The persistence and transformation of silver nanoparticles in littoral lake mesocosms monitored using various analytical techniques
Bibliographic record
Abstract
Environmental context Silver nanoparticles discharged with municipal wastewater may contaminate surface waters and harm aquatic ecosystems. We applied several analytical techniques to investigate the persistence and transformation of silver nanoparticles in a natural lake environment, and show, through multiple lines of evidence, that they persisted in lake water for several weeks after addition. The nanoparticles were releasing silver ions through dissolution, but these toxic ions were likely binding with natural organic matter in the lake water. Abstract Silver nanoparticles (AgNPs) may be released into surface waters, where they can affect aquatic organisms. However, agglomeration, dissolution, surface modifications and chemical speciation are important processes that control the toxicity of AgNPs. The purpose of the study was to apply various methods for monitoring the persistence and transformation of AgNPs added to littoral lake mesocosms. Analysis of total Ag showed that the levels in the mesocosms declined rapidly in the first 12 h after addition, followed by a slower rate of dissipation with a half-life (t1/2) of ~20 days. Analysis using single particle ICP-MS (spICP-MS) showed no evidence of extensive homo-agglomeration of AgNPs. The stability of AgNPs was likely due to the low ionic strength and high concentrations of humic-rich dissolved organic carbon (DOC) in the lake water. Analyses by spICP-MS, cloud point extraction (CPE) and asymmetric flow field flow fractionation coupled to ICP-MS (AF4-ICP-MS) all indicated that the concentrations of AgNP decreased over time, and the nanoparticles underwent dissolution. However, the concentrations of dissolved silver, which includes Ag+, were generally below detection limits when analysed by centrifugal ultrafiltration and spICP-MS. It is likely that the majority of free ions released by dissolution were complexing with natural organic material, such as DOC. An association with DOC would be expected to reduce the toxicity of Ag+ in natural waters. Overall, we were able to characterise AgNP transformations in natural waters at toxicologically relevant concentrations through the use of multiple analytical techniques that compensate for the limitations of the individual methods.
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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".