Towards understanding the mechanisms and the kinetics of nanoparticle penetration through protective gloves
Bibliographic record
Abstract
Parallel to the increased use of engineered nanoparticles (ENP) in the formulation of commercial products or in medicine, numerous health & safety agencies have recommended the application of the precautionary principle to handle ENP; namely, the recommendation to use protective gloves against chemicals. However, recent studies reveal the penetration of titanium dioxide nanoparticles through nitrile rubber protective gloves in conditions simulating occupational use. This project is designed to understand the links between the penetration of gold nanoparticles (nAu) through nitrile rubber protective gloves and the mechanical and physical behaviour of the elastomer material subjected to conditions simulating occupational use (i.e., mechanical deformations (MD) and sweat). Preliminary analyses show that nAu suspensions penetrate selected glove materials after exposure to prolonged (3 hours) dynamic deformations. Significant morphological changes are observed on the outer surface of the glove sample; namely, the number and the surface of the micropores on the surface increase. Moreover, nitrile rubber protective gloves are also shown to be sensitive to the action of nAu suspension and to the action of the saline solution used to simulate sweat (swelling).
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".