Heavy Petroleum Composition. 3. Asphaltene Aggregation
Bibliographic record
Abstract
Molecular characterization of asphaltenes by conventional analytical techniques is a challenge because of their compositional complexity, high heteroatom content, and asphaltene aggregate formation at low concentrations. Thus, most common characterization techniques rely on bulk properties or solution-phase behavior (solubility). Proposed over 20 years ago, the Boduszynski model proposes a continuous progression in petroleum composition (molecular weight, structure, and heteroatom content) as a function of the atmospheric equivalent boiling point. Although exhaustive detailed compositional analysis of petroleum distillates validates the continuum model, the available compositional data from asphaltene fractions supports the extension of the continuum model into the nondistillables only indirectly. Asphaltenes, defined by their insolubility in alkane solvents, accumulate in high-boiling fractions and form stable aggregate structures at low parts per billion (ppb) concentrations, far below the concentration required for most mass analyzers. Here, we present direct mass spectral detection of stable asphaltene aggregates at lower concentrations than previously published and observe the onset of asphaltene nanoaggregate formation by time-of-flight mass spectrometry (TOF–MS). We conclude that a fraction of asphaltenes must be present as nanoaggregates (not monomers) in all atmospheric pressure and laser-based ionization methods. Thus, those methods access a subset of the asphaltene continuum.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.006 | 0.002 |
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".