Discerning the Sources of Silver Nanoparticle in a Terrestrial Food Chain by Stable Isotope Tracer Technique
Bibliographic record
Abstract
The increasing use of silver-containing nanoparticles (NPs) in commercial products has led to NP accumulation in the environment and potentially in food webs. Identifying the uptake pathways of different chemical species of NPs, such as Ag 2 S-NP and metallic AgNPs, into plants is important to understanding their entry into food chains. In this study, soybean Glycine max L. was hydroponically exposed to Ag 2 S-NPs via their roots (10–50 mg L –1 ) and stable-isotope-enriched 109 AgNPs via their leaves [7.9 μg (g fresh weight) −1 ]. Less than 29% of Ag in treated leaves (in direct contact with 109 AgNP) was accumulated from root uptake of Ag 2 S-NPs, whereas almost all of the Ag in soybean roots and untreated leaves sourced from Ag 2 S-NPs. Therefore, Ag 2 S-NPs are phytoavailable and translocate upward. During trophic transfer the Ag isotope signature was preserved, indicating that accumulated Ag in snails most likely originated from Ag 2 S-NPs. On average, 78% of the Ag in the untreated leaves was assimilated by snails, reinforcing the considerable trophic availability of Ag 2 S-NPs via root uptake. By highlighting the importance of root uptake of Ag 2 S-NPs in plant uptake and trophic transfer to herbivores, our study advances current understanding of the biogeochemical fate of Ag-containing NPs in the terrestrial environment.
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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".