Improved Cell Morphology and Surface Roughness in High-Temperature Foam Injection Molding Using a Long-Chain Branched Polypropylene
Bibliographic record
Abstract
Long-chain branched polypropylene (LCB PP) has been used extensively to improve cell morphologies in foaming applications. However, most research focuses on low melt flow rate (MFR) resins, whereas foam production methods such as mold-opening foam injection molding (MO-FIM) require high-MFR resins to improve processability. A systematic study was conducted comparing a conventional linear PP, a broad molecular weight distribution (BMWD) linear PP, and a newly developed BMWD LCB PP for use in MO-FIM. The effects of foaming temperature and molecular architecture on cell morphology, surface roughness, and mechanical properties were studied by utilizing two chemical blowing agents (CBAs) with different activation temperatures and varying packing times. At the highest foaming temperatures, BMWD LCB PP foams exhibited 887% higher cell density, 46% smaller cell sizes, and more uniform cell structures than BWMD linear PP. Linear PP was found to have a surface roughness 23% higher on average than other resins. The BMWD LCB PP was found to have increased flexural modulus (44%) at the cost of decreased toughness (-88%) compared to linear PP. The branched architecture and high molecular weight of the BMWD LCB PP contributed to improved foam morphologies and surface quality in high-temperature MO-FIM conditions.
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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".