Fabrication of Strong Self-Reinforced Polyethylene Terephthalate Composites through the In Situ Nanofibrillation Technology
Why this work is in the frame
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Bibliographic record
Abstract
Fabrication of self-reinforced polyethylene terephthalate (PET) has been achieved through the in situ generation of PET fibrils via a spun bond process. The reinforcement fibrils created from the PET with higher Tm are made from a unique in situ processing method. As a result, the fibrils are well dispersed and distributed in the lower Tm PET matrix. The high degree of molecular similarity affords perfect interfaces between the matrix and dispersed phase, leading to excellent stress transfer from the matrix to the dispersed fibrils. While the extremely large interfaces from the nanofibrillation process can maximize the advantage of the excellent molecular similarity of the self-reinforced polymeric composites, few studies have been conducted to research nanofibrillar self-reinforced polymeric composite systems. Hence, as a proof of concept, this work provides new insight into an approach for developing a self-reinforced polymeric system with a nanofibrillation process. This process increases the tensile strength of PET composites by up to 15% compared to composites made by a simple blending process and 47% higher than neat PET. Furthermore, extensional viscosity measurements show a strain-hardening behavior in the fibrillated PET composites not observed in the neat PET and showed minimal behavior in un-fibrillated PET composites. The foam process results reveal that the presence of PET fibrils in PET improves the expansion ratio as well as the cell density of the PET composites. Specifically, compared to the PET composite foams without the fibrillation process, fibrillated PET composite foams showed up to 3.7 times higher expansion ratios and one to two orders of magnitude higher cell densities. In thermal conductivity measurements, fibrillated PET composite foams achieved thermal conductivity of as low as 0.032 W/mK.
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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 | 0.001 |
| 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 it