The foamability of low-melt-strength linear polypropylene with nanoclay and coupling agent
Bibliographic record
Abstract
In this work, the feasibility of producing microcellular foam by using homopolymer linear polypropylene with an melt flow index of 18 g/10 min, a high melt flow index coupling agent (G3003, melt flow index of 380 g/min), and different nanoclay (Cloisite 20A) content was investigated. A twin-screw extruder was used to prepare the nanocomposite compounds, and an X-ray diffraction machine was used to characterize the intercalation and exfoliation of nanoclay within the matrix. A rheology test was undertaken to investigate the melt shear viscosity of the samples. A single-screw extruder was employed to produce foam by using 5% supercritical CO 2 at various die temperatures. Scanning electron microscopy was used to explore the morphology of the foamed samples, and cell density was calculated by scanning electron microscopy images. Density measurement data was used to calculate the expansion ratio of the foamed samples. Extrusion foaming produced foams with high expansion ratios of about 20 and a high cell density of about 10 8−9 cells/cm 3 . The crystallinity behavior of the foamed and unfoamed linear polypropylene and linear polypropylene nanocomposites was also investigated. A high-pressure differential scanning calorimetry was used to investigate the dependency of crystallization behavior on high pressure CO 2 .
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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) | 0.000 | 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".