Textile Recycling’s Hidden Problem: Surface-Modified Fiber Fragments Emitted at Every Stage
Bibliographic record
Abstract
Chemical recycling methods for post-consumer textile waste are effective for sustainable textile waste management. However, recycling synthetic and blended (cotton and synthetic) textiles can contribute to the release of microplastic fibers (MPFs) into the environment. This study investigated MPF release across different stages of two chemical recycling approaches, acid and alkaline hydrolysis, of polyester/cotton-blended textiles. Recycling involves various stages, including dye removal, treatment stage, and product. In the treatment stage, acid hydrolysis breaks down cotton into cellulose, leaving the polyester (PET) intact, whereas alkaline hydrolysis degrades PET, allowing cotton recovery. Across all stages, dye removal generated the highest MPF count, averaging nearly 10,055 MPFs g –1 of textile waste. Statistical analysis confirmed that the recycling approach significantly affected MPF release ( p < 0.05), whereas the fabric type did not ( p > 0.05). Alkaline hydrolysis reduced MPF release during the treatment stage by 87.55% compared to acid hydrolysis, indicating that recovering cotton and chemically degrading PET can significantly minimize MPF emissions during recycling. Ridge regression analysis identified the reaction conditions as key factors in MPF fragmentation, with blend ratios influencing the number of released MPFs. Surface characterization revealed treatment-induced fiber alterations, raising concerns regarding MPF emissions throughout the process. These findings highlight the textile recycling industries can be a source of MPF release into the environment, but recovering PET through degradation or dissolution can help minimize this impact of the treatment stage.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".