Large Volume Cold On-Column Injection for Gas Chromatography−Negative Chemical Ionization−Mass Spectrometry Analysis of Selected Pesticides in Air Samples
Bibliographic record
Abstract
A new gas chromatographic method is described for the analysis of fungicides captan, captafol, and folpet from organic extracts of air samples using large volume injection (LVI) via a cold on-column (COC) inlet coupled with gas chromatography-negative chemical ionization-mass spectrometry (GC-NCI-MS). Although standard split/splitless injection due to high injection port temperatures (>225 degrees C) have been shown to degrade these thermally labile fungicides, COC injection minimizes degradation. Insecticides such as chlorpyrifos and diazinon were also examined to show added selectivity. By using a solvent vapor exit with the COC inlet, injection volumes of 10-100 microL can be made to lower detection levels. GC-NCI-MS was compared to GC-electron impact ionization-mass spectrometry for each pesticide using LVI-COC injections and was found to be 2-80 times more sensitive, depending on the pesticide. Method detection limit (MDL) values with 100 microL injections were 2.5 microg L-1 for captan, folpet, and diazinon, 5.0 microg L-1 captafol, and 1.0 microg L-1 for chlorpyrifos, with the normal working range examined for sample analysis from MDL to 100 microg L-1. Detection of all pesticides except captafol, used only in the United States but not Canada, was demonstrated from air samples taken from Abbotsford, British Columbia, Canada.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".