Dynamic interplay of metal and metal oxide nanoparticles with plants: Influencing factors, action mechanisms, and assessment of stimulatory and inhibitory effects
Bibliographic record
Abstract
Nanoparticles (NPs) of metals and metal oxides have received increasing attention regarding their characteristic behavior in plant systems. The fate and transport of metal NPs and metal oxide NPs in plants is of emerging concern for researchers because they ultimately become part of the food chain. The widespread use of metal-based NPs (MBNPs) in plants has revealed their beneficial and harmful effects. This review addresses the main factors affecting the uptake, translocation, absorption, bioavailability, toxicity, and accumulation of MBNPs in different plant species. It appraises the mechanism of nanoparticle-plant interaction in detail and provides understanding of the estimation strategies for the associated pros and cons with this interplay. Critical parameters of NPs include, but are not limited to, particle size and shape, surface chemistry, surface charge, concentration, solubility, and exposure route. On exposure to MBNPs, the molecular, physiological, and biochemical reactions of plants have been assessed. We have filled knowledge gaps and answered research questions regarding the positive and negative effects of metal and metal oxide NPs on seed germination, callus induction, growth and yield of plant, nutritional content, antioxidants, and enzymes. Besides, the phytotoxicity, cytotoxicity, genotoxicity, and detoxification studies of MBNPs in plants have been outlined. Furthermore, the recent developments and future perspectives of the two-way traffic of interplay of MBNPs and plants have been provided in this comprehensive review.
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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 | 0.001 | 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".