Effects of ZnO Nanoparticle on Photo-Protection and Insecticidal Synergism of Rotenone
Bibliographic record
Abstract
Rotenone has an effective insecticidal activity. However, the photodegradation of rotenone under sunlight or UV (ultraviolet light) leads to negative effects on its insecticidal activity and persistence. This study examined the photo-protection of rotenone when exposed to UV combinated with various nanoparticles. The remaining concentration of rotenone was analyzed by LC-MS/MS (liquid chromatography-triple quadrupole tandem mass spectrometry) at particular intervals. It indicated that various nanoparticles had different effects and combination with ZnO nanoparticle provided remarkable degree of photo-protection of rotenone in UV radiation. In comparision with ZnO, SiO2 nanoparticle provided moderate degree of photo-protection of rotenone in UV radiation. In addition, TiO2, Fe2O3 and CuO nanoparticle exerted catalytic degradation effects on rotenone to a certain degree.The combination of rotenone and ZnO nanoparticle(4:1) increased the effciency of mortality to the highest compared with the same concentration of sole rotenone or ZnO nanoparticle treatment alone and their co-toxicity coefficient was 128.63. ZnO nanoparticle has good UV photo-protective properties and insecticidal synergism on rotenone. The application of this proposed method can provide significant and practical guidance for improving the photostability and insecticidal activity of rotenone as well as other biopesticides.
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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".