Characterization of Phenol and Alkyl Phenols in Organic Matrixes with Monoethylene Glycol Extraction and Multidimensional Gas Chromatography/Mass Spectrometry
Bibliographic record
Abstract
The use of monoethylene glycol as an extraction medium for removing phenol and alkyl phenols in organic matrixes such as hydrocarbons is introduced and combined with a practical analytical multidimensional gas chromatography approach. The analytical approach has been successfully developed for the characterization of phenol, cresols, xylenols, and alkyl phenols like 4-ethylphenol and 2,3,5-trimethylphenol. The technique employs a single-step extraction of the analytes with monoethylene glycol and sonication, followed by multidimensional gas chromatography with mass spectrometry in selected ion monitoring mode for the detection and quantitation. Extraction efficiency of phenol approached 100% while cresols, xylenols, and 4-ethylphenol were 97% or higher and 2,3,5-trimethylphenol was better than 91% under the analytical conditions used. With the technique described, a complete analysis can be conducted in less than 16 min. Reproducibility of area counts at two levels, namely, 5 ppm(w) and 50 ppm(w) over a period of 2 days were found to be less than 4% (n = 20). The analytes of interest was found to be linear over a range from 100 ppb(w) to 250 ppm(w) with correlation coefficient of at least 0.999 and detection limit of 50 ppb(w) . Spike recoveries from 500 ppb(w) to 250 ppm(w) for all analytes range from 96 to 102%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".