Which Gloves Are Efficient To Protect Against Titanium Dioxide Nanoparticles In Work Conditions?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 m and 200m), 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 m) after only 60 deformations and the nTiO2 concentration reaches its maximum for 180 BD. Regarding the nTiO2 in powder, nitrile rubber gloves (100 m) and butyl rubber, the values achieved are significant but less than the solutions.
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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 it