Molecule–Surface Recognition between Heterocyclic Aromatic Compounds and Kaolinite in Toluene Investigated by Molecular Theory of Solvation and Thermodynamic and Kinetic Experiments
Bibliographic record
Abstract
Molecular recognition interactions between kaolinite and a series of heterocyclic aromatic compounds (HAC) representative of the N- and S-containing moieties in petroleum asphaltene macromolecules are investigated using the three-dimensional reference interaction site model with the Kovalenko–Hirata closure approximation (3D-RISM-KH) theory of solvation and experimental techniques in toluene solvent. The statistical-mechanical 3D-RISM-KH molecular theory of solvation predicts the adsorption configuration and thermodynamics from the 3D site density distribution functions and total solvation free energy, respectively, for adsorption of HAC and toluene on kaolinite. Spectrophotometric measurements show that, among the HAC studied, only acridine and phenanthridine adsorb quantitatively on kaolinite. For these pyridinic HAC, the adsorption results fitted to the Langmuir isotherm in the monolayer domain suggest a uniform monolayer of HAC molecules. The 3D-RISM-KH studies predict that the aluminum hydroxide surface of kaolinite is preferred for HAC adsorption due to strong hydrogen bonding with the pyridinic N atoms, while the rest of the HAC adsorb weaker. Adsorption on the silicon oxide side is weak and delocalized, as evident from the 3D solvation free energy density. Toluene sites effectively compete with non-hydrogen bonding HAC, such as fused thiophenes, for the kaolinite surface. The adsorption enthalpy and phenanthridine-acridine loading ratio are calculated and correlated with the experimentally determined Langmuir constant and adsorption loading. This combined experimental and computational modeling approach is aimed to provide insight into the specific interactions among clays, bitumen, and solvents so as to help accelerate the development of environmentally friendly and efficient desorption systems for nonaqueous extraction of bitumen from Oil Sands, an important unconventional petroleum reserve.
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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 | 0.000 | 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) | 0.000 | 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".