Recycling of inherently flame-resistant fabrics for protective clothing: A comprehensive review
Bibliographic record
Abstract
The accelerated consumption of flame-resistant (FR) fabrics increases the amount of textile waste. Moreover, aramids take a very long time to degrade in landfills and should not be incinerated. Mechanical recycling offers opportunities to tackle this challenge. Yet, limited information is available on this topic. The mechanical recycling process comprises collection of cleaned used FR garments, sorting of fabrics based on fibre content and color, removal of accessories, shredding, blending with virgin fibres, spinning into yarns, knitting/weaving, dyeing, and production of new FR garments. Remaining challenges include the presence of residual contaminants from prior fire exposure; reduction in fibre length after shredding; difficult balance between performance and cost; and dyeing conditions to accommodate the different fibres and residual color on the recycled fibres. Moving forward, researchers should optimize the processes from used garment collection to new FR garment production as well as develop solutions to remove the per- and polyfluoroalkyl substances (PFAS) liquid-repellent finishes from the fabrics prior to recycling. It will also be important to assess the long-term performance of fabrics made with recycled fibres. Combining the different expertise required to tackle these challenges will be key for mechanical recycling to improve the sustainability of FR protective clothing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".