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 ( TiO2 ). 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 TiO2 . 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.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".