Waste Reutilization in Polymeric Membrane Fabrication: A New Direction in Membranes for Separation
Bibliographic record
Abstract
In parallel to the rapid growth in economic and social activities, there has been an undesirable increase in environmental degradation due to the massively produced and disposed waste. The need to manage waste in a more innovative manner has become an urgent matter. In response to the call for circular economy, some solid wastes can offer plenty of opportunities to be reutilized as raw materials for the fabrication of functional, high-value products. In the context of solid waste-derived polymeric membrane development, this strategy can pave a way to reduce the consumption of conventional feedstock for the production of synthetic polymers and simultaneously to dampen the negative environmental impacts resulting from the improper management of these solid wastes. The review aims to offer a platform for overviewing the potentials of reutilizing solid waste in liquid separation membrane fabrication by covering the important aspects, including waste pretreatment and raw material extraction, membrane fabrication and characterizations, as well as the separation performance evaluation of the resultant membranes. Three major types of waste-derived polymeric raw materials, namely keratin, cellulose, and plastics, are discussed based on the waste origins, limitations in the waste processing, and their conversion into polymeric membranes. With the promising material properties and viability of processing facilities, recycling and reutilization of waste resources for membrane fabrication are deemed to be a promising strategy that can bring about huge benefits in multiple ways, especially to make a step closer to sustainable and green membrane production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".