An improved experimental methodology to evaluate the effectiveness of protective gloves against nanoparticles in suspension
Bibliographic record
Abstract
Recent studies underline the potential health risks associated to the "nano" revolution, particularly for the workers who handle engineered nanoparticles (ENPs) that can be found in the formulation of several commercial products. Although many Health & Safety agencies recommend the use of protective gloves against chemicals, few studies have investigated the effectiveness of these gloves towards nanoparticle suspensions. Moreover, the data that are available are often contradictory. This study was designed to evaluate the effectiveness of protective gloves against nanoparticles in suspension. For this purpose, a new methodology was developed in order to take into account parameters encountered in the workplace such as mechanical deformations (MD) that simulate hand flexion and sweat. The effects of the precise experimental protocol on the concentrations of nanoparticles that were detected in the sampling suspension were assessed. Several samples of nitrile rubber gloves (73 µm thick), taken from different boxes, were brought into contact with gold nanoparticles (5 nm) in water. During their exposure to ENPs, the glove samples submitted systematic mechanical deformations and were placed in contact with a physiological solution simulating human sweat. Under these conditions, results obtained by inductively coupled plasma mass spectrometry (ICPMS) showed that the 5 nm gold nanoparticles passed through the protective gloves. This result was acquired, in spite of the observation of significant losses during the sampling phase that will be important for future experiments evaluating the effectiveness of these materials.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".