AI-Driven Modeling and Statistical Assessment of AgO/ZnO/g-C3N4 Photocatalysts on Wastewater Treatment: Impact of UV-Visible Light

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dc.authorid0000-0003-4577-028X
dc.contributor.authorYoney, Busra
dc.contributor.authorAras, Omur
dc.date.accessioned2026-02-08T15:14:50Z
dc.date.available2026-02-08T15:14:50Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractMinimizing the environmental impact of dye-laden wastewater, remains a critical challenge with high economic implications. This study focuses on the development and optimization of g-C3N4-based photocatalysts doped with AgO and ZnO at varying Zn (0%-100%) and Ag (0%-2.5%) loadings. Catalysts were applied at four dosages (0.03-0.12 g/100 mL), and their methylene blue degradation efficiencies were evaluated over time intervals up to 3 h. Photocatalysts were synthesized using both conventional and ultrasound-assisted (US-assisted) co-precipitation methods. The US-assisted synthesis yielded improved morphology and dispersion, as evidenced by SEM-EDS and XRD analyses, and enhanced photocatalytic performance. Experimental data were used to train three AI models; artificial neural network (ANN), support vector regression (SVR), and Random forest (RF). SVR exhibited the highest predictive accuracy (R-2 = 0.9854, RMSE = 0.0401), while ANN and RF also showed strong performance (R-2 approximate to 0.980). Model robustness was validated through residual analysis and statistical tests. To assess the influence of input variables, one-way and multi-factor Type II ANOVA were conducted. Zn and Ag content, catalyst dosage, and reaction time were all statistically significant (p < 0.05), with US treatment and Zn loading having the most dominant effects. Ag's contribution was also significant but more composition-dependent.
dc.identifier.doi10.1002/slct.202501947
dc.identifier.issn2365-6549
dc.identifier.issue36
dc.identifier.scopus2-s2.0-105016814538
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1002/slct.202501947
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5465
dc.identifier.volume10
dc.identifier.wosWOS:001578038700001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley-V C H Verlag Gmbh
dc.relation.ispartofChemistryselect
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectAI modeling
dc.subjectANOVA
dc.subjectGraphitic carbon nitride
dc.subjectLarge data set
dc.subjectPhotocatalyst
dc.subjectUV
dc.subjectVisible light
dc.titleAI-Driven Modeling and Statistical Assessment of AgO/ZnO/g-C3N4 Photocatalysts on Wastewater Treatment: Impact of UV-Visible Light
dc.typeArticle

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