Graphene Oxide Surface Modification of Reverse Osmosis (RO) Membrane via Langmuir–Blodgett Technique: Balancing Performance and Antifouling Properties
Bibliographic record
Abstract
The reverse osmosis water treatment process is prone to fouling issues, prompting the exploration of various membrane modification techniques to address this challenge. The primary objective of this study was to develop a precise method for modifying the surface of reverse osmosis membranes to enhance their antifouling properties. The Langmuir-Blodgett technique was employed to transfer aminated graphene oxide films assembled at the air-liquid interface, under specific surface pressure conditions, to the polyamide surface with pre-activated carboxylic groups. The microstructure and distribution of graphene oxide along the modified membrane were characterized using SEM, AFM, and Raman mapping techniques. Modification carried out at the optimal surface pressure value improved the membrane hydrophilicity and reduced the surface roughness, thereby enhancing the antifouling properties against colloidal fouling. The flux recovery ratio after modification increased from 65% to 87%, maintaining high permeability. The modified membranes exhibited superior performance compared to the unmodified membranes during long-term fouling tests. This membrane modification technique can be easily scaled using the roll-to-roll approach and requires minimal consumption of the modifier used.
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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) | 0.000 | 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 | 0.001 | 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".