Cell Structure Evolution and the Crystallization Behavior of Polypropylene/Clay Nanocomposites Foams Blown in Continuous Extrusion
Bibliographic record
Abstract
In this study, linear homopolypropylene/clay (HPPC) nanocomposite foams with a high expansion ratio of about 18 and a high cell density of about 1.7 × 10 8 cells/cm 3 were produced using an extrusion foaming method with CO 2 as the physical blowing agent. The result was much better than pure HPP foams with expansion ratios of 1.7−2.2 and cell densities of 10 3 −10 5 cells/cm 3 obtained even at the same foaming conditions. The nanoclays had a half-exfoliated structure in the HPP matrix, and their presence dramatically affected the viscoelastic properties of HPP melt and foaming behaviors. It was found that the introduction of a small amount of nanoclay significantly increased the cell morphology of HPP foams at low die temperatures, where the cell wall was very thin and cell distribution was uniform. With an increase in nanoclay content of up to 5 wt %, cell morphology was improved gradually at broader die temperatures. Based on the cell morphology results, a suitable foaming window for clay content and die temperature was established. The mechanisms behind these phenomena are discussed from the perspective of cell nucleation and coalescence. Microstructures were found in the cell walls of HPP and HPPC nanocomposite foams, and they tended to evolve with cell wall thickness, depending on the die temperatures. Scanning electron microscopy (SEM) observation of foams and solvent-etched foams revealed that the microstructures in the cell walls were formed by covering large-sized crystals and that the absence of microstructures was due to the presence of small-sized crystals in the cell walls. A distribution of crystal sizes was observed across the foamed samples, which was affected by the die temperature and the introduction of nanoclay. The possible reasons were elaborated by considerations of temperature gradient. DSC tests indicated that the foaming process induced a low-temperature peak ( T m1 ) and its heat of fusion (Δ H m1 ) tended to evolve with the die temperature and the introduction of nanoclay.
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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".