Nanosilica Addition Dramatically Improves the Cell Morphology and Expansion Ratio of Polypropylene Heterophasic Copolymer Foams Blown in Continuous Extrusion
Bibliographic record
Abstract
Currently, the preparation of polypropylene (PP) foam with a well-defined cell structure and a high expansion ratio is receiving increased attention. However, the present technical problems such as poor cell nucleation ability and weak melt strength of polymer resin, hinder the broader use of linear PP in foam production. In this study, a PP heterophasic copolymer with a linear structure was selected together with nanosilica to challenge the fabrication of PP foam with uniform cell structure, high cell density, and a high expansion ratio using CO 2 as a physical blowing agent. Scanning electron microscopy (SEM) observation indicated that silica particles tended to aggregate in the PP matrix, but the multisilica aggregates with sizes from 80 to 350 nm were well dispersed in PP because of the addition of a coupling agent (CA). PP foam exhibited poor cell morphology and low cell densities of ca. 10 4–5 cells/cm 3 at different die temperatures. An introduction of a small amount of nanosilica, 0.5 wt % and 1 wt %, dramatically improved the foaming behavior of PP, where the cell structure distribution of the resultant foams was uniform, and the cell density and foam expansion were high (i.e., 10 8–9 cells/cm 3 and 16.9–19.5, respectively). Furthermore, the presence of nanosilica clearly broadened the foaming window of PP. By further increasing silica content, however, the foaming behavior of PP/silica nanocomposites became poor, especially at slightly higher die temperatures (i.e., above 140 °C), even though a high silica loading increased the number of heterogeneous nucleation sites. The effect of foaming on the dispersion of nanosilica in the PP matrix was also investigated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".