Effectiveness of Protective Gloves against Engineered Nanoparticles: Difficulties in Evaluation
Bibliographic record
Abstract
Gold nanoparticles (nAu) are presently of great interest in research and industrial applications, because they can be easily and quickly synthesised and are chemically stable.A growing number of workers must handle these nanoparticles even though recent studies underline the potential health risks associated with their use.The current means of protecting the skin of the hands is limited to the use of disposable protective gloves, according to the recommendations of Health and Safety agencies.Recent research conducted by the authors has shown the poor effectiveness of nitrile rubber gloves against titanium dioxide nanoparticles in water.However, the evaluation of the effectiveness of protective gloves is difficult owing to some intrinsic parameters of the gloves' material composition and the nanoparticles.This study emphasizes the difficulties encountered when assessing the effectiveness of nitrile protective gloves against gold nanoparticles.One model of nitrile rubber gloves (thickness ~ 100 µm) and two sizes of spherical gold nanoparticles (5 and 50 nm) in aqueous suspension were examined.To simulate the conditions of occupational use, glove samples in contact with nAu suspensions were subjected to repeated mechanical deformations.It was shown that depending on the size of the nanoparticles, the results obtained by Inductively Coupled Plasma -Mass Spectrometry are different.For nAu-5, passage across the glove material is observed, but not with nAu-50.Surprisingly, with a different batch of the same gloves, no penetration is measured for nAu-5.Mechanical characterization of the glove materials; namely, strain energy and swelling tests, were performed in an effort to better understand the observed behavior.These external parameters can influence the penetration of nanoparticles through protective gloves.Indeed, mechanical deformations and swelling induce a loss of the physical integrity of the glove sample.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".