Rapid Oil Sands Extraction Processing Stream Composition Determination by a <sup>19</sup> Fluorine Nuclear Magnetic Resonance Spectrometer with Fluorinated Tracers
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide A benchtop 19 F-NMR spectrometer, coupled with the addition of fluorine-containing tracers (hereafter referred to as the 19 F-NMR method), was explored as an alternative to the conventional Dean–Stark method for determining the composition of the oil sands slurry. Trifluorotoluene and sodium trifluoroacetate were identified as oleophilic and hydrophilic tracers, respectively, enabling accurate quantification of bitumen and water in both synthetic and real oil sands samples. Bitumen (0.5–90 wt %) and water (5–95 wt %) were accurately measured, demonstrating the method’s suitability for a broad range of slurry types, including oil sands ore, bitumen froth, middlings, fluid fine tailings, and coarse sand tailings. For samples containing bitumen <0.5 wt %, significant deviations were observed due to insufficient tracer dilution from the small bitumen volume, leading to minimal change of signal integrated intensity and high fluctuations that hindered reliable detection. Solid content was calculated by mass balance, eliminating the need for thermal drying. The 19 F-NMR method provides significant advantages over the Dean–Stark method in terms of analysis time, cost, safety, and environmental impact; however, unlike the Dean–Stark method, it is not suitable for slurry samples with under 0.5 wt % bitumen. Overall, this study highlights the 19 F-NMR method as a promising and sustainable alternative for oil sands composition analysis suitable for both laboratory and potential on-site applications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".