Characterization of Airborne Particles Emitted During Application of Cosmetic Talc Products
Bibliographic record
Abstract
A pilot study was undertaken to characterize the concentration, duration and particle size distribution of the talc cloud that forms in the personal breathing zone (PBZ) during application of certain talc-containing cosmetics. Multiple direct-reading instruments were employed to simultaneously monitor PM4 concentrations (particulate matter with aerodynamic diameter < 4 µm; mg/m3) at different distances from each of three subjects while they applied talc products. Results indicated that the purpose and method of applying the talc product, combined with behavioral and physical differences amongst subjects, all strongly influenced airborne talc concentrations and the duration of the cloud. Air concentrations of talc in the PBZ averaged around 1.0 mg/m3, and the duration of exposure varied from less than one minute to more than ten minutes. The real-time monitors captured the occasional formation of secondary clouds, likely caused by resuspension of talc particles from skin or other surfaces. Measurements of aerosolized baby powder, face powder, and two adult body powders indicated that the median aerodynamic diameter of the talc cloud ranged from 1.7 to 2.0 µm. These direct-reading approaches were valuable for providing detailed characterization of short duration exposures to airborne talc particles, and will be useful to support future exposure assessments of talc and other powders in consumer products.
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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".