Membrane-based nanopurification for plastic recycling
Bibliographic record
Abstract
Purification technologies that remove contaminants from waste plastics are critical to increasing plastic recyclability. Mechanical recycling cannot remove embedded additives, dissolution methods are limited by additive–polymer compatibility and chemical recycling requires strict control of contamination to prevent undesired reactions. This work introduces a membrane-based size-exclusion process that exploits a key property of plastics: polymer molecules typically have a molecular weight significantly higher than that of common additives. A case study using ceramic tubular ultrafiltration membranes demonstrates removal of over 90% of hexabromocyclododecane (HBCD) from both virgin and post-consumer polystyrene, while also eliminating polymer tailings originating from degraded polymer chains. By targeting the size difference between polymers and additives, this approach opens new opportunities for regeneration of plastics and offers a pathway to broader recyclability. Applied to common plastics such as PE, PP, PS, PVC, and PU, this framework could increase the fraction of recycled plastics from ~9% to over 68.5%. Purification technologies capable of removing contaminants from plastic waste are key to increasing plastic recyclability. Here, the authors report a membrane-based size-exclusion process and show that ceramic tubular ultrafiltration membranes can remove over 90% of hexabromocyclododecane from virgin and post-consumer polystyrene, while also eliminating polymer tailings from degraded polymer chains.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".