Thermal Stabilization of Recycled PET Through Chain Extension and Blending with PBT
Bibliographic record
Abstract
Abstract This study investigates the effect of using a multifunctional epoxide chain extender (Joncryl® ADR 4468) on the thermal stabilization and rheological properties of recycled polyethylene terephthalate (R-PET) and its blends with polybutylene terephthalate (PBT). The thermal stability of the melt blended samples was analyzed through small amplitude oscillatory shear (SAOS) rheological experiments. The structure of the samples was evaluated using a Fourier transform infrared (FTIR) spectrometer. While the dynamic rheological properties of R-PET were improved with the addition of Joncryl and by blending with PBT, during the SAOS rheological experiments, the complex viscosity of R-PET further increased due to the concurrent polycondensation of R-PET and the resumption of Joncryl reaction with R-PET molecules. These reactions during the rheological experiments were further expedited with increasing the testing temperature. On the other hand, in R-PET/PBT blends, the reactivity of Joncryl was more noticeable in blends with higher R-PET contents due to the higher available internal reactive sites of much shorter R-PET molecules. It was observed that the addition of only 0.2 wt.% Joncryl to the blends of R-PET/PBT (75w/25w) dramatically improves the thermal stability and dynamic rheological properties of R-PET and most likely its processability.
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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) | 0.002 | 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".