Current Issues and Challenges in Polypropylene Foaming: A Review
Bibliographic record
Abstract
Thermoplastic foams have several advantages in comparison with unfoamed polymers such as lightweight, high strength to weight ratio, excellent insulation property, high thermal stability, high impact strength and toughness, as well as high fatigue life. These outstanding properties lead cellular plastics to various industrial applications in packaging, automotive parts, absorbents, and sporting equipment. Nowadays, polypropylene (PP), because of its outstanding characteristics such as low material cost, high service temperature, high melting point, high tensile modulus, low density, and excellent chemical resistance, is a major resin in the foaming industry. However, foaming of conventional PP is limited by its low melt strength leading to poor cell morphology, cell rupture/coalescence and limited density reduction. To improve PP melt strength, several strategies including particle addition as nucleating agent, introduction of long chain branching, blending with high melt strength polymers and crosslinking have been proposed. In this review, these issues are discussed and analyzed in terms of mechanical, thermal, and rheological characterizations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".