Characterization of the fate of primary and re-precipitated silver nanoparticles in lake water model systems
Bibliographic record
Abstract
The increasing use of silver nanoparticles (nAg) in products and associated releases to the environment necessitates a thorough understanding of the environmental fate and transformations of these potentially toxic nanomaterials to inform environmental risk assessments. Herein, the physical and chemical transformations of nAg in natural lake water samples were investigated. Lake water systems containing filtered and unfiltered lake water (FLW and UFLW) were spiked with 80 nm polyvinylpyrrolidone-coated nAg (nAgpristine) at 6 μg/L and were maintained under quiescent or mixed conditions in the dark for up to 44 days. Aliquots withdrawn from the water column contained smaller re-precipitated nAg (r-nAg, diameter ∼26 nm) formed by precipitation of Ag+ released by oxidative dissolution of nAgpristine. The number concentrations of r-nAg and nAgpristine were comparable. In FLW, agglomerates of r-nAg and the partially dissolved nAgpristine were formed under quiescent conditions and their settling accelerated after 14 days, but no settling occurred in the mixed systems. In UFLW, heteroagglomerates of r-nAg and the partially dissolved nAgpristine with natural colloids formed and induced sedimentation in both quiescent and mixed systems. A fraction of the r-nAg formed and the larger (>40 nm) or primary n-Ag (comprised of partially dissolved nAgpristine and its agglomerates with r-nAg or itself) were persistent in the water column for several weeks. Under quiescent conditions, more p-nAg remained suspended in FLW (15.7%, relative to number of nAgpristine dosed) than in UFLW (5.9%), whereas more r-nAg persisted in UFLW (2.6%) than in FLW (0.6%). Thus, the size distributions and fractions of nAg persisting in the water column can change significantly depending on water chemistry and mixing conditions.
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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".