Morphological Transformation of Silver Nanoparticles from Commercial Products: Modeling from Product Incorporation, Weathering through Use Scenarios, and Leaching into Wastewater
Bibliographic record
Abstract
There is increasing interest in the environmental fate and effects of engineered nanomaterials due to their ubiquitous use in consumer products. In particular, given the mounting evidence that dramatic transformations can occur to a nanomaterial throughout its product lifecycle, the appropriateness of using pristine nanomaterials in environmental testing is being questioned. Using a combination of transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and inductively coupled plasma-mass spectrometry (ICP-MS), this work examines the morphological and compositional effects of conditions mimicking a typical lifecycle of a nano-enabled product, from the production of the silver nanoparticle (AgNP)-laden textiles, through its use, laundering, and then finally, its leaching and incubation in the wastewater collection system. These simulated weathering conditions showed evidence for the transformation of AgNPs into AgCl and Ag2S. Incubation in raw wastewater had the most dramatic effect on the AgNPs in terms of transformation, no matter what initial weathering was applied to the NPs prior to incubation. However, despite extensive transformation noted, AgNPs were still present within all the samples after the use scenarios.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".