Morphology and thermal properties of poly(ethylene terephthalate)‐modified kaolin nanocomposites
Bibliographic record
Abstract
Kaolin particles were dispersed in a PET (poly(ethylene terephthalate)) matrix using a displacement procedure in which kaolin was initially treated with potassium acetate and, subsequently, melt‐blended with PEO and PET. The disappearance of characteristic peaks in XRD patterns of chemically treated particles revealed that the crystalline form and layered structure of kaolin particles were mainly destroyed. Scanning electron microscopy and transmission electron microscopy showed that the thickness of dispersed particles were generally in the range of 10–100 nm. It was also observed that the dispersion level of the chemically treated kaolin was much better than that of “as received” kaolin particles. Rheological studies showed the formation of a network‐like structure in samples containing the chemically treated kaolin as a result of improved dispersion of particles. It was also observed that both the kaolin particles and modifiers could accelerate the degradation process and lower the molecular weight of the PET. The introduction of kaolin particles shifted the crystallization temperatures of PET to higher temperatures. It was observed that the incorporation of a chain extender significantly restricted the crystallization process. The Avrami–Jeziorny analysis confirmed the alteration of the crystalline structure of the filled polymer. Based on TGA thermograms the decomposition temperature of PET–kaolin composites was slightly lower than that of the neat PET. POLYM. COMPOS., 37:1443–1452, 2016. © 2014 Society of Plastics Engineers
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".