Association Behavior of Pyrene Compounds as Models for Asphaltenes
Bibliographic record
Abstract
Asphaltene association in solution has been studied extensively using methods such as vapor-pressure osmometry and neutron scattering. These methods give relative data on association as a function of solvent strength, temperature, and concentration, but interpretation of the results is hampered by the polyfunctional nature of asphaltenes and the distribution of molecular weight. In this work, we present data on association of representative model structures for asphaltenes, as measured using vapor-pressure osmometry in o -dichlorobenzene at 75−130 °C, and using small-angle neutron scattering in toluene. A series of compounds were synthesized based on the four-ring aromatic compound pyrene. The synthetic compounds were designed to give interactions between aromatic rings, alkyl chains, and selected functional groups through aromatic interactions, hydrogen bonding, and polar interactions. Even in this strong solvent, polar interactions between oxygen functional groups gave average molecular weights of up to twice the true value, indicating dimer formation. An alkyl pyrene compound, dipyrenyl decane, gave much less significant association. Pyrene itself exhibited little or no self-association.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".