SBS and SEBS block copolymers as impact modifiers for polypropylene compounds
Bibliographic record
Abstract
Abstract Blends of polypropylene (PP) and thermoplastic elastomers (TPE), namely SBS (styrene‐butadiene‐styrene) and SEBS (styrene‐ethylene/1‐butene‐styrene) block copolymers, were prepared to evaluate the effectiveness of the TPE type as an impact modifier for PP and influence of the concentration of elastomer on the polymer properties. Polypropylene homopolymer (PP‐H) and ethylene–propylene random copolymer (PP‐R) were evaluated as the PP matrix. Results showed that TPEs had a nucleating effect that caused the PP crystallization temperature to increase, with SBS being more effective than SEBS. Microstructure characterization tests showed that in most cases PP/SEBS blends showed the smallest rubber droplets regardless of the matrix used. It was seen that SEBS is a more effective toughening agent for PP than SBS. At 0°C the Izod impact strength of the PP‐H/SEBS 30% b/w blend was twofold higher than the SBS strength, with the PP‐R/SEBS 30% b/w blend showing no break. A similar behavior on tensile properties and flexural modulus were observed in both PP/TPE blends. Yield stress and tensile strength decreased and elongation at break increased by expanding the dispersed elastomeric phase in the PP matrix. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 95: 254–263, 2005
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) | 0.001 | 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) | 0.002 | 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".