Effects of copper and silver nanoparticles on growth of selected species of pathogenic and wood-decay fungi <i>in vitro</i>
Bibliographic record
Abstract
As research indicates a role for metal nanoparticles as fungicides, the work described here addresses the influence of copper nanoparticles (CuNPs) and silver nanoparticles (AgNPs) on the growth in vitro of pathogens causing damping-off as well as wood-decay fungi; i.e., Rhizoctonia solani (2 strains), Fusarium oxysporum, F. redolens and Phytophthora cactorum, along with Fistulina hepatica, Grifola frondosa, Meripilus giganteus and Sparassis crispa. Results indicate selective anti-fungal activity of the nanoparticles as applied at concentrations of 5, 15, 25 or 35 ppm. While neither nanoparticle affected P. cactorumor S. crispa, both inhibited growth in R. solani (strain 2), F. redolens and M. giganteus. R. solani (strain 1), F. oxysporum, F. hepatica and G. frondosa only showed sensitivity to higher concentrations of AgNPs, albeit with inhibitory impact on mycelial growth greater than with CuNPs. R. solani strains differed markedly in responses to both nanoparticles. Overall, the considerable toxicity of AgNPs and CuNPs to certain pathogens and wood-decay fungi indicates possible use in protecting nursery seedlings and safeguarding trees and wood, if with an awareness that certain fungi prove insensitive to both kinds of nanoparticle.
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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 | 0.000 | 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 | 0.001 |
| 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".