Bibliographic record
Abstract
Self-reinforced polymeric composite materials are materials where both the reinforcement and matrix are in the same family of thermoplastic materials and therefore have an advantage in recycling after their end-of-life. Due to the relative homogeneity, the system allows to achieve excellent interfaces between the matrix and reinforcement, which is often not possible in conventional composites. While various studies have been conducted with different composite manufacturing methods, in situ generation of a fibrillar minor phase morphology with the fibrillation process has potential to improve the self-reinforced polymeric composite technology. The extremely large interfaces from the fine physical network structure of the minor phase in the matrix would be able to maximize the effects generated from perfect interfaces guaranteed from the homogenous self-reinforced composite system. Furthermore, a large temperature processing window of self-reinforced polymeric composites can expand the versatility of the processing route.This thesis shows that the fibrillation process is highly beneficial to improve mechanical and rheological properties of the cyclic olefin copolymer (COC), polypropylene (PP) and polyethylene terephthalate (PET) self-reinforced polymeric system. In addition, fibrils in the matrix also enhance the foam properties of linear polymer matrix while maintaining high recyclability of the self-reinforced polymeric composite. The improvement of foam properties further leads to decrease thermal conductivity.
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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 | 0.001 |
| 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".