Rapid\nDewatering and Consolidation of Concentrated\nColloidal Suspensions: Mature Fine Tailings via Self-Healing Composite\nHydrogel
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Billions of tonnes of thick waste streams with highly concentrated\ncolloidal suspensions from different origins have accumulated worldwide,\nexampled as over 220 km<sup>2</sup> mature fine tailings (MFT) from\noil sands production in north Alberta. Current treatment technologies\nare limited by slow yet insufficient water release and sludge consolidation.\nHerein, a self-healing composite hydrogel system is designed to convert\nconcentrated aqueous colloidal suspensions (e.g., MFT with colloidal\nsolid content >30 wt %) into a dynamic double cross-linked network\nfor rapid dewatering and consolidation. The resultant composite hydrogel\ndemonstrates an excellent dewatering performance so that over 50%\nof water could be rapidly released within 30 min by vacuum filtration.\nFurthermore, the formed infinite cross-linked network with self-healing\nability can effectively trap fine particles of all sizes and capture\nsmall flocs during mechanical mixing, thereby enabling a low solid\ncontent at the ppm level in the released water. This new strategy\noutperforms all the previously reported treatment methods; under mechanical\ncompression, over 80% of water is removed from the MFT, thereby generating\na stackable material with >70 wt % solids within an hour. These\nresults\ndemonstrate a highly effective approach and provide insight into the\ndevelopment of advanced materials to tackle the challenging environmental\nslurry issues.
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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) | 0.062 | 0.001 |
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 it