Ecotoxicity of selected nano‐materials to aquatic organisms
Bibliographic record
Abstract
Present knowledge concerning the ecotoxic effects of nano-materials is very limited and merits to be documented more fully. For this purpose, we appraised the toxicity of nine metallic nano-powders (copper zinc iron oxide, nickel zinc iron oxide, yttrium iron oxide, titanium dioxide, strontium ferrite, indium tin oxide, samarium oxide, erbium oxide, and holmium oxide) and of two organic nano- powders (fullerene-C60 and single-walled carbon nanotube or SWCNT). After a simple process where nano-powders (NPs) were prepared in aqueous solution and filtered, they were then bioassayed across several taxonomic groups including decomposers (bacteria), primary producers (micro-algae), as well as primary and secondary consumers (micro-invertebrates and fish). Toxicity data generated on the 11 NPs reflected a wide spectrum of sensitivity that was biological level-, test-, and endpoint-specific. With all acute and chronic tests confounded for these 11 NPs, toxicity responses spanned over three orders of magnitude: >463 mg/L (24 h LC50 of the invertebrate Thamnoplatyurus platyurus for fullerene-C60) / 0.3 mg/L (96 h EC50 of the invertebrate Hydra attenuata for indium tin oxide), that is a ratio of 1543. On the basis of the MARA (Microbial Array for Risk Assessment) assay toxic fingerprint concept, it is intimated that NPs may have different modes of toxic action. When mixed in a 1:1 ratio with a certified reference material (CRM) sediment, two solid phase assays and an elutriate assay, respectively, showed that five NPs (copper zinc iron oxide, samarium oxide, erbium oxide, holmium oxide, and SWCNT) were able to increase both CRM sediment toxicity and its elutriate toxicity. This initial investigation suggests that chemicals emerging from nanotechnology may pose a risk to aquatic life in water column and sediment compartments and that further studies on their adverse effects are to be encouraged.
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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) | 0.001 | 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".