The effect of switchable additives on colloidal interactions found in oil sands as measured by chemical force spectrometry
Bibliographic record
Abstract
After oil sands separations, settling of clays from aqueous tailings can be promoted by additives such as Ca2+ salts. However, if the liberated water is then recycled, these same additives in the water interfere with bitumen recovery in the separator. Therefore, we have tested CO2-triggered switchable additives to see whether they can switch back and forth between a form that is suitable for the separation stage and a form that promotes tailings ponds settling. CO2-triggered switchable additives can reversibly change water chemistry merely by introduction and removal of CO2, a benign trigger. Here, the effects of CO2-mediated switchable additives on colloidal interactions found in model oil sands were studied by chemical force spectrometry. Self-assembled monolayers of 12-phenyldodecanethiol and 12-mercaptododecanoic acid were used to chemically modify gold-coated atomic force microscope tips. These were subsequently used to study the adhesion force between the modified tips and the minerals silica and mica. The adhesion between the tips and the mineral substrates was studied in aqueous solutions of varying pH and divalent cation concentration and in the presence of cationic switchable additives of varying surfactant potency, both in the presence and in the absence of CO2. In the presence of CO2, the best additive promotes attractive forces, while in the absence of CO2, the forces are repulsive. These results are discussed in the context of the mechanism of colloidal interactions in an oil sands system.
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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 | 0.001 |
| 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 | 0.001 | 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".