Customizing surface grafting and interlayer functionalization for PFOA separation in polyamide membranes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 it