Physicochemical characteristics and toxicity of surface-modified zinc oxide nanoparticles to freshwater and marine microalgae
Bibliographic record
Abstract
Abstract Because of wide applications of surface-modified zinc oxide nanoparticles (ZnO-NPs) in commercial sunscreens and their easiness of being released into water, concerns have been raised over their potential effects on aquatic organisms. This study compared physicochemical properties of silane-coated and uncoated ZnO-NPs to elucidate their toxic potencies toward three freshwater and three marine microalgae. Surfaces of ZnO-NPs (20 nm) were modified by coating with 3-aminopropyltrimethoxysilane (A-ZnO-NPs) that provides the particles with a more hydrophilic surface, or dodecyltrichlorosilane (D-ZnO-NPs) that turns the particles to hydrophobic. Uncoated ZnO-NPs formed larger aggregates and released more Zn2+ than did either of the two coated ZnO-NPs. The three nanoparticles formed larger aggregates but released less Zn2+ at pH 8 than at pH 7. Although sensitivities varied among algal species, A-ZnO-NPs and uncoated ZnO-NPs were more potent at inhibiting growth of algal cells than were D-ZnO-NPs after 96-h exposure to ZnO, uncoated ZnO-NPs, each of the coated ZnO-NPs or ZnSO4 at 10 concentrations ranging from 0.1 to 100 mg/L. The marine diatom Thalassiosira pseudonana exposed to ZnO-NPs, A-ZnO-NPs or D-ZnO-NPs resulted in differential expressions of genes, suggesting that each of the coatings resulted in ZnO-NPs acting through different mechanisms of toxic action.
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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".