Comparative Analysis of Commercial and Novel High‐Pressure Membranes for Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS) Removal
Bibliographic record
Abstract
High-pressure membrane technologies can be effective in mitigating perfluoroalkyl and polyfluoroalkyl substances (PFAS) contamination in water matrices. This review explores recent developments in both commercial (e.g., NF and RO) and novel membrane technologies, focusing on their removal mechanisms, influential factors, and challenges. Key determinants, including solution pH, PFAS molecular structure, co-contaminants, and natural organic matter, are summarized for their impacts on PFAS removal efficiency. Novel membranes incorporating materials like graphene oxide, quaternary ammonium compounds, and metal-organic frameworks are highlighted for their potential to enhance PFAS removal, particularly the removal of short-chain PFAS. Despite promising developments, challenges such as fouling, energy demands, and scalability necessitate further research. This review highlights the significance of lab-scale studies and innovative designs in bridging the gap between laboratory findings and practical applications, thereby paving the way for sustainable, large-scale PFAS treatment.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".