High-fiber content composites produced from mixed textile waste: Balancing cotton and polyester fibers for improved composite performance
Bibliographic record
Abstract
This study investigates the effect of fibers from cotton and polyester textiles on the properties of fiber-reinforced polypropylene (PP) composites aimed at durable and load-bearing materials. Herein we developed a process-centered strategy to introduce 52 wt% of fibers within the thermoplastic matrix, while ensuring proper interfacial coupling. We examined the mechanical, thermal, and rheological properties of composite materials that integrated cotton and polyester waste fibers into PP matrices with different coupling agents. Our findings highlight that the balance between cotton and polyester fibers significantly influences tensile strength and stiffness, while the choice of coupling agent (maleic anhydride or glycidyl methacrylate) impacts fiber-matrix adhesion and overall material performance. Optimal tensile strength and strain are achieved with equal proportions of cotton and polyester. Utilizing a blend of coupling agents to accommodate both hydrophilic and hydrophobic fibers enhances material strength overall. The stepwise pressing-extrusion composite preparation method enabled the creation of materials containing more recycled textile fibers than the virgin polymeric binder, providing a material-focused alternative for utilizing textile waste. Thermogravimetric analysis demonstrated that the presence of textile fibers and coupling agents enhances the thermal resistance of the composites, while differential scanning calorimetry indicated improvements in structural integrity and stability under thermal stress. This research underscores the potential of mixed textile waste as a valuable resource for developing composite materials. Our work contributes to the circular economy by presenting a viable solution that complements traditional textile-to-textile recycling strategies and can be implemented in the near future.
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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) | 0.001 | 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 | 0.001 |
| 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".