Facile Epitaxial Growth of Novel Nanoscale Ag-MAFs on Reverse Osmosis Membranes: Enhancing Performance, Antibacterial Activity, and (Bio)fouling Resistance
Bibliographic record
Abstract
The increasing demand for advanced thin-film composite (TFC) membranes stems from the limitations of current commercial membranes, particularly their vulnerability to biofouling. In this study, novel silver-based metal-azolate frameworks (Ag-MAFs) were grown insitu on the surface of TFC reverse osmosis (RO) membranes. This functionalization resulted in a 45% increase in permeate flux without compromising salt rejection (97.6%) compared to pristine TFC membranes. The surface functionalization process is rapid, non-destructive, and employs eco-friendly solvents, silver salts, and amino-benzimidazole ligands, enabling repeatable modifications without affecting separation efficiency. The successful integration of Ag-MAFs onto the membrane surface was confirmed through comprehensive chemical characterization, including Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and energy dispersive X-ray (EDX) analysis. Notably, Ag-MAFs demonstrated strong stability, with no detectable leaching or detachment after 20 days of continuous water immersion. Morphological analysis using scanning electron microscopy (SEM) and confocal microscopy revealed that Ag-MAFs nanoparticles imparted robust antibacterial activity, reducing live bacterial populations by nearly 99%. Filtration tests showed that Ag-MAFs functionalized membranes exhibited superior fouling resistance and higher water recovery ratios than pristine membranes during a 10 h filtration cycle. This study presents a scalable and reproducible approach for developing advanced antibiofouling TFC membranes capable of long-term operation, eliminating the need for module disassembly and enhancing membrane longevity in practical applications.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".