A Paradigm Shift in End-of-Life Membrane Recycling: From Conventional to Emerging Techniques
Bibliographic record
Abstract
The conventional linear life cycle of membrane materials, spanning fabrication, use, and disposal through landfilling or incineration poses serious sustainability challenges. The environmental burden associated with both the production of new membranes and the disposal of end-of-life (EoL) modules is considerable, further intensified by the reliance on fossil fuel-derived polymers, toxic solvents, and resource-intensive manufacturing processes. These challenges underscore the urgent need to integrate sustainability principles across the entire membrane life cycle, from raw material selection to reuse and regeneration. Emerging approaches such as membrane regeneration using recyclable polymers based on covalent adaptable networks (CANs) have introduced a new paradigm of closed-loop design, enabling complete depolymerization and reformation. In parallel, more conventional strategies, including the valorization of recycled plastic waste and the upcycling or downcycling of EoL membranes, offer practical routes toward a circular membrane economy. In this review, we consolidate current advances in membrane recycling, critically evaluate their practical constraints, and delineate the technical and environmental challenges that must be addressed for broader implementation. The insights presented here aim to guide the development of next-generation circular membrane technologies that harmonize sustainability with performance.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".