Reinforced graphene oxide–acrylamide/sodium acrylamide hydrogel composite-coated meshes for the separation of stabilized oil–surfactant emulsions from greywater
Bibliographic record
Abstract
• Design of hydrogel membranes embedded with graphene oxide • Treatment of kitchen greywater (KGW) containing oil and surfactant • Separation of oil and surfactant using reinforced hydrogel membranes • Optimal composition of the hydrogel membrane for highest separation efficiency • Sustainable solution for addressing wastewater treatment Kitchen greywater, which is high in fats, oils, and grease (FOG), as well as surfactants, poses a significant environmental threat due to its ability to contaminate water sources and impair wastewater infrastructure. In this study, a reinforced composite hydrogel composed of acrylamide (AM), sodium acrylate (Na-Ac), and graphene oxide (GO) is synthesized via graft polymerization and applied as a coating over stainless-steel meshes with various pore sizes (200, 255, and 405 µm) to treat oil/surfactant/water mixtures. Experiments are conducted with various acrylamide (AM) compositions (50, 55, and 60 wt%) and graphene oxide (GO) loadings (10, 20, and 40 mg) to investigate the influence of composition and mesh size on the separation efficiency. Oil removal efficiency as high as 89% and surfactant removal up to 80% were achieved with the proposed hydrogel membranes The statistical models yielded near-ideal fits for both responses (R² = 0.9994 for oil removal and R² = 1.000 for surfactant removal), indicating excellent predictive reliability across the tested formulation and operating conditions. These findings suggest that the AM/GO hydrogel-coated mesh can be effectively tuned to target either oil, surfactant, or combined removal, making it a promising candidate for compact or modular treatment units. In this way, the recovered water could be reused for secondary purposes, contributing to more sustainable kitchen wastewater management and supporting multiple Sustainable Development Goals (SDGs).
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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".