Impact of zinc and zinc oxide nanoparticles on the physiological and biochemical processes in tomato and wheat
Bibliographic record
Abstract
In this study, the effects of various concentrations of zinc and zinc oxide nanoparticles (nZn, nZnO) were evaluated in tomato and wheat. Results showed that at lower concentrations, nZn and nZnO augmented seed germination and growth parameters, whereas with higher concentrations, the nanoparticles reduced these traits. Zn concentrations corresponding to Zn dissolved (3–23 mg Zn·L−1) from nanoparticles (NPs) did not significantly affect the germination indices in either species. Compared with the bulk counterparts of ZnO, NPs exerted more toxicity on seed germination, growth parameters, and chlorophyll and carotenoid contents, and also increased Zn bioaccumulation more. More often than not, nZnO provoked more adverse symptoms than nZn at equivalent concentrations. In both species, the Zn accumulation in roots and shoots followed the order: Zn2+ ions > nZn > nZnO > bulk ZnO > control. Exposure to 200 mg Zn·L−1 nZn and nZnO increased H2O2 accumulation and malondealdehyde (MDA) levels, which were more pronounced in tomato than wheat. The results suggested that the toxicity of NPs could be due to the particle itself, or from the Zn2+ ions dissolved from NPs. Moreover, nanotoxicity, like other stresses, caused oxidative stress in both plants, and the differences in proline accumulation and the antioxidant enzyme activities of leaves, especially APX activity, at least in part, explained the higher sensitivity of tomato to NPs than wheat.
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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".