Optimization of a microwave-assisted extraction procedure for the determination of selected alkyl, aryl, and halogenated phenols in sewage sludge and biosolids
Bibliographic record
Abstract
A microwave-assisted extraction method for the determination of 15 alkyl, aryl, and halogenated phenols in sewage sludge and biosolids samples was developed and optimized. The effects of solvent, temperature, time, moisture content, acid, and number of extractions on the recovery of phenols were evaluated. Results indicated that extraction solvent had the greatest impact on the recovery of all phenols while pH had the largest effect on recovery of hexachlorophene and pentachlorophenol. Wet sludge samples were extracted with acetone-hexane mixture in the presence of glacial acetic acid. The extract was evaporated, acetylated by acetic anhydride and cleaned up by silica gel. For dry sludge samples, an optional procedure for the simultaneous extraction and acetylation of phenols was also proposed. Triclosan (TCS) and the alkyl and aryl phenols in sludge extracts were analyzed by gas chromatography-mass spectrometry (GC-MS) in electron-impact mode while polyhalogenated phenols were analyzed by GC-MS in negative ion chemical ionization mode. Method detection limits were ca. 200 ng/g for nonylphenol, <25 ng/g for TCS and other alkyl and aryl phenols, and <5 ng/g for other halogenated phenols. This method has been applied to the determination of phenolic compounds in over 150 sludge and biosolids samples since 2009.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".