Control of biofouling on reverse osmosis polyamide membranes modified with biocidal nanoparticles and antifouling polymer brushes
Bibliographic record
Abstract
Thin-film composite (TFC) polyamide reverse osmosis (RO) membranes are prone to biofouling due to their inherent physicochemical surface properties. In order to address the biofouling problem, we have developed novel surface coatings functionalized with biocidal silver nanoparticles (AgNPs) and antifouling polymer brushes via polyelectrolyte layer-by-layer (LBL) self-assembly. The novel surface coating was prepared with polyelectrolyte LBL films containing poly(acrylic acid) (PAA) and poly(ethylene imine) (PEI), with the latter being either pure PEI or silver nanoparticles coated with PEI (Ag-PEI). The coatings were further functionalized by grafting of polymer brushes, using either hydrophilic poly(sulfobetaine) or low surface energy poly(dimethylsiloxane) (PDMS). The presence of both LBL films and sulfobetaine polymer brushes at the interface significantly increased the hydrophilicity of the membrane surface, while PDMS brushes lowered the membrane surface energy. Overall, all surface modifications resulted in significant reduction of irreversible bacterial cell adhesion. In microbial adhesion tests with E. coli bacteria, a normalized cell adhesion in the range of only 4 to 16% on the modified membrane surfaces was observed. Modified surfaces containing silver nanoparticles also exhibited strong antimicrobial activity. Membranes coated with LBL films of PAA/Ag-PEI achieved over 95% inactivation of bacteria attached to the surface within 1 hour of contact time. Both the antifouling and antimicrobial results suggest the potential of using these novel surface coatings in controlling the fouling of RO 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".