Sustainable Transformation of Cellulose-Containing Textile Waste into Multifunctional Panels with Tailored FR-Lignocellulosic Fibres
Bibliographic record
Abstract
The fashion industry significantly impacts the environment, mainly through the substantial generation of waste textiles fostered by fast fashion business models. This study introduces an innovative approach to textile waste management by recycling waste textiles without the use of chemical or mechanical treatments. Herein, we developed a method adhering to the principles of circular economy to transform these textile wastes into high-quality construction panels using a papermaking process. This method not only provides a sustainable solution to reduce landfill dependency but also enhances resource efficiency in the construction industry. The fabricated panels, composed of a blend of 45% textile waste microfibres and 55% fire-retardant fibres, exhibit several advantageous properties. They feature a low apparent density ranging between 170–180 kg/m3 and a low thermal conductivity coefficient of 0.047 W/m∗K at 50 kPa. It revealed that phosphorylated fibres not only provide flame-retardant properties, but they also significantly improve the mechanical properties of the panels. For example, load at break increases from 12.4 to 81.1 N, stress at break from 0.44 to 3.59 MPa, and E-modulus from 29.2 to 198.8 MPa after the addition of these 55% fibres. Moreover, these panels successfully met the criteria set by international standards for construction products satisfying the fire test, EN ISO 11925-2. These characteristics make the panels superior options for sustainable construction materials, offering enhanced fire resistance and insulation properties, which are critical to meet modern building standards. They mark a pivotal step towards sustainable construction and waste reduction in the fashion industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".