Effects of Titanium Dioxide Nanoparticles on Photosynthetic and Antioxidative Processes of Scenedesmus obliquus
Bibliographic record
Abstract
The effects of the photocatalytic toxicity of titanium dioxide nanoparticle (nano-TiO2) on phytoplankton are well understood. However, as UV light intensity decreases sharply with the depth of the water column, the effects of nano-TiO2 itself on deeper water phytoplankton, such as green algae, need further research. In this research, we investigated the effects of three sizes of TiO2 (10, 50 and 200 nm) on the photosynthetic and antioxidative processes of Scenedesmus obliquus in the absence of UV light. We found that 50 nm and 10 nm TiO2 (10 mg/L) inhibited growth rates and the maximal photosystem II quantum yield compared to the control in Scenedesmus obliquus. The minimal and maximal fluorescence yields, and the contents of reactive oxygen species and lipid peroxidation, increased, indicating that photosynthetic energy/electrons transferred to oxygen and induced oxidative stress in nano-TiO2-treated samples. In addition, we found that aggregations of algae and 10 nm TiO2 were present, which could induce cell membrane disruption, and vacuoles were induced to cope with nano-TiO2 stress in Scenedesmus obliquus. These results enhance our understanding of the effects of nano-TiO2 on the photosynthetic and antioxidative processes of green algae, and provide basic information for evaluating the ecotoxicity of nano-TiO2 in freshwater ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".