Molecular Dynamics Simulation of the Adsorption Interactions of Selected Polar and Nonpolar Polymers on Kaolinite Basal Surfaces in the Presence of Cyclohexane
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The nonaqueous extraction (NAE) of bitumen has been proposed as a more environmentally friendly alternative to the current water-based extraction process employed in Athabasca oil sands. The high content of fine clays in the solvent-diluted bitumen product is a considerable challenge to the NAE commercial implementation. Seven candidate polymer flocculants were studied in a kaolinite–cyclohexane suspension by using molecular dynamics simulations. The adsorption interactions on both kaolinite basal surfaces were evaluated along with the solubility in cyclohexane. The strongest interaction was observed between poly(2-acrylamido-2-methylpropanesulfonic acid) (PAMPS) and the aluminum hydroxide surface, with an adsorption energy of −228 kJ mol –1 . On the silicon oxide surface, polyacrylamide interacted the strongest, with an adsorption energy of −106 kJ mol –1 . The interactions are interpreted in relation to the number of polymer–surface and polymer–solvent contacts and the cyclohexane–polymer Flory–Huggins interaction parameters and corroborated by the radii of gyration and the solvent accessible surface areas of the polymers in cyclohexane. Polyisoprene (PI) presented the highest number of contacts with kaolinite among the nonpolar polymers. The findings suggest that efficient polymer flocculants containing cyclohexane-soluble and kaolinite-attracting moieties, such as the PI–PAMPS block copolymer, could potentially improve fine solid removal from diluted bitumen product and enhance NAE viability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".