Molecular dynamics investigation of the asphaltene–kaolinite interactions in water, toluene, and water–toluene mixtures
Bibliographic record
Abstract
Understanding the interactions of petroleum asphaltenes with mineral surfaces is important for diluted bitumen spill response and modeling. In this study, molecular dynamics and umbrella sampling simulations are performed using interfacially active and non-interfacially active asphaltene model compounds individually positioned near each of the surfaces of kaolinite in the presence of explicit solvent environments containing water, toluene, and mixtures of toluene and water in varying proportions. The interfacially active asphaltene bonds the strongest to the silicon oxide surface of kaolinite in pure water and the bonding weakens to nearly zero in toluene-water mixtures. The non-interfacially active asphaltenes bond to kaolinites silicon oxide surface in water about half as strongly as the interfacially active one in water and the bonding weakens in the presence of toluene. The number of non-hydrogen bonded contacts between the interfacially active asphaltene and the aluminum hydroxide surface of kaolinite increases as the proportion of toluene is increased and the contacts with water are decreased. In these conditions, the non-interfacially active asphaltenes do not form non-hydrogen bonded contacts with kaolinite. On the silicon oxide surface, the number of non-hydrogen bonded contacts of all asphaltenes with kaolinite tends to decrease as the proportion of toluene is increased and the contacts with water are decreased. The number of hydrogen bonds of the interfacially active asphaltene with water decreases as the proportion of toluene is increased. The radii of gyration indicate that the interfacially active asphaltene is extended in water and when adsorbed on kaolinite, and becomes compact as the proportion of toluene is increased. The simulation results highlight the competitive interfacial interactions in the complex scenario of diluted bitumen spills in the presence of water and clay minerals.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".