A Novel Water-Swelling Sampling Probe for in Vivo Detection of Neonicotinoids in Plants
Bibliographic record
Abstract
Ecotoxicological risks of neonicotinoid insecticides are raising significant concerns, including their potential role in bee population declines. Neonicotinoids are water-soluble, systemic insecticides, and exposure of nontarget organisms such as pollinators occurs mainly through residues in nectar and pollens of flowering plants. To better elucidate the underlying mechanisms for such nontarget exposure, it is highly valuable to develop analytical capabilities for in vivo monitoring of neonicotinoids in live plants. In this study, we developed a novel biomimetic water-swelling solid-phase microextraction (SPME) probe, with limits of detection for neonicotinoids as low as 0.03 ng mL–1, and applied it for in vivo detection of seven neonicotinoids in plant sap. The preparation of this fiber was simple and free of stringent or complex physical–chemical reactions. Equilibrium in neonicotinoid accumulation on the fiber was reached in <10 min, allowing for near instantaneous sampling. The water-swelling fiber displayed much greater sampling capacity than the commercially available polydimethylsiloxane and polyacrylate fibers, good reproducibility (RSD of inter- and intrafiber <8.9% and 7.8%, respectively), and antibiofouling property (no loss in performance after 20 use cycles). After treating lettuce (Lactuca sativa L.) by foliar spray and soybean (Glycine max M.) by seed soaking, the in vivo assays provided a wealth of information, including changes in levels and distribution of neonicotinoids over time in the same plants. Kinetics and distribution patterns suggested that after treatment at the same level, neonicotinoids differed significantly in their levels in the sap. The in vivo sampling and monitoring of neonicotinoids in live plants may provide unique and much needed information in achieving breakthrough understanding of the connection between neonicotinoid use and pollinator exposure.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".