Morphology and properties of polymer/organoclay nanocomposites based on poly(ethylene terephthalate) and sulfopolyester blends
Bibliographic record
Abstract
Abstract Poly(ethylene terephthalate) (PET) nanocomposite films containing two different organoclays, Cloisite 30B® (C30B) and Nanomer I.28E® (N28E), were prepared by melt blending. In order to increase the gallery spacing of the clay particles, a sulfopolyester (PET ionomer or PETi) was added to the nanocomposites via a master‐batch approach. The morphological, thermal and gas barrier characteristics of the nanocomposite films were studied using several characterization techniques such as scanning electron microscopy, transmission electron microscopy, X‐ray diffraction, differential scanning calorimetry, dynamic mechanical analysis, rheometry and oxygen permeability. PET and PETi were found to form immiscible polymer blends and the nanoparticles were preferentially located in the PETi dispersed phase. A better dispersion of clay was obtained for nanocomposites containing N28E with PETi. On the contrary, for nanocomposites containing C30B and PETi, the number of tactoids increased and the clay distribution and dispersion became worse than for C30B alone. Overall, the best properties were obtained for the PET/C30B nanocomposite without PETi. Higher crystallinity was found for all nanocomposite films in comparison to that of neat PET. © 2012 Society of Chemical Industry
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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".