Interactive Toxicity of Triclosan and Nano-TiO<sub>2</sub> to Green Alga <i>Eremosphaera viridis</i> in Lake Erie: A New Perspective Based on Fourier Transform Infrared Spectromicroscopy and Synchrotron-Based X-ray Fluorescence Imaging
Bibliographic record
Abstract
in Lake Erie. Multiple physicochemical end points were conducted to perform a comprehensive analysis of the toxic effects of individual and combined pollutants. Fourier transform infrared spectromicroscopy and synchrotron-based X-ray fluorescence imaging were first documented to be applied to explore the distribution variation of macromolecules and microelements in single algal cells in interactive toxicity studies. The results were different based on different triclosan concentrations and measurement end points. Comparing with individual pollutants, the toxicity intensified in lipids, proteins, and oxidative stress at 1000 and 4000 μg/L triclosan in the presence of P25. There were increases in dry weight, chlorophyll content, lipids, and catalase content when cells were exposed to P25 and 15.625 μg/L triclosan. The toxicity alleviated when P25 interacted with 62.5 and 250 μg/L triclosan compared with triclosan-only exposure. The reasons could be attributed to the combination of adsorption, biodegradation, and photocatalysis of triclosan by algae and P25, triclosan dispersion by increased biomass, triclosan adherency on algal exudates, and triclosan adsorption site reduction on algae surface owing to P25's taking over. This work provides new insights into the interactive toxicity of nanoparticles and personal care products to freshwater photosynthetic organisms. The findings can help with risk evaluation for predicting outcomes of exposure to mixtures and with prioritizing further studies on joint toxicity.
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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".