Review on electrospinning of recycled polymer-derived fibers: A road towards sustainability, production and applications
Bibliographic record
Abstract
In recent decades, the substantial growth in the production of non-biodegradable plastics led to a striking increase in environmental pollution. Conventional methods to manage these wastes, such as landfilling, incineration or physical recycling, are in many cases not only inefficient, but also costly and environmentally damaging. In these cases, the need for innovative and sustainable methods to reduce and reuse plastics waste is mandatory. One of the new and efficient approaches is the use of electrospinning and similar technology to directly convert plastics waste into thin fibers as an upcycling method. This process allows the production of micro- and nano-scale fibers from recycled polymers. These fibers can then be used in various applications, such as filtration, medicine, packaging and advanced engineering materials and composites (as reinforcements). This is related to their interesting properties, such as high contact surface area, tunable porosity and good strength depending on the original polymer. In this review, potential applications for this technology are presented in using polymer wastes and to describe its role as a sustainable solution in reducing the negative impacts of plastics on the environment. The scientific basis and functional advantages of this method are described first and experimental studies conducted in the field of electrospinning of recycled polymers are reviewed next. Finally, some conclusions are reported with openings for future developments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".