Unveiling Microplastic Release from Discarded Textiles: A Potential Threat to Aquatic Environments
Bibliographic record
Abstract
The life cycle of clothing is becoming increasingly shorter due to the rapid growth of fast fashion. Developed countries export tons of clothes to developing nations, particularly in Africa. However, rather than being reused as intended, a significant amount of this clothing ends up discarded on shores and riverbanks, leading to various environmental problems. This study explores the microplastic release from discarded clothes into the water body when exposed under different conditions, assessing their environmental impact. Two commonly used types of synthetic fabrics (polyester and nylon) were selected as representative materials. Scanning electron microscopy (SEM) revealed that ultraviolet (UV) irradiation damaged the fiber surface and released tiny particles. Fourier-transform infrared spectroscopy (FTIR) tests indicated that UV irradiation leads to the degradation of the polymers that compose the fibers, and cross-linking or chain breaking occurs, leading to the friability of the fibers, resulting in the release of microplastics. However, the short-term UV radiation resulted in a more stable mechanical strength of the nylon fibers due to the cross-linking that occurs. In addition, abrasion from grit and changes in turbulent kinetic energy can rapidly damage fibers, accelerating the release of microplastics from the fabrics. Experimental results demonstrated that nylon textiles were more likely to release microplastics. However, polyester fibers had a greater tendency to release microplastics with increasing turbulent rotational speed. Future research could further investigate the environmental risks associated with microplastic release from discarded 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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".