Synthesis, characterisation and artificial neural network modelling of methylene blue adsorption of TiO2 and ZnO coated zeoli̇tes

In this study, the adsorption and degradation conditions of methylene blue (MM) from aqueous solutions were investigated using composite materials synthesized by coating Turkish and Hungarian zeolites with ZnO-Np and TiO2. The effects of various parameters on adsorption, including initial pH (3-10), initial dye concentration (50-200 mg/L), adsorbent dosage (0.05-1.00 g/100mL), temperature (298-313 K), and contact time (5-300 min), were systematically evaluated. The adsorption performance of the synthesized composite materials was assessed through isotherm, kinetic, and thermodynamic analyses in relation to factors such as time, temperature, degradation, adsorbent dosage, concentration, and pH. The initial and post-adsorption concentrations of methylene blue solutions were measured using a UV-Visible spectrophotometer in batch experiments conducted with a shaking method. From these measurements, adsorption capacities and rate constants were calculated. Throughout the experiments, it was observed that the synthesized composites exhibited varying adsorption behaviors under different pH values and temperature conditions. These findings demonstrated that the composites developed different interaction mechanisms depending on environmental parameters, significantly influencing adsorption performance. Detailed analyses of the synthesized products were conducted using advanced characterization techniques such as X-ray Diffraction (XRD), Brunauer-Emmett-Teller (BET) surface area analysis, Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), and Selected Area Electron Diffraction (SAED). These methods provided comprehensive insights into the structural changes, surface areas, and other physical and chemical properties of the materials. Additionally, desorption studies were performed to evaluate the sustainability of the adsorbent's efficiency, investigating its reuse potential. This approach highlighted both the performance and the environmental and economic advantages of the synthesized products. The experimental results revealed that at pH 8 and room temperature (298 K), increasing the dosage of the synthesized composites enhanced MM adsorption. In this study, initial pH, concentration, contact time, and initial adsorbent dosage were determined as input variables for the adsorption system's Artificial Neural Network (ANN) model. The dye removal rate was used as the output parameter. Experimental data for ANN training were divided into 90% training, 10% validation, and 10% testing datasets, with a maximum epoch value of 19,000. Materials such as ZnO-TZ, ZnO-MZ, TiO2-TZ, and TiO2-MZ were identified as highly suitable and effective adsorbents for adsorption processes. Key reasons for this include their large surface areas, appropriate pore sizes that favor adsorption, and high adsorption capacities. Furthermore, artificial intelligence-based analysis and modeling demonstrated that these materials achieved successful results in removing dyes like methylene blue from aqueous solutions. Considering these features, the materials stand out as practical and sustainable alternative adsorbents in combating environmental pollution. This study comprehensively addresses the potential of these composite materials to serve as efficient adsorbents for environmental remediation, showcasing their adaptability to different environmental conditions and their alignment with sustainability objectives. The thermodynamic analysis provided additional insights into the spontaneity and nature of the adsorption processes. The calculated Gibbs free energy (∆G°), enthalpy (∆H°), and entropy (∆S°) values indicated that the adsorption process was endothermic and spontaneous under the conditions studied. Kinetic studies further revealed that the adsorption followed a pseudo-second-order kinetic model, suggesting that chemisorption played a significant role in the removal of methylene blue. The isotherm analysis showed that the Langmuir model best described the adsorption equilibrium data, indicating monolayer adsorption on a homogeneous surface. The structural characterization of the synthesized composites demonstrated that the TiO2 and ZnO nanoparticles were uniformly distributed on the zeolite surfaces, enhancing their adsorption capacity and photocatalytic degradation efficiency. The BET analysis confirmed the high surface area of the composites, which is a critical factor for their effective adsorption performance. SEM and TEM images illustrated the morphological features and the distribution of nanoparticles, while FTIR spectra identified the functional groups involved in the adsorption process. XRD patterns confirmed the crystalline nature of the composites, and SAED analysis provided additional details about the crystal structures. Desorption experiments highlighted the reusability of the synthesized adsorbents, with minimal loss in adsorption efficiency over multiple cycles. This characteristic is vital for their application in large-scale wastewater treatment systems, as it reduces operational costs and environmental impact. The study also explored the potential of these composites to degrade methylene blue under UV light, leveraging the photocatalytic properties of TiO2 and ZnO. The combined adsorption and photocatalytic degradation processes resulted in higher removal efficiencies, showcasing the dual functionality of the synthesized materials. Artificial Neural Network modeling further validated the experimental results by accurately predicting dye removal efficiencies under various conditions. The high correlation coefficients obtained during ANN validation demonstrated the reliability of the model in simulating complex adsorption systems. This integration of experimental and computational approaches provides a robust framework for optimizing the design and application of composite adsorbents in environmental remediation. In addition to their adsorption capabilities, the synthesized composites were evaluated for their potential to contribute to circular economy principles. The ability to regenerate and reuse these materials without significant loss of performance aligns with the goals of sustainable development and resource efficiency. The study also emphasized the role of advanced materials in addressing global water scarcity issues by providing cost-effective and scalable solutions for water treatment. Future research directions could include exploring the composites' effectiveness in removing other contaminants, such as heavy metals, pharmaceuticals, and industrial effluents, from wastewater. Investigating the impact of long-term usage on structural stability and adsorption performance can further enhance the practical application of these materials. Moreover, integrating these composites with renewable energy sources for photocatalytic degradation could pave the way for energy-efficient wastewater treatment systems. In conclusion, the synthesized composite materials offer a promising solution for removing methylene blue from aqueous solutions. Their high adsorption capacities, coupled with their photocatalytic degradation abilities and reusability, make them ideal candidates for sustainable wastewater treatment. This study not only underscores the potential of these materials but also contributes to the development of advanced methodologies for tackling water pollution through innovative and environmentally friendly technologies. The integration of experimental insights with computational modeling further highlights the interdisciplinary approach required to address complex environmental challenges effectively.