Enhancement of an Inhibition of Plant Pathogen Alternaria alternata with ZnO Nanoparticles and Lactobacillus plantarum
Bibliographic record
Abstract
The global fungal pathogen Alternaria alternata causes economically significant yield losses in various crop varieties through symptoms like leaf blight and seed rot.Because green synthesis of nanoparticles is safe, environmentally friendly, and cost-effective, it is preferred over other methods of synthesis.The purpose of this work was to evaluate the potential role of zinc oxide nanoparticles, or ZnO-NPs, and Lactobacillus plantarum crude extract in improving plant tolerance against A. alternata attack.ZnO nanoparticles were biosynthesized with water extract from dandelion roots and leaves.Atomic force microscopy (AFM) for ZnO nanoparticles were 87.62 nm in size, and scanning electron microscopy showed that they were spherical.FTIR spectroscopy for ZnO nanoparticles revealed functional groups which demonstrated formation of the ZnO structure and its purity, and UV-visible absorption spectrum showed a distinct peak at 355 nm.With a 76% inhibition rate, ZnO nanoparticles exhibited the highest antifungal activity against Alternaria alternata at a concentration of 200 mg/ml.Lactobacillus plantarum crude extract inhibited Alternaria alternata at 72%, but when ZnO nanoparticles and Lactobacillus plantarum crude extract were combined, the activity against Alternaria alternata increased to 93%.These findings support the use of that combination to combat a variety of plant-infecting pathogenic fungi.
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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".