Solid‐state polymerization of poly(ethylene terephthalate): Effect of organoclay concentration
Bibliographic record
Abstract
Poly(ethylene terephthalate) (PET)/Cloisite 30B (C30B) nanocomposites of different organoclay concentrations were prepared using a water‐assisted extrusion process. The reduction of the molecular weight (Mw) of the PET matrix, caused by hydrolysis during water‐assisted extrusion, was compensated by subsequent solid‐state polymerization (SSP). Viscometry, titration, rheological, and dynamic scanning calorimetry measurements were used to analyze the samples from SSP. The weight‐average molecular weight (Mw) of PET increased significantly through SSP. PET nanocomposites exhibited solid‐like rheological behavior, and the complex viscosity at high frequencies was scaled with the Mw of PET. The Maron–Pierce model was used to evaluate the Mw of PET in the nanocomposites before and after SSP. It was found that the extent and the rate of the SSP reaction in nanocomposites were lower than those for the neat PETs, due to the barrier effect of clay platelets. Consequently, the SSP rate of PET increased with decreasing particle size for the neat PET and PET nanocomposites. The effect of the Mw of PET on the crystallization temperature, crystallinity, and the half‐time, t½, of nonisothermal crystallization was also investigated. With increasing Mw of PET, t½ increased, whereas Tc and Xc decreased. POLYM. ENG. SCI., 54:2925–2937, 2014. © 2014 Society of Plastics Engineers
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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) | 0.001 | 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".