Liquid Chromatography–Electron Capture Negative Ionization–Tandem Mass Spectrometry Detection of Pesticides in a Commercial Formulation
Bibliographic record
Abstract
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| 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) | 0.000 | 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 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".