Processing and characterization of solid and foamed injection-molded polylactide with talc
Bibliographic record
Abstract
Polylactide (PLA) suffers from poor processability due to its low melt strength and slow crystallization kinetics and it is thus very challenging to achieve uniformly distributed fine-celled PLA foams with high void fractions in injection molding process. In this work, the low-pressure structural foam molding of linear PLA with a relatively high void fraction of ∼30% was conducted and by fine tuning the talc content and the foaming and processing parameters, a relatively uniform fine-celled structure with improved cell size and cell density was successfully produced. The effects of twin-screw compounding and the addition of talc on the foaming behavior, structural uniformity, crystallinity, and mechanical properties of the solid and foamed PLA samples were investigated. The results showed that the addition of 5 wt.% talc significantly improved the foaming properties such as cell density, cell size, structural uniformity, and consequently improved the mechanical properties of foams. The twin-screw compounding before injection molding did not significantly change the foaming behavior, but adversely affected the mechanical properties of the solid and foamed PLA samples due to mechanical and thermal degradation. The changes in the mechanical properties were discussed in terms of the crystallinity, talc toughening effect, and foam quality.
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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 | 0.001 | 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".