Compounding of nanocomposites by thermokinetic mixing
Bibliographic record
Abstract
Abstract Nanocomposites have been prepared by melt mixing poly(propylene) (PP) and different levels of a premixed montmorillonite‐organoclay masterbatch (PP/clay concentrate). Melt mixing was achieved using a Gelimat, a high‐speed thermokinetic mixer. The Gelimat system is designed to handle difficult compounding and dispersion applications and can achieve mixing, heating, and compounding of products within a minute. Therefore, the thermal history of the compounded polymer is short, which limits degradation. The structure and properties of the nanocomposites prepared with a Gelimat were compared to ones prepared with a twin‐screw extruder. The structure and properties of PP/clay nanocomposites were compared by TEM, X‐ray diffraction, mechanical testing, and rheological analysis. Results indicate that a better dispersion of the clay can be achieved by thermokinetic mixing when compared to extrusion, resulting in better mechanical properties. Calculations, based on simplifying assumptions, showed that the shear rates generated in a Gelimat are at least one order higher than those generally generated in an extruder. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 96: 1557–1563, 2005
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".