Probing the Impact of Asphaltene Contamination on Kaolinite and Illite Clay Behaviors in Water and Organic Solvents: A Calorimetric Study
Bibliographic record
Abstract
A detailed understanding of the impacts of trace compounds and asphaltene adsorption on the behavior of clays contributes to the development of production processes for heavy oils and bitumen with lower environmental impacts, to the treatment of tailings from mined bitumen, and to the mitigation of impacts from oil spills in natural environments. Probes, such as solution calorimetry, are sensitive to species transfer to and from clay surfaces and outcomes can be interpreted unambiguously when supplemented with thermogravimetric analysis and scanning electron microscopy measurements. In this study, the effects of asphaltene coating on the enthalpy of solution of kaolinite and illite clays in toluene, n -heptane, and deionized water were investigated at 60 °C and atmospheric pressure. Asphaltene coating increases organic compound sorption but does not impact water sorption vis-à-vis uncoated clay particles or water displacement from clay particles by organic liquids as solvents or as trace contaminants in water. Experimental outcomes are interpreted using a quantitative mass and energy balance model framework that isolates terms for solvent and trace contaminant sorption/desorption and surface energy effects. Mechanistic and quantitative insights underlying the stability of asphaltene-coated clay dispersions in tailings ponds and the behaviors of these clays in diverse industrial and natural environments are discussed.
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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".