First evidence of nanoparticle uptake through leaves and roots in beech (<i>Fagus sylvatica</i> L.) and pine (<i>Pinus sylvestris</i> L.)
Bibliographic record
Abstract
Trees have been used for phytoremediation and as biomonitors of air pollution. However, the mechanisms by which trees mitigate nanoparticle pollution in the environment are still unclear. We investigated whether two important tree species, European beech (Fagus sylvatica L.) and Scots pine (Pinus sylvestris L.), are able to take up and transport differently charged gold nanoparticles (Au-NPs) into their stem by comparing leaf-to-root and root-to-leaf pathways. Au-NPs were taken up by roots and leaves, and a small fraction was transported to the stem in both species. Au-NPs were transported from leaves to roots but not vice versa. Leaf Au uptake was higher in beech than in pine, probably because of the higher stomatal density and wood characteristics of beech. Confocal (3D) analysis confirmed the presence of Au-NPs in trichomes and leaf blade, about 20-30 μm below the leaf surface in beech. Most Au-NPs likely penetrated into the stomatal openings through diffusion of Au-NPs as suggested by the 3D XRF scanning analysis. However, trichomes were probably involved in the uptake and internal immobilization of NPs, besides their ability to retain them on the leaf surface. The surface charge of Au-NPs may have played a role in their adhesion and uptake, but not in their transport to different tree compartments. Stomatal conductance did not influence the uptake of Au-NPs. This is the first study that shows nanoparticle uptake and transport in beech and pine, contributing to a better understanding of the interactions of NPs with different 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 | 0.000 |
| 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".