Environmental Fate of Silver Nanoparticles in Boreal Lake Ecosystems
Bibliographic record
Abstract
Silver nanoparticles (AgNPs) are currently the most commonly used nanoparticles in consumer products, yet their environmental fate in natural waters is poorly understood. In the present study, we investigated the persistence, transformations and distribution of polyvinylpyrrolidone (PVP) and citrate (CT) coated AgNPs in boreal lake mesocosms dosed either with a 6-week chronic regimen or a one-time pulse treatment at environmentally relevant dosing levels. In the chronic treatments, total Ag (TAg) concentrations reached ∼40% of target concentrations by the end of the experiment, and in the pulsed mesocosms, TAg dissipated slowly, with a half-life of ∼20 days. Sediments and periphyton on the mesocosm walls were an important sink for Ag. We found little effect of AgNP loading and surface coating on the persistence of TAg. There were also no differences between treatments in the degree of agglomeration of AgNPs, as indicated by the accumulation and distribution of Ag in the particulate and colloidal fractions. The low ionic strength and relatively high dissolved organic carbon concentrations in the lake water likely contributed to the relative stability of AgNP in the water column. The low concentrations of dissolved Ag (<1 μg L(-1)) in the size fraction <3 kDaA reflect the importance of natural ligands in controlling the concentrations of Ag released by dissolution of AgNPs. Overall, these data indicate that AgNPs are relatively stable in the tested lake environment and appear to result in quantities of highly toxic ionic Ag(+) that are below our limit of detection.
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 | 0.000 | 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".