dosages of sodium hydroxide and sodium silicate. Tests conducted on a geopolymerized model kaolinite sample showed that under the test conditions, the geopolymerization reaction only occurs on the surface of the kaolinite at the indicated dosages. The surface geopolymers held the kaolinite particles together, leading to an increase in its shear strength.
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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) | 0.002 | 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".