Generating Nano-Aerosols from TiO<sub>2</sub>(5 nm) Nanoparticles Showing Different Agglomeration States. Application to Toxicological Studies
Bibliographic record
Abstract
Agglomeration of nanoparticles (NP) is a key factor in the generation of aerosols from nano-powders and may represent an important parameter to consider in toxicological studies. For this reason, the characterization of NP aerosols (e.g., concentration, size, and structure of agglomerates) is a critical step in the determination of the relationship between exposure and effects. The aim of this study was to generate and characterize aerosols composed of TiO₂ (5 nm) NP showing different agglomeration states. Two concentrations were tested: 2 and 7 mg/m³. Stable mass concentrations over 6 hr were successfully generated by a wet method using Collison and Delavan nebulizers that resulted in aerosols composed of smaller agglomerates (<100 nm), while aerosols composed of larger agglomerates (>100 nm) were obtained by dry generation techniques using either a Palas dust feeder or a Fluidized Bed. Particle size distributions in the aerosols were determined by an electrical low pressure impactor. Median number aerodynamic diameters corresponding to the aerosol with smaller and larger agglomerates were 30 and 185 nm, respectively, for the 2 mg/m³ concentration, and 31 and 194 nm for the 7 mg/m³ experiment. Image analysis by transmission electron microscopy showed the presence of compact or agglomerates with void spaces in the different nano-aerosols. These characterized nano-aerosols will be used in further experiments to study the influence of agglomerate size on NP toxicity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".