MgO-LaO mixed metal oxides heterostructure catalysts for photodegradation of dyes pollutant: synthesis, characterization and artificial intelligence modelling.

In the present work, we prepared MgO-LaO-mixed-metal oxides (MMO) as efficient photocatalysts for degradation of organic pollutants. First, a series of MgAl-%La-CO-layered double hydroxide (LDH) precursors with different contents of La (5, 10, and 20 wt%) were synthesized by the co-precipitation process and then calcined at 600 °C. The prepared materials were characterized by XRD, SEM-EDX, FTIR, TGA, ICP, and UV-vis diffuse reflectance spectroscopy. XRD indicated that MgO, LaO, and MgAlO phases were found to coexist in the calcined materials. Also, XRD confirms the orthorhombic-tetragonal phases of MgO-LaO. The samples exhibited a small band gap of 3.0-3.22 eV based on DRS. The photocatalytic activity of the catalysts was assessed for the degradation of two dyes, namely, tartrazine (TZ) and patent blue (PB) as model organic pollutants in aqueous mediums under UV-visible light. Detailed photocatalytic tests that focused on the impacts of dopant amount of La, catalyst dose, initial pH of the solution, irradiation time, dye concentration, and reuse were carried out and discussed in this research. The experimental findings reveal that the highest photocatalytic activity was achieved with the MgO-LaO-10% MMO with photocatalysts with a degradation efficiency of 97.4% and 93.87% for TZ and PB, respectively, within 150 min of irradiation. The addition of La to the sample was responsible for its highest photocatalytic activity. Response surface methodology (RSM) and gradient boosting regressor (GBR), as artificial intelligence techniques, were employed to assess individual and interactive influences of initial dye concentration, catalyst dose, initial pH, and irradiation time on the degradation performance. The GBR technique predicts the degradation efficiency results with R = 0.98 for both TZ and PB. Moreover, ANOVA analysis employing CCD-RSM reveals a high agreement between the quadratic model predictions and the experimental results for TZ and PB (R = 0.9327 and Adj-R = 0.8699, R = 0.9574 and Adj-R = 0.8704, respectively). Optimization outcomes indicated that maximum degradation efficiency was attained under the following optimum conditions: catalyst dose 0.3 g/L, initial dye concentration 20 mg/L, pH 4, and reaction time 150 min. On the whole, this study confirms that the proposed artificial intelligence (AI) techniques constituted reliable and robust computer techniques for monitoring and modeling the photodegradation of organic pollutants from aqueous mediums by MgO-LaO-MMO heterostructure catalysts.