Pre‐treatment with extraction solvent yields higher recovery: Method optimization for efficient determination of polycyclic aromatic hydrocarbons in organic‐rich fine‐textured wastes
Bibliographic record
Abstract
Fluid fine tailings (FFT) contain numerous organic compounds, including polycyclic aromatic hydrocarbons (PAHs). Growing concerns of PAH toxicity warrants monitoring for environmental consequences and natural attenuation. Conventional Soxhlet extraction yields low (∼50%-60%) recovery of PAHs (naphthalene, phenanthrene, pyrene, dibenzofuran, fluorene, and dibenzothiophene) from FFT, which impedes accurate PAH determination. Therefore, an optimized method was developed in this study that included (1) selection of a suitable solvent, (2) enhancement of PAH recovery by pretreatment, (3) determination of optimal extraction time, and (4) optimization of sample cleanup procedure. Results showed that (1) dichloromethane (DCM) recovered significantly higher masses of PAHs from FFT than hexane (HEX), cyclohexane, or their mixtures with DCM; (2) pretreatment of FFT with DCM significantly improved PAHs recovery using either Soxhlet or mechanical shaking methods; (3) a 24-h Soxhlet extraction with pretreatment yielded the highest and the most consistent PAH recoveries; (4) DCM proved to be an efficient eluent for sample cleanup in silica gel column; and (5) consecutive cleanups with additional silica gel column removed excessive impurities without PAH losses. Therefore, this study developed an optimized method for PAH recoveries from FFT, achieving a pooled mean recovery of ∼94%. This method is applicable to other organic-rich fine-textured wastes such as sludge and clay sediments.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".