Polystyrene/polyolefin elastomer/halloysite nanotubes blend nanocomposites: Morphology‐thermal degradation kinetics relationship
Bibliographic record
Abstract
Abstract Polystyrene/polyolefin elastomer (PS/POE) (90/10 and 80/20 wt/wt) blends containing 1, 3, and 5 phr halloysite nanotubes (HNTs) in the presence and absence of a compatibilizer (polypropylene‐graft‐maleic anhydride) were prepared using the melt‐mixing technique. Scanning electron microscopic studies confirmed a matrix‐droplet morphology. Energy dispersive spectroscopy (EDS) mapping indicated that the blends containing 5 phr HNTs possessed aggregates, while no agglomeration was observed after incorporating 5 phr compatibilizer. Thermal stability and thermal degradation kinetics were investigated using thermogravimetry analysis (TGA). The results demonstrated that the PS/POE blend (90/10) containing 5 phr HNTs and compatibilizer (90/10/5/5) has the best thermal stability. Different methods such as Friedman, Flynn‐Ozawa‐Wall, and Kissinger‐Akahira‐Sunose were applied to calculate the degradation activation energy. The 90/10/5/5 nanocomposite exhibited the highest degradation activation energy, indicating that this sample is more difficult to degrade thermally than other samples. A correlation was obtained between the activation energy and the intensity of the TGA‐fourier‐transform infrared spectroscopy (TGA/FTIR) peaks of the evolved products. The Criado method was used to determine the changes in the thermal degradation mechanism of the samples.
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 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".