PBAT/PLA copolymer properties improved with end-of-life polyester, viscose, cotton, and silk fibers
Bibliographic record
Abstract
Bioflex is a biodegradable polymer blend combining poly(butylene adipate-co-terephthalate) (PBAT) and bio-based poly(lactic acid) (PLA), offering properties comparable to polyethylene. However, challenges like limited processability and low mechanical properties restrict its use to agricultural films. In this study, fibers from end-of-life textiles (polyester, viscose, cotton, and silk) are used to address these limitations, demonstrating a resource-efficient approach to reducing landfill deposits. Adding fibers to the polymer blend (30 wt%) visibly improves the melt strength. The end-of-life fibers affect the mechanical properties in different ways: polyester fibers almost double the tensile strength, viscose fibers triples flexural strength, and silk fibers lead to the highest compressive strength. The retained colors of the fibers further contribute to vibrant composites, making them ideal for cosmetics packaging, household goods, fashion accessories, and toys. Additionally, the composting test revealed varied disintegration behaviors. Cotton and silk began disintegrating first, viscose followed, while polyester showed no disintegration, extending the composite's durability in use. This study highlights the potential of end-of-life textiles as an excellent reinforcement for Bioflex copolymer blends, promoting efficient resource use, reducing environmental waste, and unlocking new application areas for biodegradable polymers. • Bioflex (PBAT/PLA) presented improved flow and strength with end-of-life fibers. • Retained fiber colors make composites ideal for various consumer products (cosmetics packaging). • Polyester doubles tensile strength, viscose triples flexural, silk boosts compression. • End-of-life fibers slow down the disintegration rate in composting tests. • End-of-life fibers in Bioflex expand applications for bioplastics and reduce waste.
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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".