Starch‐based composites using mature fine tailings as fillers
Bibliographic record
Abstract
Abstract Mature fine tailings (MFT) are one of the major environmental problems associated with the oil sands industry in Canada. To help mitigate the negative impact tailings have on the environment, we prepared MFT/starch composites and studied their morphology, water resistance, and mechanical properties as a function of filler percentage. The motivation behind this approach is to turn waste tailings into a source of potentially useful materials. We compared the MFT/starch composites to similar composites made with montmorillonite (MMT), cellulose nanocrystals (CNC), and Dean Stark solids (DS). The water resistance of MFT/starch and DS/starch composites improved 6 % at the highest filler content. MFT/starch and DS/starch composites had similar mechanical properties, but performed better than plasticized starch, with an increasing tensile modulus with increasing filler content. Despite the higher modulus increase in intercalated MMT/starch and CNC/starch composites (up to 5 % filler), the MFT/starch composites with filler contents higher than 10 % achieved the same tensile modulus values; since our objective was to transform as much MFT as possible into useable materials, this can be seen as a positive feature of these composites. The dynamic mechanical analysis of the composites showed they were heterogeneous and identified a plasticizer‐rich phase and a starch‐rich phase.
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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".