Investigation of Toughening Micro-Mechanisms in Polypropylene/Ethylene-Propylene-Diene Rubber Blends at Crack and Notch Tips
Bibliographic record
Abstract
Abstract Polypropylene (PP) has the highest growth rate among commodity thermoplastics and is widely used in many applications including packaging, auto, and pipe industries. The relatively low impact strength of this polymer has led to the production of rubber-modified polypropylene with higher impact strength. To promote the applications of these blends, it is necessary to establish the relationship between the mechanical performance and fracture micro-mechanism(s). Fracture behavior is different depending on the application where either crack or notch might be present. In this study, a systematic approach is taken with the aim of understanding the toughening micro-mechanisms of polypropylene/ethylene-propylene-diene monomer (PP/EPDM) blends at both crack and notch tip using different microscopy techniques. Rubber-modified blends were prepared using a twin screw extruder. The samples were exposed to different mechanical, physical, and microscopic examinations. X-ray diffractometer (XRD) and differential scanning calorimetry (DSC) techniques were used to study the crystalline structure. Impact and fracture toughness (JIC) tests were conducted to evaluate toughness of blends. Morphology and fracture behavior of the blends were investigated via transmission optical microscopy (TOM) and scanning electron microscopy (SEM). Results indicate that both impact strength and fracture toughness (JIC) increase with increasing EPDM content. The two parameters, however, do not follow the same trend. The microscopic evaluations reveal that massive crazing coupled with particle cavitation is the dominant toughening mechanism in PP/EPDM blends under quasi-static and impact loading. Morphological features of the damage zone are different in these two loading conditions.
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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".