Influences of temperature and salinity on physicochemical properties and toxicity of zinc oxide nanoparticles to the marine diatom Thalassiosira pseudonana
Bibliographic record
Abstract
Abstract Climate change is predicted to result in rising average temperature of seawater with more extreme thermal events, and frequent rainfalls in some coastal regions. It is imperative to understand how naturally mediated changes in temperature and salinity can modulate toxicity of chemical contaminants to marine life. Thus, this study investigated combined effects of temperature and salinity on toxicity of zinc oxide nanoparticles (ZnO-NPs) to the marine diatom Thalassiosira pseudonana . Because ZnO-NPs formed larger aggregations and released less zinc ions (Zn 2+ ) at greater temperature and salinity, toxicity of ZnO-NPs to T . pseudonana was less at 25 °C than at 10 °C and less at 32 than 12 PSU. However, toxicity of ZnO-NPs was significantly greater at 30 °C, since T . pseudonana was near its upper thermal limit. Three test compounds, ZnO, ZnO-NPs and ZnSO 4 , displayed different toxic potencies and resulted in different profiles of expression of genes in T . pseudonana . This indicated that ZnO-NPs caused toxicity via different pathways compared to ZnSO 4 . Mechanisms of toxic action of the three compounds were also dependent on temperature and salinity. These results provide insights into molecular mechanisms underlying the responses of the diatom to ZnO-NPs and Zn 2+ under various regimes of temperature and salinity.
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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".