Enhancing the dynamic mechanical properties of thermoplastic elastomers: A study on polypropylene /natural rubber blends
Bibliographic record
Abstract
The aim of this study was to investigate the modifications of the mechanical properties of polypropylene (PP) by incorporating elastomers, while considering the impact on its stiffness. Specifically, the research focused on determining the optimal loading of elastomer to achieve desirable properties and exploring the influence of these processes on the morphology and mechanical behavior of the prepared blends. Thermoplastic elastomers (TPEs) consisting of polypropylene and natural rubber (PP/NR) were prepared using a melt-mixing process, and the mechanical properties of the blends were evaluated. The stress-strain properties of the blends revealed a successful modification of PP, transforming it from a stiff and strong thermoplastic into a stiff and tough thermoplastic elastomer when 10% NR was included in the PP matrix. As the loading of NR increased, a reduction in tensile strength (TS) and modulus (E) of the blends was observed, while elongation at break (EB) increased. The flexural strength of unmodified PP was 45.9 MPa, which decreased with increasing NR loading. Similarly, the impact strength of unmodified PP was 25.8 KJ/m2, whereas the values for 10%, 20%, 30%, and 40% NR inclusion were 30.8, 24.3, 20.6, and 15.2 KJ/m2, respectively. The melt flow index (MFI) of unmodified PP was 14.1 g/10 min, while the values for 10%, 20%, 30%, and 40% NR inclusion were 19.4, 15.7, 11.6, and 10.2 g/10 min, respectively. The best combination of mechanical properties was observed at 10% NR inclusion in the PP matrix. The micrograph of the blends, as observed from SEM micrographs, supported the modification of PP, resulting in the production of TPE with observable adhesion sites, indicating good compatibility between the components. In a nutshell, a significant 47% increase in impact strength was achieved through the modification process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".