Chemical Characteristics of Zirconium Chloride and Zirconium Oxide Nanoparticles Driving Toxicity on Lemna minor
Bibliographic record
Abstract
The increasing global production and utilization of zirconium (Zr) compounds, including zirconium chloride (ZrCl4) and zirconium oxide nanoparticles (NPs-ZrO2), raises concerns about their potential environmental impact. This study investigated the toxicity mechanisms of ZrCl4 and NPs-ZrO2 on the aquatic plant Lemna minor. The physicochemical properties of NPs-ZrO2 in the test medium were characterized, revealing concentration-dependent changes in the hydrodynamic diameter, zeta potential, and solubility over time. The analysis of Zr speciation showed the predominance of Zr(OH)4(aq) species from ZrCl4. Plants of L. minor exposed to ZrCl4 and NPs-ZrO2 exhibited differential Zr bioaccumulation, growth inhibition, oxidative stress, and antioxidant responses. ZrCl4 induced a higher toxicity than NPs-ZrO2, with bioaccumulation strongly correlating with adverse effects. The differential toxicity impact between these two Zr-compounds was also determined by the lowest observed-effect doses for growth and biochemical parameters. The scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy confirmed internalization of NPs-ZrO2 and Zr uptake in the L. minor plant. Therefore, these findings highlighted the importance of chemical speciation, environmental transformations, and biological responses in assessing the ecological impact of Zr-compounds for effective risk assessment and management strategies for protecting aquatic ecosystems.
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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".