Effect of water‐assisted extrusion and solid‐state polymerization on the microstructure of PET/Clay nanocomposites
Bibliographic record
Abstract
An melt‐mixing process has been used to prepare Poly(ethylene terephthalate) (PET)/clay nanocomposites with high degree of clay delamination. In this method, steam was fed into a twin‐screw extruder (TSE) to reduce the PET molecular weight and to facilitate their diffusion into the gallery spacing of organoclays. Subsequently, the molecular weight (MW) reduction of the PET matrix due to hydrolysis by water was compensated by solid‐state polymerization (SSP). The effect of the thermodynamic compatibility of PET and organoclays on the exfoliated microstructure of the nanocomposites was also examined by using three different nanoclays. The dispersion of Cloisite 30B (C30B) in PET was found to be better than that of Nanomer I.28E (I28E) and Cloisite Na+. The effect of feeding rate and consequently residence time on the properties of PET nanocomposites was also investigated. The results reveal more delamination of organoclay platelets in PET‐C30B nanocomposites processed at low feeding rate compared to those processed at high feeding rate. Enhanced mechanical and barrier properties were observed in PET nanocomposites after SSP compared to the nanocomposites prepared by conventional melt‐mixing. POLYM. ENG. SCI., 54:1723–1736, 2014. © 2013 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) | 0.000 | 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".