Sorption of Athabasca Vacuum Residue Constituents on Synthetic Mineral and Process Equipment Surfaces from Mixtures with Pentane
Bibliographic record
Abstract
Deposition of organic material on mineral and process equipment surfaces poses production, transport, and refining challenges for the petroleum industry. For high-asphaltene content hydrocarbon resources, such as bitumen, deposits are frequently assumed to be asphaltene rich. In this work, deposits formed from Athabasca vacuum residue (AVR) comprising 32 wt % asphaltenes + pentane mixtures on acidic (FeS, SiO 2 ) and basic (Fe 2 O 3 /FeOOH/FeO, Ni/NiO/NiOH) substrates are analyzed using X-ray photoelectron spectroscopy. Control experiments with pure compounds are used to confirm experimental protocols and to address substrate contamination, which interferes with deposit composition measurements, if the organic deposit is thin or surface coverage is partial. Substrate properties are found to affect both deposit thickness and deposit composition. On basic substrates, deposits are thinner and are enriched in sulfur relative to AVR. On acidic substrates, deposits are thicker and are sulfur deficient relative to AVR, even though asphaltenes, which are rich in sulfur, sorb more strongly on acidic substrates in the absence of competition from other species. Deposit composition was also found to be invariant with the composition and phase behavior of the AVR + pentane mixtures. These results were not expected.
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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 | 0.000 | 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) | 0.002 | 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".