Bibliographic record
Abstract
Abstract Interactions between negatively charged bitumen and fine solids under oil sands extraction conditions were simulated using mature fine tailings (MFT) and hydrocarbon oil with dissolved carboxylic acids. Their attachment induced by cation activation was evaluated with different types of cations by simple dynamic attachment tests. The results revealed that solid hydrophobization by adsorbing surfactants was key for oil–solid attachment. Activation by multivalent metal cations was due to surface precipitation of metal hydroxides, followed by chemisorbing anionic surfactants on metal‐activated solids to form metal carboxylate complexes/precipitates, thereby hydrophobizing the solids. Activation by cationic surfactants depended on their hydrocarbon chain lengths. For short hydrocarbon chains, where hydrophobic interaction is weaker than electrostatic interaction between the added cationic and anionic surfactants, the added cations promote the adsorption of anionic surfactants by electrostatic interaction to render the solids hydrophobic. For long hydrocarbon chains where hydrophobic interaction is stronger than electrostatic interaction between the added cationic and anionic surfactants, the adsorption of anionic surfactants occurs through the hydrophobic association of the hydrocarbon chains, posing the head group towards water, thereby making the solids less hydrophobic. Activation by cationic flocculants was purely physical (hydrogen bonding and electrostatic): when the solids were turned positive by the added cationic flocculants, the added anionic surfactants then adsorbed onto the solids to render them hydrophobic. It appeared that soluble multivalent metal species (e.g., Ca 2+ and Mg 2+ ) were much less harmful to bitumen extraction than those heavy metals coated on the solids, either in the form of surface precipitates or hydrolyzed ionic species.
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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) | 0.001 | 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".