Solvent resistance and mechanical properties in thermoplastic elastomer blends prepared by dynamic vulcanization
Bibliographic record
Abstract
Abstract Dynamic vulcanization was used to prepare thermoplastic elastomer blends of nylon (polyamide), polypropylene (PP) and polybutylene terephthalate thermoplastics with chlorobutyl (CIIR) and nitrile (NBR) rubbers. Mechanical properties of the blends were correlated against composition. Although hardness and tensile strength increase with increasing thermoplastic content for all blends, elongation at break values initially decrease and then increase in the range of 20–40% thermoplastic. For various blend compositions, the swelling behavior was evaluated with solvents that are able to dissolve the uncured rubber portion but not the thermoplastic component of the mixtures. All five systems showed swelling index values that were substantially less than the calculated “theoretical” values of swelling index. This was attributed to a caging effect of the thermoplastic component on the rubber phase, which restricts access of solvent and swelling of the rubber phase. In turn, this affects the solvent resistance of the blend. Some of the blends were evaluated by differential scanning calorimetry to assess the compatibility of the components in the blend. scanning electron microscopy was also used to determine the degree of compatibility of the two phases generated in the mixing process. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".