Foaming Behaviors and Mechanical Properties of Injection-Molded Polylactide/Cotton-Fiber Composites
Bibliographic record
Abstract
In this study, we investigated the effects of short shot size, lubricant, and cotton fibers on the foaming and mechanical properties of poly(lactic acid) (PLA). The short shot filling capacity influenced the expansion ratio and void fraction of PLA and PLA/cotton-fiber dramatically, a set shot size of 45 cm 3 (80%) filled part presented a relatively uniform cell morphology and the improved impact strength, while 38 cm 3 (70%) filled ones exhibited an inferior cell structure, low tensile strength, and impact strength. Because of the microsize and the difficult dispersion of the cotton fiber, the tensile properties of all fiber filled solid and foamed samples decreased. Compared to the solid injection molded neat PLA, an impact strength of 52 cm 3 (100%) fully filled and 45 cm 3 filled foaming neat PLA climbed from 32.48 to 42.36 and 47.40 J/m, respectively. Moreover, at the 45 cm 3 filled level, the impact strength of 10 wt % cotton fiber added composite is 31.24 J/m, a 3.8% slight decrease was calculated compared with the solid neat PLA. The composite with a higher fiber filling content of 20 wt % resulted in a severe cell deformation and obvious less cell asymmetry, and a less impact strength of 28.65 J/m was obtained. The filling of the cotton fiber could slightly change the crystallization behavior of PLA through the injection foaming process, and the increasing of crystallinity was limited.
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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".