Customizing surface grafting and interlayer functionalization for PFOA separation in polyamide membranes
Bibliographic record
Abstract
Emerging contaminants, such as per- and polyfluoroalkyl substances (PFAS), pose significant challenges to ensuring a clean drinking water supply. This study evaluates various fabrication techniques for incorporating silver-based metal-organic frameworks (Ag-MOFs) into polyamide (PA) nanofiltration (NF) membranes to enhance perfluorooctanoic acid (PFOA) separation and anti-fouling performance. Various characterizations, including scanning and transmission electron microscopy, carboxylic group density, molecular weight cut-off (MWCO) measurements, and zeta potential analyses revealed that each method imparts distinct physicochemical and morphological characteristics to the modified membranes. Among all fabricated membranes, the interlayered Ag-MOFs (UI-MOF) obtained the highest permeance (13.7 Lm −2 h −1 bar −1 ) but the lowest PFOA rejection (88.9%), likely due to its loose PA network with large MWCO (522 Da) and high carboxylic group density (82.0 sites/nm 2 ). In contrast, the dip-coating surface-grafted Ag-MOFs (DS-MOF) achieved the highest PFOA rejection (93.4 %), attributed to its narrow pores (average pore diameter of 10 Å ± 0.06). Additionally, all modified membranes showed superior anti-fouling performance (flux recovery ratio > 94.0%) compared to the Blank PA membrane, likely due to the improved surface hydrophilicity of the modified membranes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".