Nanostructured Insecticide Composition through the Incorporation of Natural Abamectin in β-Cyclodextrin: Activity against Aedes aegypti Larvae
Bibliographic record
Abstract
This study searched for a new pre-formulation based on the natural compound from the class of the avermectins, named abamectin (ABA), in order to improve its action against Aedes aegypti larvae by complexation with β-cyclodextrin (βCD). Concerning the low aqueous solubility of ABA, even in the presence of βCD, it was also invoked the strategy of working with hydrophobic nanoprecipitates (HNPs). For these purposes, molecular and supramolecular characterizations of 1:1 ABA/βCD complex and evaluation of its toxicity against A. aegypti larvae were performed. In the physical-chemistry characterizations, changes in the infrared spectra and thermal profiles in relation to precursors confirmed the occurrence of interactions between ABA and βCD in solid state. Nuclear magnetic resonance (NMR) data suggest the inclusion of ABA in βCD via benzofuran ring. Isothermal titration calorimetry (ITC) experiments allowed to verify the formation of complex with a 1:1 stoichiometry, which was entropy driven. The dynamic light scattering and zeta potential data from inclusion compounds demonstrated changes in the size of the ABA/βCD HNP if compared with the ABA HNP. Finally, the results for biological assays demonstrate that the strategy to prepare the inclusion compound led to an increase in the larvicidal activity in relation to free ABA.
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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".