Effect of Precipitating Conditions on Asphaltene Properties and Aggregation
Bibliographic record
Abstract
Asphaltene, resin, and maltene physical isolation procedures involving different alkane precipitants and solvent/sample ratios were applied in this work to Athabasca bitumen vacuum residue. Samples were characterized by solubility profiling, size exclusion chromatography, fluorescence spectroscopy, X-ray photoelectron spectroscopy and density–viscosity analyses. Isolated fractions were found to display systematic property changes. Thus, it was found that denser, more polar, higher molecular weight (MW), more viscous, red-shifted fluorescence materials were sequentially ranked as follows: solvent extracted asphaltenes, C7 (unwashed) asphaltenes, C5 (unwashed) asphaltenes, resins, and maltenes. Intermolecular aggregation for these fractions was determined to follow the same order. Decreasing contents of resins in the same order were found to increase aggregation phenomena. This work further reports on aspects of possible practical interest, i.e., the liquid nature of asphaltenes at 300 °C and the possible existence of oxidative reactions affecting fraction isolation that follow standard methods which do not contemplate inert atmospheres. Preliminary assessment of chemical functionalities within isolated fractions highlighted the possible enrichment of pyrrolic compounds within resins and oxygen functionalities in asphaltenes.
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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) | 0.001 | 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".