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

Key Points

• Performance prediction of a photocatalyst for indigo carmine mineralization shows significant promise.
• A machine learning model reveals the potential to enhance degradation pathways of organic dyes like indigo carmine.
• Investigation utilizes a Z-scheme g-C3N4/SnS2 heterostructure to improve photocatalytic activity.
• Insights into mineralization pathways provide a basis for environmental remediation strategies.