Fuzzy logic-assisted development of high-performance mesoporous iron-carbon nanocomposites for environmental applications

This study aimed to synthesis mesoporous iron oxide–carbon (MIC) nanocomposites using P123 and gelatin as dual surfactants and evaluate their effectiveness in degrading methylene blue dye. Five catalyst variations were prepared with gelatin ratios ranging from 0% to 20% through sol-gel hydrothermal synthesis. The materials were characterized using FT-IR, XRD, SEM, TEM, EDX, BET, and TGA techniques. Among all samples, MIC10% demonstrated the most favorable physicochemical characteristics, including the highest surface area of 26.92 m²/g, well-defined mesoporous structure, and optimal particle size distribution. Photocatalytic tests revealed that MIC10% achieved 96.23% methylene blue degradation efficiency under UV light and maintained over 80% activity after four regeneration cycles, confirming its excellent stability and reusability. A Mamdani fuzzy logic model was developed to correlate physicochemical properties—surface area, porosity, carbon content, and particle size—with photocatalytic performance. The model showed strong predictive accuracy with R² = 0.91, and sensitivity analysis identified particle size and porosity as the most influential factors. This integrated experimental-computational approach not only provides insights into structure-activity relationships but also offers a practical framework for designing efficient photocatalysts for environmental remediation applications.