Thermal Degradation of Conventional and Nanoencapsulated Azoxystrobin due to Processing in Water, Spiked Strawberry, and Incurred Strawberry Models
Bibliographic record
Abstract
Nanoencapsulated formulations of pesticides have been recently developed, and some products are now marketed for specific applications in agriculture. Pesticide residues present in raw agricultural products can degrade or react during food processing steps. To date, the fate of nanopesticides during food processing has not been well described. In this study, the thermal degradation of azoxystrobin (AZOX) in conventional and nanoencapsulated (Allosperse and nSiO 2 ) formulations was first assessed in water, spiked strawberry, and incurred strawberry models. The thermal degradation followed first-order kinetics when heated at 100 °C in the water model. The thermal degradation of AZOX in nanoformulations in strawberry models (18% AZOX decrease) was comparable to or lower than in the conventional formulation (21%), possibly due to the nanocarriers protecting the active ingredient from hydrolytic degradation. Out of 32 thermal degradation products (TDPs), only two were detected in both the spiked water and strawberry models, indicating differences in the thermal degradation reactions for AZOX in these two models. Identical TDPs were detected for both conventional and nanoformulations for each specific model, except for the absence of one (TDP22) in the nSiO 2 formulations. The nanoencapsulation of AZOX did not result in new TDPs in any of the matrices. Only six of the TDPs detected in water, four in spiked strawberries, and two in incurred strawberries have been previously reported in environmental studies on the metabolism of AZOX. Based on the observed TDPs, AZOX thermal degradation pathways include ether cleavage, hydrolysis, demethylation, and decarboxylation. Overall, although nanocarriers have no impact on the degradation product types, nanocarriers had a slight but significant impact on the degradation rate of pesticide active ingredients.
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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 | 0.000 | 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) | 0.001 | 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 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".