The effect of clay type and of clay–masterbatch product in the preparation of polypropylene/clay nanocomposites
Bibliographic record
Abstract
Abstract Clay containing polypropylene (PP) nanocomposites were prepared by direct melt mixing in a twin screw extruder using different types of organo‐modified montmorillonite (Cloisite 15 and Cloisite 20) and two masterbatch products, one based on pre‐exfoliated clays (Nanofil SE 3000) and another one based on clay–polyolefin resin (Nanomax‐PP). Maleic anhydride‐grafted polypropylene (PP‐g‐MA) was used as a coupling agent to improve the dispersability of organo‐modified clays. The effect of clay type and clay–masterbatch product on the clay exfoliation and nanocomposite properties was investigated. The effect of PP‐g‐MA concentration was also considered. Composite morphologies were characterized by X‐ray diffraction (XRD), field emission gun scanning electron microscopy (FEG‐SEM), and transmission electron microscopy (TEM). The degree of dispersion of organo‐modified clay increased with the PP‐g‐MA content. The thermal and mechanical properties were not affected by organo‐modified clay type, although the masterbatch products did have a significant influence on thermal and mechanical properties of nanocomposites. Intercalation/exfoliation was not achieved in the Nanofil SE 3000 composite. This masterbatch product has intercalants, whose initial decomposition temperature is lower than the processing temperature (T ∼ 180°C), indicating that their stability decreased during the process. The Nanomax‐PP composite showed higher thermal and flexural properties than pure PP. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
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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".