Physicochemical Characteristics of Polymer-Coated Metal-Oxide Nanoparticles and their Toxicological Effects on Zebrafish (<i>Danio rerio</i>) Development
Bibliographic record
Abstract
Coated nanoparticles (NPs) will end up in the environment due to their proposed use in agricultural applications and may potentially cause toxic effects due to their unique properties. To determine the effects of coated NPs on zebrafish (Danio rerio) development, we tested aqueous poly(acrylic acid) (PAA)-coated metal-oxide NPs including TiO2, ZnO, Fe2O3, and CeO2, as well as the polymer coating alone (nanocapsule). Zebrafish embryos were exposed to NPs over a 72 h period at 1, 10, 50, 100, 200, 400, 800, 1200, 1600, and 2000 mg/L to measure various end points. We also ran free metal controls. Time-dependent changes in physicochemical properties of NPs were characterized using dynamic light scattering. Dissolution experiments over 72 h showed minimal free metals were present in stock suspensions and released from the NPs. Interestingly, nanocapsules (≥ 800 mg/L) cause inhibition of hatch, and we suggest that a low pH environment may explain this effect. This study has also demonstrated that CeO2 NPs and nanocapsules containing Nile red are able to traverse the chorion. Overall, our findings indicate that each NP type is stable and neither the NP or encapsulating PAA coating causes apparent toxicity to developing zebrafish.
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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".