High-Throughput Quantitation of Neonicotinoids in Lyophilized Surface Water by LC-APCI-MS/MS
Bibliographic record
Abstract
Abstract Background: Neonicotinoids are among the most widely used insecticides. Recently, there has been concern associated with unintended adverse effects on honeybees and aquatic invertebrates at low parts-per-trillion levels. Objective: There is a need for LC-MS/MS methods that are capable of high-throughput measurements of the most widely used neonicotinoids at environmentally relevant concentrations in surface water. Methods: This method allows for quantitation of acetamiprid, clothianidin, imidacloprid, dinotefuran, nitenpyram, thiacloprid, and thiamethoxam in surface water. Deuterated internal standards are added to 20 mL environmental samples, which are concentrated by lyophilisation and reconstituted with methanol followed by acetonitrile. Results: A large variation of mean recovery efficiencies across five different surface water sampling sites within this study was observed, ranging from 45 to 74%. This demonstrated the need for labelled internal standards to compensate for these differences. Atmospheric pressure chemical ionization (APCI) performed better than electrospray ionization (ESI) with limited matrix suppression, achieving 71–110% of the laboratory fortified blank signal. Neonicotinoids were resolved on a C18 column using a 5 min LC method, in which MQL ranged between 0.93 and 4.88 ng/L. Conclusions: This method enables cost effective, accurate, and reproducible monitoring of these pesticides in the aquatic environment. Highlights: Lyophilization is used for high throughput concentration of neonicotinoids in surface water. Variations in matrix effects between samples was greatly reduced by using APCI compared with ESI. Clothianidin and thiamethoxam were detected in all samples with levels ranging from below method quantitation limit to 65 ng/L.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".