Development of a difenoconazole/propiconazole microemulsion and its antifungal activities against Rhizoctonia solani AG1-IA
Bibliographic record
Abstract
According to its physical and chemical properties, the composition of difenoconazole/propiconazole microemulsion was as follows: xylene as solvent, emulsifier HSH as surfactant and methanol as cosurfactant. The optimal formulation of difenoconazole/propiconazole microemulsion was oil/SAA/water = 1/2/5 (w/w), in which the SAA consisted of emulsifier HSH and methanol with ratio of 3/2 (w/w). The cloud point of difenoconazole/propiconazole microemulsion was 70 degrees C and its effective ingredient content was 2.5% measured by High Performance Liquid Chromatography (HPLC). Its heat storage stability was studied according to the standards. The decomposition rates of the difenoconazole/propiconazole microemulsion were merely 2.45%, 2.63% respectively and met the Food and Agriculture Organization (FAO) standards of pesticide microemulsion. Investigated by Transmission Electron Microscopy (TEM) the particle size of difenoconazole/propiconazole microemulsion was 90-140 nm and its antifungal activities against Rhizoctonia solani AG1-IA were tested and compared with that of Meiyu. We found that the inhibition rates in the difenoconazole/propiconazole microemulsion treatment group were significantly higher than that of the emulsion group with the same content of effective ingredients and the study also revealed that its inhibiting ability on the formation and germination of sclerotia was significant.
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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".