Solid-state enzymatic hydrolysis of mixed PET-cotton textiles
Bibliographic record
Abstract
Abstract Waste polyester textiles trap copious amounts of useful polymers, which are not recycled due to separation challenges and partial structural degradation during use and thermo-mechanical recycling. Chemical recycling of polyethylene terephthalate (PET) through depolymerization can provide a feedstock of recycled monomers to make “as-new” polymers, and reduce the accumulation of plastic waste in landfills. Enzymes are highly specific, renewable, environmentally benign catalysts, with hydrolases available that are active on common PET textile fibers and on cotton. The enzymatic PET recycling methods in development, however, have thus far been limited to clean, high-quality PET feedstocks, and most such processes require an energy-intensive melt-amorphization step ahead of enzymatic depolymerization. Here we report that high-crystallinity PET in mixed PET/cotton textiles can be directly and selectively depolymerized to terephthalic acid (TPA) by using a commercial cutinase from Humicola insolens under moist-solid reaction conditions, affording up to 30 ± 2% yield of TPA. The process is readily combined with cotton depolymerisation through simultaneous application of cellulase enzymes (CTec2 ® ), providing up to 83 ± 4% yield of glucose without any negative influence on the TPA yield. The herein presented selective and/or simultaneous enzymatic hydrolysis of PET/cotton textiles in solid reaction mixtures can expand the biocatalytic recycling processes of PET to less-valuable waste materials, and significantly increase its profitability through operating at very high solid-loading (40%), without the need for melt-amorphization.
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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".