Composition and mechanical properties of polypropylene montmorillonite nanocomposites
Bibliographic record
Abstract
Montmorillonite layered clay has been treated with poly (ethylene) glycol (PEG) based surfactants with long alkyl chains. PEG600 possesses both intercalating property between clay layers and compatibilizing property with polypropylene. The treated clay was used to prepare polypropylene clay nanocomposites. Maleic anhydride grafted polypropylene (MA-PP) was added to the mixture of the treated clay and the polypropylene to prevent aggregation. We successfully prepared polypropylene nanocomposite, using solution blending technique. The effect of treated clay on the thermal, structural, and dynamic mechanical properties of polypropylene were analysed with thermogravimetry (TGA), wide angle x-ray scattering (WAXS), transmission electron microscopy, and dynamic mechanical analysis. X-ray diffraction showed that the clay is well dispersed and preferentially embedded in the polymer matrix. The thermal stability enhancement of PP after adding treated clay was determined with thermogravimetry (TGA). The exfoliation degree of the clay decreased with increasing organoclay content. The transmission electron microscopy studies showed a better dispersion of clay in the PP matrix. Furthermore, the dynamic mechanical analysis studies showed an increase in the storage modulus and glass transition temperature for PP nanocomposite with respect to pure polypropylene.
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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 | 0.001 |
| 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".