Density Functional Theory Investigation of the Contributions of π−π Stacking and Hydrogen Bonding with Water to the Supramolecular Aggregation Interactions of Model Asphaltene Heterocyclic Compounds
Bibliographic record
Abstract
Abstract A complex supramolecular process involving electrostatic and dispersion interactions, asphaltene aggregation is associated with detrimental petroleum deposition and scaling that pose challenges to petroleum recovery, transportation, and upgrading. The density functional ωB97X-D with a dispersion correction was employed to investigate supramolecular aggregates incorporating heterocycles dimers with 0, 1, 2, and 3 water molecules forming a stabilizing bridge connecting the monomers. The homodimers of seven heterocyclic model compounds, representative of moieties commonly found in asphaltene structures were studied: pyridine, thiophene, furan, isoquinoline, pyrazine, thiazole, and 1,3-oxazole. The contributions of hydrogen bonding involving water bridges spanning between dimers and π−π stacking to the total interaction energy were calculated and analyzed. The distance between the planes of the aromatic rings is correlated with the π-π stacking interaction strength. All the dimerization reactions are exothermic, although not spontaneous. This is mostly modulated by the strength of the hydrogen bond of the water bridge and the π-π stacking interaction. Dimers bridged by two water molecules are more stable than with additional water molecules or without any water molecule in the bridge. Energy decomposition analysis show that the electrostatic and polarization components are the main stabilizing terms for the hydrogen bond interaction in the bridge, contributing with at least 80% of the interaction energy in all dimers. The non-covalent interaction analysis confirms the molecular sites that have the strongest (hydrogen bond) and weak (π-π stacking) attractive interactions. They are concentrated in the water bridge and in the plane between the aromatic rings, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".