Mechanical properties and foaming behavior of polypropylene/elastomer/recycled carbon fiber composites
Bibliographic record
Abstract
Abstract Research on lightweight products for transportation industry, to address fuel efficiency and lower carbon footprint, has attracted great attention due to public environmental awareness. Polymer composites containing carbon fiber are boosted by automotive original equipment manufacturers (OEMs) as a replacement for steel. However, their high cost has generated interest to find cost‐effective alternatives. In this report, a recycled carbon fiber and an elastomer are introduced to polypropylene impact copolymers to modify matrix properties in terms of strength and toughness. Fractography analysis is carried out to elucidate the failure mechanisms governing the mechanical performance of the compatibilized carbon fiber reinforced thermoplastic polyolefin (TPO) composites. The results suggest that for the TPO composites, toughened by an ethylene‐1‐octene copolymer, the complex interplay of intrinsic and extrinsic toughnesses in impact resistance is predominantly governed by the presence of the fibers. Incorporating 20 wt% recycled carbon fibers into the matrix resulted in an improvement of the heat distortion temperature (HDT) by 100 °C, and ~ 3.5 and ~ 11.5 times enhancements in tensile strength and stiffness, respectively. Moreover, microcellular foams with expansion ratios higher than 11 and 5 were achieved for the neat samples and reinforced composites, respectively, confirming the foamability of composites with such a high carbon fiber content.
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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".