Computational Investigations Complement Experiment for a System of Non-Covalently Bound Asphaltene Model Compounds
Bibliographic record
Abstract
In this paper, the aggregation of asphaltene model compounds has been explored using a combination of density functional tight-binding (DFTB) and density functional theory (DFT), in a manner that revisits an experimental study from 2015 by Schulze, Lechner, Stryker, and Tykwinski. The model compounds investigated include a porphyrin with an acidic side chain, and a three-island archipelago compound with pyridine as the central island, and pyrene for the outer islands. The possible stoichiometries and conformations for complexes were explored and compared to the experimental results. Our computational results show that there are four possible complexes involving these two model compounds with large (K>1000) equilibrium constants of formation, which will exist in competition with each other. We find that both hydrogen bonding and π − π stacking are important to this aggregation. On the other hand, neither water-mediated aggregation nor coordination to open porphyrin sites was found to be significant, in contrast to some previous suggestions of their importance. The multiple possible stoichiometries of complexes confound some of the analysis done in the experimental paper, as Job plots assume that only one complex is present. Gibbs free energies of association were determined for various complexes, with and without microhydration, at the ωB97X-V/def2-QZVPP//ωB97X-D4/def2-SVP level of theory. We also briefly explore some of the factors influencing the change in NMR chemical shift for select nuclei reported in the experimental paper.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.006 | 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".