Determination of Naphthenic Acids in Oil Sands Tailings: Extraction, Cleanup, and Molecular Characterization
Bibliographic record
Abstract
This study reports an optimized workflow for the quantitative profiling of naphthenic acids (NAs) in oil sands tailings. Systematic screening of solvent composition and agitation conditions identified two successive leaches with 0.5 M NaOH, each shaken orbitally for 40 min, as the most efficient protocol for liberating NAs from freeze-dried solids. The alkaline extracts were purified on hydrophilic–lipophilic balance cartridges and eluted with formic-acidified methanol, followed by reconstitution of the dried fraction in 1:1 methanol–water, with maximized recovery. Purified samples were resolved by ultraperformance liquid chromatography and interrogated with quadrupole time-of-flight mass spectrometry. With application to three representative waste streams, fluid fine tailings (FFT), mature fine tailings (MFT), and FFT treated in a permanent aquatic storage structure, the method yielded total NA concentrations of 56–112 mg kg –1 . Despite these differences in abundance, all tailings shared congruent class distributions (O 2 –O 6 ) and nearly identical carbon-number and double-bond-equivalent ( Z number) patterns for the dominant O 2 family, irrespective of the mine source. The protocol affords a robust, high-throughput tool for routine surveillance of NA speciation in oil sands tailings, enabling operators to track compositional evolution during treatment and to inform evidence-based reclamation strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".