Biosynthesized Chitosan-Coated Silver Nanoparticles: Insecticide Activity and Sublethal Effects Against Drosophila suzukii (Diptera: Drosophilidae)
Bibliographic record
Abstract
The overuse of synthetic pesticides has triggered resistance in insect pests and caused severe environmental impacts, emphasizing the urgent need for sustainable alternatives in Integrated Pest Management (IPM). This study aimed to biosynthesize and characterize chitosan-coated silver nanoparticles (AgChNPs) using Galega officinalis leaf extract and evaluate their insecticidal effects against Drosophila suzukii (Diptera: Drosophilidae), a key pest of fruit crops worldwide. The biosynthesized AgChNPs (257.2 nm) were polydisperse, crystalline, and stable, as confirmed by UV-vis spectroscopy, dynamic light scattering (DLS), X-ray diffraction (XRD), and transmission electron microscopy (TEM). AgChNPs exhibited strong toxicity across multiple developmental stages. Combined larvicidal and pupicidal activity reached 48.3% and 73.3% at 500 and 1000 ppm, respectively, significantly affecting immature stages. As a consequence, adult emergence declined to 46.7%, 51.7%, and 26.7% at 250, 500, and 1000 ppm. Among emerged adults, 71.7% displayed sublethal effects, with 62.8% showing morphological malformations (deformed wings, dehydration) and 37.2% presenting cuticle demelanization. Adulticidal bioassays revealed progressive mortality over 48 h, with 96% mortality at 1000 ppm. Overall, AgChNPs caused acute and chronic toxicity, reduced adult emergence, and induced severe morphological alterations, demonstrating their potential as a sustainable nanotechnological tool for effective pest control within IPM programs.
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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) | 0.000 | 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 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".