Synergistic Adsorption of Methyl Orange Using MgO/Clinoptilolite Nanocomposites: Characterization, Isotherm Analysis, and Optimization through Response Surface Methodology

نویسندگان

1 Department of Chemistry, Faculty of Sciences, Mahabad Branch, Islamic Azad University, Mahabad, I.R. IRAN

2 Scientific Research Center, Soran University, Soran, Kurdistan Region, IRAQ

3 Department of Physical Chemistry, Faculty of Chemistry, University of Kashan, Kashan, I.R. IRAN

4 Department of Applied Chemistry, Faculty of Chemistry, University of Kashan, Kashan, I.R. IRAN

doi
10.30492/ijcce.2025.2038275.6742
چکیده

A novel adsorbent combining magnesium oxide (MgO) nanostructures with natural clinoptilolite (Cp) zeolite, a cost-effective clay, was developed. This adsorbent aimed to stabilize MgO nanostructures and explore the synergistic effect of the zeolite surface and MgO groups in adsorbing Methyl Orange (MO), a toxic anionic dye. Various adsorbents were synthesized via a co-precipitation method and characterized using X-Ray Diffraction (XRD), scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET) surface area analysis, and Fourier Transform InfraRed (FT-IR) spectroscopy. The findings revealed that the adsorbent with 20 wt.% MgO/Cp exhibited superior performance in MO adsorption. Isotherm data were evaluated using multiple models, including Langmuir, Freundlich, Sipes, Temkin, Flory-Huggins, and Redlich–Peterson. For the 20 wt.% MgO/Cp adsorbent, the Temkin and Langmuir isotherms were deemed most fitting for equilibrium analysis. Kinetic studies favored the Fractal-Langmuir model, and thorough thermodynamic investigations were conducted. Response surface methodology (RSM) was utilized to optimize the effects of pH, temperature, and adsorbent-to-adsorbate ratio (A/a) on dye removal efficiency. The optimal conditions for maximal dye removal were identified at pH 4.9, a temperature of 54.5 °C, and an A/a ratio of 0.129 g/g. Under these conditions, the experimental removal of MO was 98.5%, closely aligning with the predicted value of 99.95%.