Solvent-Kaolinite Interactions Investigated using the 3D-Rism-Kh Molecular Theory of Solvation
Bibliographic record
Abstract
Abstract The oil sands of western Canada represent the third largest hydrocarbon deposit in the world. Bitumen, a very heavy petroleum, is recovered from mined oil sands using warm water extraction followed by separation treatments to isolate the bitumen product. The high energy, water use, as well as tailings remediation challenges associated with the warm water extraction process raise major environmental concerns. Non-aqueous extraction using organic solvents at room temperature has been investigated extensively as an alternative to the warm water extraction process. The main challenge to the large-scale implementation of non-aqueous extraction is the retention of solvent in the tailings. The objective of this work was to present and validate a computational model for the interaction of solvents used in non-aqueous extraction with minerals, such as the abundant and adsorbent clay mineral kaolinite. The model system contained a periodically extended kaolinite platelet immersed in a solvent and all were treated at the atomic level using the 3D Reference Interaction Site Model with the Kovalenko-Hirata closure approximation (3D-RISM-KH) molecular theory of solvation. The solvent solvation free energy of interaction with kaolinite as well as site-specific adsorption energies and kinetic barriers for desorption were computed based on the solvent site density distribution functions. Moreover, the lateral and integrated density distributions were computed to analyze the organization of solvent at kaolinite surfaces. The integrated density distribution profiles were correlated with experimental adsorption isotherms. The results showed very strong adsorption of ethanol and weak adsorption of hydrocarbon solvents on kaolinite, which were in qualitative agreement with experimental solvent extraction reports. The model and these findings are valuable in understanding the mechanism of solvent retention in tailings after non-aqueous extraction and highlight the action of hydroxylated cosolvent additives to enhance extraction using nonpolar solvents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".