Dissolution of Silver Nanoparticles in Stratified Estuarine Mesocosms and Silver Accumulation in a Simple Planktonic Freshwater Trophic Chain
Bibliographic record
Abstract
The increasing presence of nanomaterials in consumer products has led the scientific community to study the environmental fate of these contaminants of emerging concern. Silver nanoparticles, used mainly for their antibacterial properties, are among the most common nanomaterials. Understanding their transformations and interactions with living organisms, especially under environmentally relevant conditions that can modify metal bioavailability, is a crucial step in the study of their impacts on aquatic ecosystems. In the present study, citrate-coated silver nanoparticles (20 nm; 10 µg/L) were added to the surface freshwater layer of mesocosms simulating a stratified estuary. The investigation by dialysis of the nanoparticle dissolution showed that a large amount of total silver was found in the freshwater layer (and a very low amount in the seawater layer) and that 5–15% was in the form of dissolved silver. These results indicate that the halocline, separating fresh water from seawater, acted as a strong density barrier limiting the sedimentation of the nanoparticles. A simple trophic chain, composed of the freshwater alga Chlamydomonas reinhardtii and the invertebrate Daphnia magna, was used to determine silver bioavailability. This study suggests that citrate-coated silver nanoparticles do not significantly contribute to Ag accumulation by algae but may do so for invertebrates.
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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) | 0.000 | 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 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".