Experimental data-driven and phenomenological modeling approaches targeting the enhancement of CaTiO₃ photocatalytic efficiency

Optimization based on mathematical models has received growing attention in materials science. The first part of the work aims to optimize the photocatalytic activity of CaTiO₃ for rhodamine B (RhB) degradation under UV-A irradiation, based on two developed mathematical models. Thirty hydrothermal syntheses of CaTiO₃ were carried out according to the Box-Behnken design, considering synthesis temperature (X₁), duration (X₂), and concentration of shaping agent (X₃) as input variables for two different Ca²⁺ sources: Ca(NO₃)₂ and CaCl₂ (X₄). The conversion of the studied pollutant after 4 h was situated in the range of 20–80%. Second-order regression and feedforward backpropagation artificial neural network models were developed, considering the synthesis conditions (X₁, X₂, X₃, X₄) as input and the conversion as output variables. The proposed model-based methodology for the optimization of CaTiO₃ photocatalytic efficiency finally directed to the experimentally attained value of 96% for 200 °C (X_{1, opt}), 23.17 h (X_{2, opt}), 0.67 M (X_{3, opt}), CaCl₂ (X_{4, opt}). Furthermore, in the second part of the study, the morphological, structural, textural, and optical properties of selected CaTiO₃ samples were investigated via scanning electron microscopy, X-ray diffractometry, N₂ sorption, and diffuse reflectance spectroscopy. Finally, the kinetic parameters for adsorption (k_ads: 0.10–0.67 m·h⁻¹), desorption (k_des: 79–150 mmol·m⁻²·h⁻¹), degradation (k_degr: 0.001–0.010 mmol·m^−2(1−α)·W^−α·h⁻¹), and intensity exponent (α: 0.54–0.55) were fitted using an optimization procedure, considering the experimentally determined and model-predicted apparent reaction rate constants. The obtained kinetic parameters were correlated with the specific surface area of the catalysts and the conversion of RhB.