Talc reinforcement of polylactide and biodegradable polyester blends via injection‐molding and pilot‐scale film extrusion
Bibliographic record
Abstract
Abstract As a renewable and biodegradable polymer, polylactide (PLA) has taken a foothold in the packaging industry. However, the thermomechanical and barrier properties of PLA‐based films need to be improved to facilitate a wider adoption. To address this challenge, we examined the effect of talc reinforcement in composites based on PLA and a biodegradable polyester. Masterbatches of the polymers and talc were produced by melt compounding and processed by either injection‐molding or film extrusion in a pilot‐scale unit operating at 60–80 m/min. The effect of talc was investigated in relation to the morphological, thermal, mechanical, and barrier properties of the composites. Based on SEM‐imaging, talc was found to increase the miscibility of PLA and the polyester while acting as a nucleating agent that improved PLA crystallinity. While this effect did not track with an increased mechanical strength, the composites with 3–4 wt% talc displayed a significantly higher barrier to water vapor. Compared to the neat polymer films, a reduction of water vapor transmission rate, by ~34–37%, was observed at 23°C/50% RH. Meanwhile, the systems loaded with 1 wt% talc showed a reduction in oxygen transmission rates, by up to 34%. Our results highlight the challenges and prospects of commercial PLA‐based blends filled with talc from films extruded in pilot‐scale units.
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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".