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Record W4404617617 · doi:10.3390/molecules29235520

Silver Nanoparticles as a Potent Nanopesticide: Toxic Effects and Action Mechanisms on Pest Insects of Agricultural Importance—A Review

2024· review· en· W4404617617 on OpenAlexaff
Daniel Martínez-Cisterna, Olga Rubilar, Gonzalo Tortella, Lingyun Chen, Manuel Chacón-Fuentes, Marcelo Lizama, Pablo Parra, Leonardo Bardehle

Bibliographic record

VenueMolecules · 2024
Typereview
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsUniversity of Alberta
FundersAgencia Nacional de Investigación y Desarrollo
KeywordsContext (archaeology)Membrane permeabilityHemipteraBiologyReactive oxygen speciesInsectBiotechnologyNanotechnologyChemistryEcologyCell biologyBiochemistryMaterials scienceMembrane

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.145
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.311
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreReview

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".

Quick stats

Citations34
Published2024
Admission routes1
Has abstractyes

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