Bibliographic record
Abstract
Abstract The clay-containing polymeric nanocomposites (CPNC) can be visualized as binary mixtures of strongly interacting, inorganic, plate-like molecules dispersed in a polymeric matrix. To be successful, one must ascertain the thermodynamics, which controls CPNC structure on the molecular level. In this work dispersion of organoclay (Cloisite 15A, C15A) in polyamide 6 (PA6) or in polypropylene (PP) is discussed. The PA-based CPNC's contained two components: polymer and organoclay, whereas those based on PP in addition contained a mixture of two maleated polypropylene's (PP-MA), as a compatibilizer. The melt compounding was carried out either in a single-screw extruder (SSE), or a twin-screw extruder (TSE). Both compounding lines were used with or without the extensional flow mixer (EFM). Furthermore, two versions of EFM were evaluated – one commercial, designed for polymer homogenization and blending, and the other designed for dispersing nano-particles. It was found that addition of EFM significantly improved clay dispersion. Better dispersion was found compounding the CPNC's in a SSE+EFM than in TSE with or without EFM. The best results were obtained using SSE with the new EFM having a relatively small gap between the convergent-divergent plates. C15A was fully exfoliated in PA6 matrix. The results in PP/PP-MA matrix were less spectacular, but again the highest degree of dispersion was obtained using SSE+new EFM with a small gap. Tensile, flexural and impact properties were measured and evaluated.
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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) | 0.002 | 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".