Effects of exposure pathways on the accumulation and phytotoxicity of silver nanoparticles in soybean and rice
Bibliographic record
Abstract
The widespread use of silver nanoparticles (AgNPs) raises concerns both about their accumulation in crops and human exposure via crop consumption. Plants take up AgNPs through their leaves and roots, but foliar uptake has been largely ignored. To better understand AgNPs–plant interactions, we compared the uptake, phytotoxicity and size distribution of AgNPs in soybean and rice following root versus foliar exposure. At similar AgNP application levels, foliar exposure led to 17–200 times more Ag bioaccumulation than root exposure. Root but not foliar exposure significantly reduced plant biomass, while root exposure increased the malondialdehyde and H2O2 contents of leaves to a larger extent than did foliar exposure. Following either root or foliar exposure, Ag-containing NPs larger (36.0–48.9 nm) than the originally dosed NPs (17–18 nm) were detected within leaves. These particles were detected using a newly developed macerozyme R-10 tissue extraction method followed by single-particle inductively coupled plasma mass spectrometry. In response to foliar exposure, these NPs were stored in the cell wall and plamalemma of leaves. NPs were also detected in planta following Ag ion exposure, indicating their in vivo formation. Leaf-to-leaf and root-to-leaf translocation of NPs in planta was observed but the former did not alter the size distribution of the NPs. Our observations point to the possibility that fruits, seeds and other edible parts may become contaminated by translocation processes in plants exposed to AgNPs. These results are an important contribution to improve the risk assessment of NPs under environmental exposure scenarios.
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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".