Liquid Chromatography–Electron Capture Negative Ionization–Tandem Mass Spectrometry Detection of Pesticides in a Commercial Formulation
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Negative chemical ionization (NCI) and electron-capture negative ionization (ECNI) are gas chromatography–mass spectrometry (GC–MS) techniques that generate negative ions in the gas phase for compounds containing electronegative atoms or functional groups. In ECNI, gas-phase thermal electrons can be transferred to electrophilic substances to produce M –• ions and scarce fragmentation. As a result of the electrophilicity requirements, ECNI is characterized by high-specificity and low background noise, generally lower than EI, offering lower detection limits. The aim of this work is to explore the possibility of extending typical advantages of ECNI to liquid chromatography–mass spectrometry (LC–MS). The LC is combined with the novel liquid-EI (LEI) LC–EIMS interface, the eluent is vaporized and transferred inside a CI source, where it is mixed with methane as a buffer gas. As proof of concept, dicamba and tefluthrin, agrochemicals with herbicidal and insecticidal activity, respectively, were chosen as model compounds and detected together in a commercial formulation. The pesticides have different chemical properties, but both are suitable analytes for ECNI due to the presence of electronegative atoms in the molecules. The influence of the mobile phase and other LC- and MS-operative parameters were methodically evaluated. Part-per-trillion (ppt) detection limits were obtained. Ion abundances were found to be stable with quantitative linear detection, reliable, and reproducible, with no influence from coeluting interfering compounds from the sample matrix.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".