Application of automated solid-phase microextraction to determine haloacetonitriles, haloketones, and chloropicrin in Canadian drinking water
Bibliographic record
Abstract
An automated headspace solid-phase microextraction gas chromatography mass spectrometry (HS-SPME-GC-MS) method was developed to monitor the occurrence of selected haloacetonitriles (HANs), haloketones (HKs), and chloropicrin (CP) in drinking water supplies. The method was rapid with analysis time of 30 min, including extraction and chromatographic run. Chemical ionization (CI) was used to increase the sensitivity of the method for the HKs. SPME fibers with seven different coatings including commercial polyacrylate (PA), carbowax/divinylbenzene (CW/DVB), polydimethylsiloxane (PDMS), polydimethylsiloxane/divinylbenzene (PDMS/DVB), carboxen/polydimethylsiloxane (CAR/PDMS), divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS), and a novel custom-made polydimethylsiloxane/divinylbenzene-N-vinylpyrrolidone (PDMS/DVB-NVP) were evaluated. The DVB/CAR/PDMS fiber was found more suitable for the range of the analytes and the novel PDMS/DVB-NVP fiber more efficient for the brominated acetonitriles under the experimental conditions. Method detection limits (MDLs) for the chlorinated acetonitriles and CP varied between 2 and 40 ng/L and for the brominated acetonitriles and HKs between 100 and 180 ng/L. Relative standard deviations (RSD %) of measurements were 4–7%. The method was applied in parallel with a liquid–liquid extraction gas chromatography electron capture detection (LLE-GC-ECD) method (EPA Method 551.1) to the analysis of drinking water samples from eight Canadian water treatment and distribution systems. The results generated by the two methods showed good agreement.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".