Swelling behavior of polymeric membranes to metalworking fluids
Bibliographic record
Abstract
ABSTRACT In some working places, such as metal manufacturing or automotive services, mechanical hazards commonly occur along with chemical hazards, particularly metalworking fluids (MWFs). The presence of these chemicals could modify the properties of gloves made from polymeric materials and thus reduce their protective properties against chemical contamination (solvent, MWFs) and mechanical risks (puncture and cutting). This work focused on determining the swelling characteristics and the resistance of six polymeric membranes which were exposed to seven industrial MWFs. We found that the swelling tests can be used to classify the potential of coating polymers in descending order of their resistance to MWFs: nitrile, polyurethane > poly(vinyl chloride), neoprene > butyl, latex. The analysis by multiple linear regression showed, for the first time, that the density or the viscosity‐gravity constant of the fluid and Hansen's solubility parameters of the polymers have a significant impact on the swelling of polymer. For the first time, two new multiple regression models have been proposed, to predict the swelling phenomena of polymers under various MWFs with an accuracy of ≈80%. The effect of temperature on mechanical properties and morphology of material was also examined. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 45717.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".