Binary Interactions in Coke Formation from Model Compounds and Asphaltenes
Bibliographic record
Abstract
The effects of binary interactions on the coke yield and cracking kinetics in mixtures of model compounds were examined to understand the interactions present in multi-component mixtures, such as petroleum residues. Eight model compounds were selected with molecular weights in the range of 398–623 g/mol and structural elements to represent components of vacuum residues, including a pyrene substituted with four alkyl groups and pyrene derivatives tethered to various heterocycles via an ethano bridge. Thermogravimetric analysis of the binary mixtures showed evidence of three distinct modes of behavior with respect to the additive sum of the mass-weighted component contributions: the coke yield was (1) equal to the additive sum, (2) less than the additive sum, or (3) greater than the additive sum. The individual behavior correlated well with the type of compounds present in the specific mixtures and can be explained in terms of three types of interactions between the mixture components: radical stability or kinetic factors, association forces between the mixture components, and matrix effects. The apparent activation energies of cracking for the mixtures, on the other hand, showed complex behavior that sometimes followed the additivity contributions of the components but, on other occasions, was outside the bounds of activation energies of the pure compounds.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".