Role of Particle Size and Soil Type in Toxicity of Silver Nanoparticles to Earthworms
Bibliographic record
Abstract
Silver nanoparticles (NPs) are an emerging contaminant of concern due to their increased use. The earthworm Eisenia fetida was exposed to a range of concentrations of AgNO 3 and two polyvinylpyrolidone coated Ag NPs with different particle size distributions. They were exposed in two different soils: a naturally occurring sandy loam and a standardized artificial soil. The AgNO 3 significantly reduced E. fetida growth and reproduction at 7.41 ± 0.01 mg kg −1 Ag in the sandy loam but only reproduction was affected at concentrations of 94.1 ± 3.2 mg kg −1 in the artificial soil. In the artificial soil, significant (α = 0.05) reproductive toxicity was only observed in organisms exposed to the Ag NPs at concentrations approximately eight times higher than those at which the effects from ionic Ag were observed. Eisenia fetida exposed to either AgNO 3 or Ag NPs in the sandy loam accumulated significantly (α = 0.05) higher concentrations of Ag than those exposed in the artificial soil and had higher bioaccumulation factors. Earthworms exposed to AgNO 3 also accumulated significantly higher concentrations of Ag than those exposed to Ag NPs. No differences in toxicity were observed between the two size distributions. Extended x‐ray absorption fine structure spectroscopy analysis of the soils indicated that the Ag was approximately 10 to 17% Ag(I), suggesting that Ag ions may be responsible for effects on growth and development caused by exposure to Ag NPs. Our results also suggest that soil type is a more important determinant of Ag accumulation from Ag NPs than particle size.
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.
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 | 0.001 | 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".