Antimicrobial effect of green synthesized TiO<sub>2</sub> nanoparticles against Cercospora canesces growth in mung bean
Bibliographic record
Abstract
Mung bean is one of the economically important leguminous crops generally grown in arid region of Pakistan. Mung bean grows best at 30-35℃ and is a nutrient enriched crop with higher amounts of starch (50%), proteins (18-25%), fat (3%) and fiber (3-4.5%). Many biotic factors affect crop production. Yield losses due to biotic factors are up to 40-80% in mung bean. Cercospora leaf spot (CLS) is very common disease in mung bean caused by hemi-biotrophic fungal pathogen Cercospora canescens. For sustainable agrocontrol there is need to develop alternative solutions for conventional methods. Green synthesized nanoparticles offer promising solution to mitigate adverse effect of agrochemicals and fungicides. In this review green synthesized TiO2 nanoparticles used to mitigate the disease severity by seed priming with 50mgL-1 and 150mgL-1 nanoparticles (NPs) suspension solution in mung bean. The result shows that 50mgL-1 Tio2-NPs inhibit fungal growth by 87.85% in in vitro and 74.07% in in vivo experiment. Tio2-NPs application improve root, shoot, leaf surface area, chlorophyll content, stomatal conductance and boost plant immunity by enhancing expression of the Pathogenesis-related (PR-1) gene in response stress. Therefore, present study indicates that green synthesized Tio2-NPs is an effective way to enhance the growth and productivity of mung bean by inhibiting C. canescens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".