Dissolution Methods for the Quantification of Metals in Oil Sands Bitumen
Bibliographic record
Abstract
Seven different dissolution methods proposed in the literature to prepare oil samples for metal analysis by inductively coupled plasma optical emission spectrometry (ICP-OES) were evaluated for V, Ni, Fe, and Ca analysis of bitumen. The dissolution methods evaluated were direct dilution, dry ashing, sulfated ashing, ultrasound assisted extraction, extraction induced emulsion breaking, detergentless microemulsification, and acid decomposition in closed vessels. The types of bitumen samples evaluated were raw bitumen, bitumen with emulsified water, and diluted bitumen. Only direct dilution and sulfated ashing could be recommended for the dissolution of all types of bitumen samples to quantify V and Ni content. Of these two methods, only sulfated ashing resulted in samples with good storage stability. Typical values for these elements in raw Athabasca bitumen were 224–226 (±14) μg V/g and 94 (±4) μg Ni/g. No firm recommendation about the most appropriate dissolution methods for analysis of Fe and Ca in bitumen could be made, and the values were typically around 10 μg/g or less for both Fe and Ca. When bitumen was stored as diluted bitumen in a toluene solution, it was found that 40–50% of the V-containing species separated from the bulk solution during a 28 day storage period. Over the same storage period <20% of the Ni-containing species separated from the bulk solution. These observations are relevant to bitumen upgrading, and some implications for metal removal from bitumen were discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".