Effects of Tungsten and Titanium Oxide Nanoparticles on the Diazotrophic Growth and Metals Acquisition by <i>Azotobacter vinelandii</i> under Molybdenum Limiting Condition
Bibliographic record
Abstract
The acquisition of essential metals, such as the metal cofactors (molybdenum (Mo) and iron (Fe)) of the nitrogenase, the enzyme responsible for the reduction of dinitrogen (N(2)) to ammonium, is critical to N(2) fixing bacteria in soil. The release of metal nanoparticles (MNPs) to the environment could be detrimental to N(2) fixing bacteria by introducing a new source of toxic metals and by interfering with the acquisition of essential metals such as Mo. Since Mo has been reported to limit nonsymbiotic N(2) fixation in many ecosystems from tropical to cold temperate, this question is particularly acute in the context of Mo limitation. Using a combination of microbiology and analytical chemistry techniques, we have evaluated the effect of titanium (Ti) and tungsten (W) oxide nanoparticles on the diazotrophic growth and metals acquisition in pure culture of the ubiquitous N(2) fixing bacterium Azotobacter vinelandii under Mo replete and Mo limiting conditions. We report that under our conditions (≤10 mg·L(-1)) TiO(2) NPs have no effects on the diazotrophic growth of A. vinelandii while WO(3) NPs are highly detrimental to the growth especially under Mo limiting conditions. Our results show that the toxicity of WO(3) NPs to A. vinelandii is due to an interference with the catechol-metalophores assisted uptake of Mo.
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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".