A study of dynamic vulcanization for polyamide‐12 and chlorobutyl rubber
Bibliographic record
Abstract
Abstract Polyamide‐12 and chlorobutyl rubber were blended by dynamic vulcanization in a high shear environment using curing systems based on sulfur, dithiocarbamate/ZnO, and 4,4‐methylenebiscyclohexylamine/MgO. As expected, all blends with curing agents show increased tensile strength and elongation at break in comparison to blends without curing agents. Maximum mechanical properties are obtained at relatively low levels of curing agent in all systems. Hexane extraction of the mixtures and measurement of percentage of insolubles along with the swelling index of the rubber phase confirm that a high level of cure is achieved at low levels of curing agent. Although the curatives are designed for the rubber phase, differential scanning calorimetry results indicate that both phases are affected during the dynamic vulcanization process, with polyamide‐12 showing a reduced melting temperature that is indicative of molecular weight reduction, structure changes, or reaction with the rubber phase. Scanning electron microscopy results indicate that phase size is reduced with increased blending time and level of curing agent. Rheological studies indicate that blends containing curing agents exhibit non‐Newtonian behavior to a greater extent than polyamide or nonvulcanized polyamide/chlorobutyl rubber blends. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 90: 871–880, 2003
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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 | 0.000 |
| 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 | 0.000 |
| 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".