Use of recycled glass fibres from end-of-life wind turbine blades in thermoplastic composites for automotive, construction and 3d printing applications
Bibliographic record
Abstract
The waste created due to the rapid expansion of the wind energy industry is escalating at an exponential rate and is projected to result in 43 million tons of discarded wind blades by 2050. The first generations of wind turbines are now reaching their end-of-life, signaling the beginning of a significant future waste problem. The blades are currently sent to landfills, which is an environmentally damaging solution. Efforts to manage this waste can bring environmental-friendly solutions such as a significant reduction in CO2 emissions and the reuse of glass fibres recycled from blades. Significant quantities of recycled glass fibres can be extracted through various mechanical processes and can be compounded with a large number of thermoplastics to create new composites to be used in different industries. The aim of this work was to use glass fibres recycled from wind turbine blades in four different thermoplastics, i.e., polypropylene, polyamide, acrylonitrile butadiene styrene, and recycled polystyrene to produce composites through a compounding melt-process. The obtained eco-responsible composites were fully characterized and demonstrated mechanical, thermal, and micro-structural characteristics equivalent to commercial glass fibres composites. The results from this work indicate that recycled glass fibres from wind turbine blades can be highly valuable in the manufacturing of parts for automotive, construction, and 3D printing industries.
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 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 | 0.001 | 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 | 0.001 |
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".