Shape-Dependent Toxicity of Silver Nanoparticles on Freshwater Cnidarians
Bibliographic record
Abstract
Silver nanoparticles (AgNPs) are increasingly used in various consumer products, leading to their inadvertent release in aquatic ecosystems. The toxicity of AgNPs could be associated with the leaching of ionic Ag but also with the size, shape and surface properties. The purpose of this study was to test the null hypothesis that toxicity of AgNPs was independent of shape in the invertebrate Hydra vulgaris. The hydranths were exposed to increasing concentrations of ionic Ag and AgNPs of three different shapes (spherical, cubic and prismatic) with the same size and coating (polyvinylpyrrolidone). The data revealed that between 68% and 75% of total Ag remained in solution after the 96 h exposure period, while 85−90% of ionic Ag remained in solution. The 96 h lethal concentration (LC50) was lower with ionic (4 µg/L) and spherical AgNPs (56 µg/L), based on irreversible morphological changes such as loss of tentacles and body disintegration. Cubic and prismatic AgNPs were not toxic at a concentration of <100 µg/L. The sublethal toxicity was also determined at 96 h based on characteristic morphological changes (clubbed and/or shortened tentacles) and showed the following toxicity: ionic (2.6 µg/L), spherical (22 µg/L) and prismatic (32.5 µg/L) AgNPs. The nanocube was not toxic at this level. The data indicated that toxicity was shape-dependent where nanoparticles with a low aspect ratio in addition to high circularity and elongation properties were more toxic at both the lethal and sublethal levels. In conclusion, the shape of AgNPs could influence toxicity and warrants further research to better understand the mechanisms of action at play.
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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".