Toward Sustainable Tackling of Biofouling Implications and Improved Performance of TFC FO Membranes Modified by Ag-MOF Nanorods
Bibliographic record
Abstract
In this work, nanorods with high antibacterial properties were synthesized with silver acetate as the metal source and 2-aminoterephthalic acid as the organic linker and were then embedded into thin-film composite (TFC) membranes to amend their performance as well as to alleviate biofouling. Silver metal–organic framework (Ag-MOF) nanorods with a length smaller than 40 nm were incorporated within the polyamide thin selective layer of the membranes during interfacial polymerization. The interaction of the synthesized nanorods with the polyamide was favored because of the presence of amine-containing functional groups on the nanorodʼs surface. The results of X-ray photoelectron spectroscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and atomic force microscopy characterizations proved the presence of Ag-MOF nanorods in the selective layer of thin-film nanocomposite (TFN) membranes. TFN membranes demonstrated improved water permeance, salt selectivity, and superior antibacterial properties. Specifically, the increased hydrophilicity and antibacterial potential of the TFN membranes led to a synergetic effect toward biofouling mitigation. The number of live bacteria attached to the surface of the neat TFC membrane decreased by more than 92% when a low amount of Ag-MOF nanorods (0.2 wt %) was applied. Following contact of the TFN membrane surface with Escherichia coli and Staphylococcus aureus, full inactivation, and degradation of bacteria cells were observed with microscopy, colony-forming unit tests, and disc inhibition zone analyses. This result translated to a negligible amount of the biofilm formed on the active layer. Indeed, the incorporation of Ag-MOF nanorods decreased the metal-ion release rate and therefore provided prolonged antibacterial performance.
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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 | 0.001 | 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".