Synthesis and Characterization of Anti‐fouling Ultrafiltration Nanocomposite Membranes Integrated with S‐β Zeolite Nanoparticles for Oily Wastewater Treatment
Bibliographic record
Abstract
Abstract Membrane separation has been proven to be highly effective in oily wastewater treatment, although fouling remains a persistent challenge. This study explored the incorporation of S‐β zeolite nanoparticles into an ultrafiltration membrane, forming a nanocomposite to tackle fouling and enhance water flux recovery. Porous nanoparticles were synthesized using sol‐gel and hydrothermal methods, featuring a remarkable surface area of 450 m 2 /g and pore sizes ranging from 50 to 175 nm according to BET results. These nanoparticles were integrated into the membranes at various concentrations using the phase inversion technique. The presence of S‐zeolite in the membrane was confirmed through FTIR, EDX, and SEM analyses. The S‐β1 nanocomposite membrane demonstrated exceptional antifouling properties, particularly at higher concentrations of oily wastewater. Its outstanding performance is attributed to its enhanced hydrophilicity and average pore size of 13±3 nm, which prevents fouling formation. Compared to other membranes, S‐β1 exhibited remarkable water flux recovery, reaching 100 % at an oil concentration of 50 ppm and 72 % at 1000 ppm. These results highlight the significant advantages of this nanocomposite in reducing fouling and improving the water flux recovery in oily wastewater treatment.
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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 | 0.000 | 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".