Interaction Effect of EDTA, Salinity, and Oxide Nanoparticles on Alga Chlamydomonas reinhardtii and Chlamydomonas euryale
Bibliographic record
Abstract
The interaction effects of organic ligand ethylene diamine tetra-acetic acid (EDTA) and oxide nanoparticles (magnetite Fe3O4-NPs and copper CuO-NPs) were investigated during a 72 h period on two green algal species—Chlamydomonas reinhardtii under freshwater conditions and Chlamydomonas euryale under saltwater conditions. Fe3O4-NPs had larger agglomerates and very low solubility. CuO-NPs, having smaller agglomerates and higher solubility, were more toxic than Fe3O4-NPs in freshwater conditions for similar mass-based concentrations, especially at 72 h under 100 mg L−1. Furthermore, the effect of EDTA increased nanoparticle solubility, and the salinity caused a decrease in their solubility. Our results on C. euryale showed that the increase in salinity to 32 g L−1 caused the formation of larger nanoparticle agglomerates, leading to a decrease in the toxicity impact on algal cells. In addition, EDTA treatments induced a toxicity effect on both freshwater and saltwater Chlamydomonas species, by altering the nutrient uptake of algal cells. However, C. euryale was more resistant to EDTA toxicity than C. reinhardtii. Moreover, nanoparticle treatments caused a reduction in EDTA toxicity, especially for CuO-NPs. Therefore, the toxicity impact caused by these environmental factors should be considered in risk assessment for metallic nanoparticles.
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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".