Silver nanoparticles enter the tree stem faster through leaves than through roots
Bibliographic record
Abstract
A major environmental pollution problem is the release into the atmosphere of particulate matter, including nanoparticles (NPs), which causes serious hazards to human and ecosystem health, particularly in urban areas. However, knowledge about the uptake, translocation and accumulation of NPs in plant tissues is almost completely lacking. The uptake of silver nanoparticles (Ag-NPs) and their transport and accumulation in the leaves, stems and roots of three different tree species, downy oak (Quercus pubescens Willd.), Scots pine (Pinus sylvestris L.) and black poplar (Populus nigra L.), were assessed. In the experiment, Ag-NPs were supplied separately to the leaves (via spraying, the foliar treatment) and roots (via watering, the root treatment) of the three species. Uptake, transport and accumulation of Ag were investigated through spectroscopy. The concentration of Ag in the stem was higher in the foliar than in the root treatment, and in poplar more than in oak and pine. Foliar treatment with Ag-NPs reduced aboveground biomass and stem length in poplars, but not in oaks or pines. Species-specific signals of oxidative stress were observed; foliar treatment of oak caused the accumulation of H2O2 in leaves, and both foliar and root treatments of poplar led to increased O2- in leaves. Ag-NPs affected leaf and root bacteria and fungi; in the case of leaves, foliar treatment reduced bacterial populations in oak and poplar and fungi populations in pine, and in the case of roots, root treatment reduced bacteria and increased fungi in poplar. Species-specific mechanisms of interaction, transport, allocation and storage of NPs in trees were found. We demonstrated definitively that NPs enter into the tree stem through leaves faster than through roots in all of the investigated tree species.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".