Evidence that nano-TiO<sub>2</sub> induces acute cytotoxicity to the agronomically beneficial nitrogen-fixing bacteria <i>Sinorhizobium meliloti</i>
Bibliographic record
Abstract
When nano-sized titanium dioxide (nano-TiO2) absorbs ultra-violet (UV-A) radiation, it produces reactive oxygen species that can be toxic to bacteria. We used the agronomically beneficial nitrogen-fixing bacterium Sinorhizobium meliloti strain 1021 as a model microorganism to detect nano-TiO2 toxicity. Sinorhizobium meliloti was exposed to aqueous dispersions of micrometer-sized TiO2 (micron-TiO2, 44 μm) or nanometer-sized TiO2 (nano-TiO2, 21 nm) at nominal concentrations of 0, 100, 300, 600, 900, and 1800 mg TiO2/L. There were fewer viable S. meliloti cells after exposure to nano-TiO2 under dark and UV-A light conditions. Nano-TiO2 was more toxic to S. meliloti with UV-A irradiation (100% mortality at 100 mg TiO2/L) than under dark conditions (100% mortality at 900 mg TiO2/L). Micron-TiO2 concentrations less than 300 mg TiO2/L had no effect on S. meliloti viability under dark or UV-A light conditions. Exposure to 600 mg/L or more of micron-TiO2 under UV-A light could also photo-kill S. meliloti cells (100% mortality). Further studies are needed to ascertain whether nano-TiO2 interferes with the growth of N2-fixing microorganisms in realistic agricultural environments.
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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".