Removing antibiotics from water is critical to prevent the emergence and spread of antibiotic resistance, protect ecosystems, and maintain the effectiveness of these vital medications. The combination of ozone and electrocoagulation in wastewater treatment provides enhanced removal of contaminants, improved disinfection efficiency, and increased overall treatment effectiveness. In this work, the removal of sulfamethoxazole (SMX) from an aqueous solution using an ozone–electrocoagulation (O–EC) system was optimized and modeled. The experiments were designed according to the central composite design. The parameters, including current density, reaction time, pH, and ozone dose affecting the SMX removal efficiency of the OEC system, were optimized using a response surface methodology. The results show that the removal process was accurately predicted by the quadric model. The numerical optimization results show that the optimum conditions were a current density of 33.2 A/m², a time of 37.8 min, pH of 8.4, and an ozone dose of 0.7 g/h. Under these conditions, the removal efficiency reached 99.65%. A three-layer artificial neural network (ANN) with logsig-purelin transfer functions was used to model the removal process. The data predicted by the ANN model matched well to the experimental data. The calculation of the relative importance showed that pH was the most influential factor, followed by current density, ozone dose, and time. The kinetics of the SMX removal process followed the first-order kinetic model with a rate constant of 0.12 (min⁻¹). The removal mechanism involves various processes such as oxidation and reduction on the surface of electrodes, the reaction between ozone and ferrous ions, degradation of SMX molecules, formation of flocs, and adsorption of species on the flocs. The results obtained in this work indicate that the O–EC system is a potential approach for the removal of antibiotics from water.

The present study deals with the fabrication of activated carbon from longan peels (LPAC) using a phosphoric acid (H₃PO₄) activation method and an evaluation of LPAC’s capability for the adsorption of Reactive Black 5 (RB5) dye from aqueous solutions. The synthesized LPAC was characterized using XRD, SEM, FT-IR, and EDX, confirming a porous, carbon-rich structure with the dominant elemental composition of carbon (85.21%) and oxygen (12.43%), and a surface area of 1202.38 m²/g. Batch adsorption experiments revealed that optimal performance was achieved at pH 3.0, with equilibrium reached after 240 min. The experimental data were well fitted to the Elovich model p, suggesting a heterogeneous adsorption process with diffusion limitations. The intraparticle diffusion model further supported a multi-stage mechanism involving both film diffusion and intraparticle transport. Isotherm studies conducted at varying temperatures (293–323 K) showed a maximum adsorption capacity exceeding 370 mg/g. The adsorption data fit best with the Freundlich (R² = 0.962) and Temkin (R² = 0.970) models, indicating multilayer adsorption on a heterogeneous surface. Thermodynamic analysis revealed that the adsorption process was spontaneous and endothermic, with ΔG° values ranging from −23.15 to −26.88 kJ/mol, ΔH° = 14.23 kJ/mol, and ΔS° = 0.127 kJ/mol×K, consistent with physisorption as the dominant mechanism. Predictive modeling using an artificial neural network (ANN) achieved superior accuracy (R² = 0.989 for RRE; R² = 0.991 for q) compared to multiple linear regression (MLR). Calculation from ANN indicated that pH and contact time were the most influential factors for RB5 removal efficiency, while initial dye concentration and temperature were most critical for adsorption capacity. Furthermore, LPAC demonstrated excellent reusability, retaining over 83% removal efficiency after five adsorption–desorption cycles. These findings confirm that LPAC is an efficient and renewable adsorbent for the treatment of RB5 dye in wastewater treatment applications.

The fabrication of nonenzymatic glucose sensors is essential because of the enhancement in the selectivity and accuracy of these sensors. In this work, we used the electrodeposition approach to prepare a CeO₂-based electrode for nonenzymatic glucose detection. A CeO₂ branched-like nanostructure was successfully fabricated by electrodeposition on the surface of a Au substrate electrode at room temperature. The effects of cyclic voltammetry, CH₃COOH content, and scan cycle number on the formation of the CeO₂ branched-like nanostructure were investigated. The fabricated electrodes were characterized by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). The results showed that a CeO₂ branched-like nanostructure could be obtained with a CH₃COOH content of 1.0 mL and a scan cycle number of 100 in a solution containing 0.015 M Ce(NO₃)₃, 0.01 M KCl, and 0.02 M CH₃COONH₄ and with a scan rate of 400 mV/s. The electrochemical characteristics of the sensor were examined by chronoamperometry and cyclic voltammetry. The results showed that the sensitivity of the sensor was 37.72 μA/mM·cm² and the limit of detection (LOD) of the sensor was 0.093 mM. The findings in this work prove that it is feasible to fabricate CeO₂-based sensors for nonenzymatic glucose detection.