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 (X1, opt), 23.17 h (X2, opt), 0.67 M (X3, opt), CaCl2 (X4, 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 (kads: 0.10–0.67 m·h−1), desorption (kdes: 79–150 mmol·m−2·h−1), degradation (kdegr: 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.