Lightweight and tough PP/talc composite foam with bimodal nanoporous structure achieved by microcellular injection molding
Bibliographic record
Abstract
Although polypropylene (PP) foams fabricated by microcellular injection molding (MIM) have been widely studied and globally applied, it is still a big challenge to achieve lightweight products with high mechanical performance. Herein, the MIM method assisted by a fast cooling design was applied to prepare lightweight bimodal nanoporous PP foams with high toughness. A specific mold with a thin cavity was designed to achieve rapid cooling rate during MIM process , and to further accelerate crystallization and refine crystals, which was verified by visualization results, DSC curves, and WAXD patterns. Thus, nanocellular PP foams were prepared, whose high toughness was presented in tensile testing , especially for bimodal cellular foams, more than 327% higher than that of microcellular foams, and up to 53% higher than that of solid. It is owing to the introduced matrix craze, shear yielding, and larger plastic zone by nanocells and the transformed crack propagation direction by microcells. Therefore, this novel toughening method paves a promising prospect for MIM technology to achieve lightweight and tough products for industrial applications.
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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) | 0.001 | 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".