Recycling post-industrial polyester fibers
Bibliographic record
Abstract
The production of clothing has experienced exponential growth, primarily driven by ""fast fashion,"" resulting in significant environmental challenges. Alarming estimates from the Ellen MacArthur Foundation (EMF) highlight that every second, the equivalent of a truckload of textile waste is either landfilled or incinerated globally. In 2022, 52% of fibers produced were polyester (Observatory of Economic Complexity, Textile Exchange, Bettenhausen et al., 2022).This thermoplastic polymer has become one of the leading contributors to post-industrial and post-consumer plastic waste. Its properties and low cost have led to high production levels, which in turn have caused severe environmental issues. Most products made with this polymer are designed for short-term use, leading to their rapid accumulation in landfills.In response to this environmental crisis, it is imperative to rethink production and waste management practices in the textile industry. In this context, this presentation focuses on exploring sustainable solutions for the valorization of textile and plastic waste, with a particular emphasis on mechanical recycling.Mechanical recycling is preferable to chemical recycling due to its lower climate impact (4.0 t CO2 eq./t of textile waste) and reduced energy consumption (Moazzem et al., 2021b; van Duijn et al., 2022). Although chemical recycling produces higher-quality fibers, mechanical recycling offers opportunities for diverse applications, such as plastic pellets or 3D printing filaments.In collaboration with ETS and Vestechpro, a center for research and innovation in apparel, this exploratory project on the valorization of textile waste into polymeric shaping materials aims to reduce the environmental impact of textiles in Quebec while promoting their adaptation to more sustainable and circular practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.039 |
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; both teacher heads agree on what is shown here.
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".