Characterization of Aerosols of Titanium Dioxide Nanoparticles Following Three Generation Methods Using an Optimized Aerosolization System Designed for Experimental Inhalation Studies
Bibliographic record
Abstract
Nanoparticles (NPs) can be released in the air in work settings, but various factors influence the exposure of workers. Controlled inhalation experiments can thus be conducted in an attempt to reproduce real-life exposure conditions and assess inhalation toxicology. Methods exist to generate aerosols, but it remains difficult to obtain nano-sized and stable aerosols suitable for inhalation experiments. The goal of this work was to characterize aerosols of titanium dioxide (TiO₂) NPs, generated using a novel inhalation system equipped with three types of generators-a wet collision jet nebulizer, a dry dust jet and an electrospray aerosolizer-with the aim of producing stable aerosols with a nano-diameter average (<100 nm) and monodispersed distribution for future rodent exposures and toxicological studies. Results showed the ability of the three generation systems to provide good and stable dispersions of NPs, applicable for acute (continuous up to 8 h) and repeated (21-day) exposures. In all cases, the generated aerosols were composed mainly of small aggregates/agglomerates (average diameter <100 nm) with the electrospray producing the finest (average diameter of 70-75 mm) and least concentrated aerosols (between 0.150 and 2.5 mg/m³). The dust jet was able to produce concentrations varying from 1.5 to 150 mg/m³, and hence, the most highly concentrated aerosols. The nebulizer collision jet aerosolizer was the most versatile generator, producing both low (0.5 mg/m³) and relatively high concentrations (30 mg/m³). The three optimized generators appeared suited for possible toxicological studies of inhaled NPs.
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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".