Evaluation of antifungal activity of silver nanoparticles against some phytopathogenic fungi and Trichoderma harzianum
Bibliographic record
Abstract
In vitro antifungal activity of silver nanoparticles, at concentrations of 6, 8, 10, 12, 14 and 16 ppm, was studied on five phytopathogenic fungi, and a biocontrol agent. Then effect of silver nanoparticle at 6 ppm (optimum concentration) was evaluated on Macrophomina phaseolina in greenhouse. For in vitro experiment, the fungal isolates were grown on potato dextrose agar medium amended with silver nanoparticles. Radial fungal growth was recorded after 1, 2, 3, 5 and 10 days and mycelial growth inhibition rates were calculated. The most sensitive fungus to nanoparticles was Pythium aphanidermatum, since all tested concentrations showed 100% inhibition during the 10 days of observation.The second most sensitive fungus was Sclerotinia sclerotiorum, since it was able to grow only at concentration of 6 ppm and M. phaseolina was the third in sensitivity since its growth was inhibited in all concentrations after three days. In greenhouse experiments, five treatments including no nanosilver-no pathogen (Negative control), no nanosilver +pathogen (Positive control), 6 ppm nanosilver– no pathogen, 6 ppm nanosilver +pathogen, Carboxin-Thiram (0.15%) +pathogen were compared. Four characters viz shoot and root fresh and dry weights were measured. Based on the greenhouse experimental results, treatments with nanosilver and fungicide gave higher yields than the positive control. The chemical control treatment had the highest measured parameters, while 6 ppm nanosilver +pathogen treatment had the same parameters as negative control. It may therefore be suggested to use nanosilver as a safer alternative to chemical fungicides for control of M. phaseolina.
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 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".