Melt compounding of polypropylene‐based clay nanocomposites
Bibliographic record
Abstract
Abstract Polypropylene (PP)‐based nanocomposites containing 4 wt% maleic anhydride grafted PP (PP‐ g ‐MA) and 2 wt% Cloisite 20A (C20A) were prepared using various processing devices, viz., twin‐screw extruder (TSE), single‐screw extruder (SSE), and SSE with an extensional flow mixer (EFM). Two processing methods were employed: (I) masterbatch (MB) preparation in a TSE (with 10 wt% C20A and clay/compatibilizer ratio of 1:2), followed by dilution in TSE, SSE, or SSE + EFM, to 2 wt% clay loading; (II) single pass, i.e., directly compounding of dry‐blended PP‐g‐MA/clay in TSE, SSE, or SSE + EFM. It has been indicated that the quality of clay dispersion, both at micro‐ and nanolevel, of the nanocomposites depends very much on the operating conditions during processing, such as mixing intensity and residence time, thus affecting the mechanical performance. Besides that the degradation of the organoclay and the matrix is also very sensitive to these parameters. According to results of X‐ray diffraction, field emission gun scanning electron microscopy, transmission electron microscopy, and mechanical tests, the samples prepared with MB had better overall clay dispersion, which resulted in better mechanical properties. The processing equipment used for diluting MB had a marginal influence on clay dispersion and nanocomposite performance. POLYM. ENG. SCI., 47:1447–1458, 2007. © 2007 Society of Plastics Engineers
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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) | 0.001 | 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".