Photocatalytic and Photo-Fenton-like Degradation of Cationic Dyes Using SnFe2O4/g-C3N4 Under LED Irradiation: Optimization by RSM-BBD and Artificial Neural Networks (ANNs)

The development of heterostructures incorporating photocatalysts optimized for visible-light activity represents a major breakthrough in the field of environmental remediation research, offering innovative and sustainable solutions for environmental purification. This study explores the photocatalytic capabilities of a SnFe2O4/g-C3N4 heterojunction nanocomposite, successfully synthesized from graphitic carbon nitride (g-C3N4) and tin ferrate (SnFe2O4) and applied to the degradation of the cationic dye brilliant cresyl blue (BCB) in an aqueous solution. These two components are particularly attractive due to their low cost and ease of fabrication. Various characterization techniques, including XRD, FTIR, SEM, and TEM, were used to confirm the successful integration of SnFe2O4 and g-C3N4 phases in the synthesized catalysts. The photocatalytic and photo-Fenton-like activity of the heterojunction composites was evaluated by the degradation of brilliant cresyl blue under visible LED illumination. Compared to the pure components SnFe2O4 and g-C3N4, the SnFe2O4/g-C3N4 nanocomposite demonstrated a superior photocatalytic performance. Furthermore, the photo-Fenton-like performance of the composites is much higher than the photocatalytic performances. The significant improvement in photo-Fenton activity is attributed to the synergistic effect between SnFe2O4 and g-C3N4, as well as the efficient separation of photoexcited electron/hole pairs. The recyclability of the SnFe2O4/g-C3N4 composite toward BCB photo-Fenton like degradation was also shown. This study aimed to assess the modeling and optimization of photo-Fenton-like removal BCB using the SnFe2O4/g-C3N4 nanomaterial. The main parameters (photocatalyst dose, initial dye concentration, H2O2 volume, and reaction time) affecting this system were modeled by two approaches: a response surface methodology (RSM) based on a Box–Behnken design and artificial neural network (ANN). A comparison was made between the predictive accuracy of RSM for brilliant cresyl blue (BCB) removal and that of the artificial neural network (ANN) approach. Both methodologies provided satisfactory and comparable predictions, achieving R2 values of 0.97 for RSM and 0.99 for ANN.