Which Gloves Are Efficient To Protect Against Titanium Dioxide Nanoparticles In Work Conditions?
Bibliographic record
Abstract
Recent articles underline the potential health risks associated to the "nano" revolution.Titanium dioxide nanoparticles (nTiO2) are one of these engineered nanoparticles (ENP) that have been cautioned about their likely harmful effects on health.In occupational use, to handle ENP, many Health & Safety agencies have recommended the application of the precautionary principle namely the recommendation of the use of protective gloves against chemicals.However, at the best of our knowledge, no study about the penetration of ENP through protective gloves in working conditions was performed.This study was designed to evaluate the efficiency of several models of protective gloves against nTiO2.Two types of nitrile rubber gloves (100 µm and 200µm), latex and butyl rubber gloves were brought into contact with nTiO2 in water, in propylene glycol (PG) or in powder.Mechanical biaxial deformations (BD), simulating the flexing of the hand, were applied to the samples during their exposure to ENP.Depending the model of gloves and the mode of application of the NP, the results obtained by ICP-MS (Inductively Coupled Plasma -Mass Spectrometry) are different.For nTiO2 in water, the passage is highlighted for nitrile rubber gloves (100 µm) after only 60 deformations and the nTiO2 concentration reaches its maximum for 180 BD.Regarding the nTiO2 in powder, nitrile rubber gloves (100 µm) and butyl rubber, the values achieved are significant but less than the solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".