Pesticide Efficiency of Environment-Friendly Transition Metal-Doped Magnetite Nanoparticles
Bibliographic record
Abstract
This study explored the potential of Fe3O4, SnFe2O4, and CoFe2O4 nanoparticles as larvicidal and adulticidal agents against Aedes aegypti (A. aegypti) larvae and adults, which are vectors for various diseases. This research involved the synthesis of these nanoparticles using the coprecipitate method. The results indicate that CoFe2O4 nanoparticles are the most effective in both larvicidal and adulticidal activities, with complete mortality achieved after 96 h of exposure. SnFe2O4 nanoparticles also showed some larvicidal and adulticidal efficacy, although to a lesser extent than the CoFe2O4 nanoparticles. Fe3O4 nanoparticles exhibited minimal larvicidal and adulticidal effects at low concentrations but showed increased efficacy at higher concentrations. The study also revealed the superparamagnetic nature of these nanoparticles, making them potentially suitable for applications in aquatic environments, where A. aegypti larvae often thrive. Additionally, the nanoparticles induced observable damage to the gut structure of the mosquitoes and larvae, which could contribute to their mortality. Overall, this research suggests that CoFe2O4 nanoparticles, in particular, hold promise as environment-friendly and effective agents for controlling A. aegypti mosquitoes, which are responsible for the transmission of diseases such as dengue fever, Zika virus, and Chikungunya. Further studies and field trials are needed to validate their practical use in mosquito control 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".