Blends of poly(vinyl chloride) (PVC)/natural rubber‐<i>g</i>‐(styrene‐<i>co</i>‐methyl methacrylate) for improved impact resistance of PVC
Bibliographic record
Abstract
Abstract To improve the mechanical properties of poly(vinyl chloride) (PVC), the possibility of combining PVC with elastomers was considered. Modification of natural rubber (NR) by graft copolymerization with methyl methacrylate (MMA) and styrene (St) was carried out by emulsion polymerization by using redox initiator to provide an impact modifier for PVC. The impact resistance, dynamic mechanical analysis (DMA), and scanning electron microscopy (SEM) of St and MMA grafted NR [NR‐g‐(St‐co‐MMA)]/PVC (graft copolymer product contents of 5, 10, and 15%) blends were investigated as a function of the amount of graft copolymer product. It was found that the impact strength of blends was increased with an increase of the graft copolymer product content. DMA studies showed that NR‐g‐(St‐co‐MMA) has partial compatibility with PVC. SEM confirmed a shift from brittle failure to ductility with an increase graft copolymer content in the blends. The mechanical properties showed that NR‐g‐(St‐co‐MMA) interacts well with PVC and can also be used as an impact modifier for PVC. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 93: 1666–1672, 2004
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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".