Regulatory ecotoxicity testing of engineered nanoparticles: are the results relevant to the natural environment?
Bibliographic record
Abstract
Engineered nanoparticles (ENPs) will be released to the environment during use or following the disposal of ENP-containing products and concerns have been raised over the risks of ENPs to the environment. Many studies have explored the toxicity of ENPs to aquatic organisms but these studies have usually been performed with little understanding of the ENPs' behaviour in the test media and the relationship between behaviour in the media to behaviour in natural waters. This study evaluated and compared the aggregation behaviour of four model gold nanoparticle (NP) types (coated with neutral, negative, positive and amphoteric cappings) in standard ecotoxicity test media and natural waters. The effects of humic acid (HA) and test organisms on aggregation were also investigated. In standard media, positive and neutral NPs were stable, whereas amphoteric and negative NPs generally showed substantial aggregation. In natural waters, amphoteric NPs were generally found to be stable, neutral and positive NPs showed substantial aggregation while negative NPs were stable in some waters and unstable in others. HA addition stabilised the amphoteric NPs, destabilised the positive NPs and had no effect on stability of negative NPs. The presence of invertebrates generally lowered the degree of particle aggregation while macrophytes had no effect. Given the dramatically different behaviours of ENPs in various standard media and natural waters, current regulatory testing may either under- or overestimate the toxicity of nanomaterials to aquatic organisms. Therefore, there is a pressing need to employ ecotoxicity media which better represent the behaviour of ENPs in natural system.
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 | 0.006 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".