Cell Nucleation and Growth Study of PP Foaming with CO<sub>2</sub> in a Batch-Simulation System
Bibliographic record
Abstract
TPO is being used to make automotive parts for its number of advantages: i) low temperature flexibility and ductility, ii) excellent impact/stiffness/flow balance, iii) excellent weatherability, and iv) free-flowing pellet form for easy processing, storage, and handling. However, by foaming TPO, due to its higher rigidity-to-weigh ratio, it would offer additional advantages over the solid counterparts in terms of reduced weight, reduced material cost, and decreased fuel usage without compromising their performance. Since a major component in TPO is polypropylene (PP), understanding PP foaming behaviours is an important step towards understanding TPO foaming. For foam materials, cell density and cell size are two significant parameters that affect their material properties. In this research, we observed the cell nucleation and initial growth behaviours of PP foams blown with CO2 under various experimental conditions in a batch foaming simulation system. We found that the nucleating agent content, initial temperature, pressure (i.e., gas content), and pressure drop rate during foaming have a significant effect on cell density and cell growth. Based on the experimental results, optimum foaming conditions and effective processing strategies for PP-CO2 system are suggested.
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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) | 0.001 | 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".