Silver Nanoparticles as a Potent Nanopesticide: Toxic Effects and Action Mechanisms on Pest Insects of Agricultural Importance—A Review
Bibliographic record
Abstract
Nanotechnology has been a promising plant protection discipline in recent years, attributed to the unique physicochemical properties exhibited at the nanoscale. In this context, silver nanoparticles (AgNPs) have been effective in various applications, including medical, industrial, and agronomic, and during the last few years, the control of insect pests has raised great interest. The present review mainly provides updated information about the use of AgNPs elaborated by different synthesis methods, such as biological (plants, microorganisms), physical, and chemical, and their effect against various insect species of agricultural importance belonging to the order Diptera, Coleoptera, Lepidoptera, and Hemiptera. The physiological and toxic effects of applying AgNPs are reported and characterized by developmental problems, mortality, weight reduction, interference with enzymatic activity, and anomalies in the life cycle. Moreover, in the final section, the action mechanisms through which AgNPs act on insects are also discussed, highlighting mechanisms such as alteration of transmembrane permeability, interruption of DNA replication, alteration of protein synthesis, and production of reactive oxygen species (ROS).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".