Recycling of waste cotton fabrics into regenerated cellulose films through three solvent systems: A comparison study
Bibliographic record
Abstract
Abstract Large amounts of textile waste are generated every year and disposed of through landfill or incineration, leading to numerous environmental and social issues. In this work, the dissolution of three typical waste cotton fabrics (t‐shirts, bed sheets and jeans) in NaOH/urea aqueous solution, H2SO4 aqueous solution, and LiCl/DMAc solution was investigated. Compared to different types of cotton fabrics, the effects of three solvents on the dissolution of fabrics were more obvious, leading to the significant changes in the structure and properties of regenerated cellulose films. Cotton fabrics (about 2%–5%) were rapidly dissolved (8 min) in H2SO4 and NaOH/urea solvents after acid pretreatment, while the dissolution in LiCl/DMAc solvent did not need any pretreatment, but a lower cellulose concentration (1%), higher dissolution temperature (80°C), and longer dissolution time (24 h) were required. The films produced from bed sheets in NaOH/urea solution exhibited the highest tensile strength, thermal stability, and water vapor barrier property. It was because of the stronger cellulose chain entanglement and hydrogen bonds induced by the higher cellulose concentration in NaOH/urea solution. Therefore, this work proves the feasibility to recycle waste cotton fabrics into biodegradable cellulose films, which can be potentially used in various food and agricultural applications.
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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 | 0.000 |
| 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".