Swelling of Elastomers in Solutions of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mtext>TiO</mml:mtext></mml:mrow><mml:mtext>2</mml:mtext></mml:msub></mml:mrow></mml:math> Nanoparticles
Bibliographic record
Abstract
Elastomers used in protective gloves can be sensitive to the action of solvents used to disperse commercial solutions of nanoparticles. These effects may include the swelling of the polymer, leading to a modification of its mechanical and chemical properties. Modifications to the properties of the polymer will impact the protection provided by the protective gloves. The goal of this work was therefore to study the swelling of several elastomers when exposed to commercial solutions of nanoparticles. The study involved four elastomers and three commercial solutions of colloidal titanium dioxide (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msub><mml:mrow><mml:mtext>TiO</mml:mtext></mml:mrow><mml:mtext>2</mml:mtext></mml:msub></mml:mrow></mml:math>). Swelling was assessed by measurements of mass gain and length change. Tests were also performed with technical and ultrapure solvents corresponding to the liquid carriers. The solutions had a significant effect on the swelling of nitrile rubber, latex, and neoprene. A large mass gain was recorded for short immersion times, indicating a possible penetration of the nanoparticle liquid carrier into these elastomers. Length change measurements revealed a swelling anisotropy effect with nitrile rubber and latex in the solutions of colloidal <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:msub><mml:mrow><mml:mtext>TiO</mml:mtext></mml:mrow><mml:mtext>2</mml:mtext></mml:msub></mml:mrow></mml:math>. No effect was measured with butyl rubber. The results show that great care must be taken when selecting protective gloves for the handling of nanoparticle dispersions.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.118 | 0.002 |
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; both teacher heads agree on what is shown here.
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".