Effects of Size and Morphology of TiO2 and SiO2 Airborne Nanoparticles on their Filtration through Chemical Protective Clothing
Bibliographic record
Abstract
Titanium dioxide (nTiO2) and silicon dioxide (nSiO2) nanoparticles are employed in numerous products that are used daily and also in building materials such as concrete, plaster or paints.Whether during manufacturing or released by sanding or polishing, nTiO2 and nSiO2 can become airborne, exposing workers to possible health risks.Although the penetration of airborne nanoparticles through the filtering media used in respiratory protective equipment has been studied for several years with different types of airborne nanoparticles, the study of the efficiency of chemical protective clothing (CPC) materials is much more recent.Furthermore, these studies were generally conducted with polydisperse sodium chloride airborne nanoparticles, which are particles that are seldom used in the workplace.The present work focuses on the effect of the size and shape of nTiO2 and nSiO2 airborne nanoparticles on their penetration level through a sample of filtering material directly taken from a model of nonwoven CPC.The clogging effect was also evaluated and its impact on the pressure drop was determined.Results indicate that contrary to the particle size, the shape could have a significant effect on the level of penetration.Moreover, clogging due to the deposit of nanoparticles on the filtering fibers could be seen, in some cases, in terms of penetration, but did not have a significant effect on the pressure drop.
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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".