Synthesis and characterization of fly ash–graphene oxide nanocomposite ceramic membranes
Bibliographic record
Abstract
Abstract The utilization of fly ash in ceramic membrane fabrication presents a sustainable solution to reduce solid waste disposal and lower production costs, positioning it as a promising material for membrane development. In this study, fly ash‐based photocatalytic nanocomposite hybrid ceramic membranes and systematically evaluates their structural, thermal, and functional properties. X‐ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier‐transform infrared spectroscopy (FTIR) were employed to characterize the crystallinity, morphology, and functional groups. SEM imaging confirmed a defect‐free surface with a porous architecture, whereas XRD identified dominant crystalline phases, including quartz, haematite, and mullite. Thermogravimetric analysis (TGA) established 750°C as the optimal sintering temperature, yielding membranes with 30.63% porosity and an average hydraulic pore radius of 0.311 μm. To enhance photocatalytic performance, graphene oxide (GO) was integrated via spin‐coating during phase inversion, achieving a significant increase in pure water flux of 0.042 to 0.145 L/(m 2 min bar) under 1–4 kg/cm 2 transmembrane pressures. This study uniquely investigates the influence of fly ash particle size on membrane properties, providing actionable insights for selecting appropriate fly ash grades for fabrication processes. Furthermore, membranes sintered at varying temperatures were compared to validate thermal stability and structural integrity. These findings underscore the potential of fly ash‐derived ceramic membranes in sustainable water treatment applications, combining cost efficiency, waste valorization, and enhanced separation performance through tailored nanocomposite design.
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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 | 0.001 | 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".