Machine learning-driven performance prediction of Z-scheme g-C3N4/SnS2 heterostructure photocatalyst for complete mineralization of indigo carmine and elucidation of degradation pathways

The extensive use of synthetic dyes in industry has raised environmental concerns due to their high toxicity and persistence in water. In this study, a Z-scheme g-C₃N₄/SnS₂ heterostructure was synthesized via a facile one-pot thermal decomposition method with varying SnS₂ loading. The nanocomposites were thoroughly characterized using XRD, FT-IR, DRS, PL, Raman, XPS, FE-SEM, and TEM to confirm their structural, optical, and morphological structures. The photocatalytic performance was evaluated for indigo carmine degradation under natural sunlight. The nanocomposite GS5 sample (5% SnS₂) showed the highest efficiency, achieving 100% dye removal and 74.1% mineralization (TOC) of 10 ppm dye within 30 min at 1 mg/mL catalyst dosage, and 88.67% removal at 50 ppm dye concentration. The photocatalytic performance of the g-C₃N₄/SnS₂ nanocomposites was evaluated by varying key parameters such as pH, catalyst dosage, dye concentration, and regeneration cycles. Structural and optical analyses confirmed the formation of a well-coupled Z-scheme heterojunction, promoting charge separation and reactive oxygen species generation. The degradation mechanism, supported by GC-MS and scavenger studies, highlighted the dominant role of superoxide radicals. Machine learning models, including Random Forest, ANN, SVM, and XGBoost, successfully predicted photocatalytic efficiency, with Random Forest showing the highest accuracy (R² = 0.9734, error = 6.24). This study highlights the importance of light-driven photocatalysis, efficient Z-scheme heterostructures for improved charge separation, and the limited reports using machine learning with g-C₃N₄/SnS₂. It demonstrates the potential of g-C₃N₄/SnS₂ as a solar-driven photocatalyst for degrading recalcitrant dyes and the role of machine learning in predicting real-world performance.