Recycling of Waste Cotton Textile Containing Elastane Fibers through Dissolution and Regeneration
Bibliographic record
Abstract
Increasing utilization of textiles has raised concern regarding the environmental impact brought by the textile manufacturing process and disposal of waste textiles. In our previous work, the dissolution of cotton waste through different solvent systems was demonstrated. Herein, this study aimed to further investigate the recycling of waste cotton–elastane fabrics using H2SO4, NaOH/urea, and LiCl/DMAc solvent systems. The structure of regenerated films was characterized with Fourier transform infrared spectroscopy and scanning electron microscopy, and the properties of the regenerated films, including transparency, mechanical properties, water vapor permeability, and thermal stability, were investigated. The results revealed that all solvent systems could convert the waste cotton–elastane fabrics into regenerated films with the existence of different forms of elastane components. The elastane fibers were partially hydrolyzed in H2SO4 solvent and reduced the transparency of regenerated films, but they were well retained in NaOH/urea solvent and interrupted the structure of regenerated cellulose films. It is worth noting that the elastane fibers were completely dissolved in LiCl/DMAc solvent and formed a composite structure with cellulose, leading to obviously improved tensile strength (from 51.00 to 121.63 MPa) and water barrier property (from 3.50 × 10−7 to 1.03 × 10−7 g m−1 h−1 Pa−1). Therefore, this work demonstrates the possibility to directly recycle waste cotton–elastane fabrics through dissolution and regeneration, and the resultant films have potential applications as packaging materials.
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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 | 0.001 |
| 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".