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

Crossref DOI link: https://doi.org/10.1038/s41598-026-37528-5

Published Online: 2026-01-27

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Gaur, Rama

Parmar, Helly

Patel, Jinal

Mhaske, Prajakta

Pathak, Gunjan

Taneja, Neha

Tyagi, Inderjeet

Ali Mohsin, Mohammed E.

Mousa, Suleiman

Shahabuddin, Syed
Funding

Funding for this research was provided by:

Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research at King Faisal University (KFU253313)
License Information

Text and Data Mining valid from 2026-01-27

Accepted Manuscript valid from 2026-01-27
More Information

Article History

Received: 8 October 2025

Accepted: 22 January 2026

First Online: 27 January 2026

Declarations

:

: The authors declare no competing interests.

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