Composition and Size Distribution of Coherent Nanostructures in Athabasca Bitumen and Maya Crude Oil
Why this work is in the frame
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Bibliographic record
Abstract
The size, shape, and composition of coherent nanostructures in hydrocarbon resources from natural gas to bitumen continue to be debated. Much research has focused on asphaltenes and other fractions separated chemically from their underlying hydrocarbon resources and their behaviors exhibited when these materials are remixed or added to other fluids. In this contribution, we step back and pose a simple question: “what can be learned about nanostructure size and composition in hydrocarbon resources using a simple physical separation technique?” To this end, Athabasca bitumen and Maya crude oil were partitioned, without solvent addition, using nanofilters at 473 K. These feeds possess significant asphaltene contents and, in the case of Athabasca bitumen, significant mineral matter content, which facilitate the measurement and interpretation of standard chemical analyses obtained for the feeds, and for permeates and retentates produced. Organic and inorganic elemental composition and saturate, aromatic, resin, and asphaltene fractions were obtained. Details of the experimental and analytical techniques employed and their limitations are presented. Pentane asphaltene-enriched nanostructures, and mineral-matter-rich nanostructures, with distinct size distributions, and independent behaviors are identified. The asphaltene-rich nanostructures are shown to possess a broad size distribution, and they do not associate preferentially with other constituents such as resins. Implications of these key results are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it