Effects of exposure pathways on the accumulation and phytotoxicity of silver nanoparticles in soybean and rice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it