Photocatalysis has been shown to be a promising and ecofriendly approach to the removal of contaminants of emerging concern (CECs). However, preventing the recombination of photogenerated charge carriers and achievement of suitable band edge positions are still major challenges to ensuring better performance. Herein, we report the preparation of surface-decorated BiVO₄ with both a noble metal (Ag) and transition metal (Fe). The structural, morphological, and semiconducting features of the material were examined employing various techniques (XRD, SEM, UV-DRS, PL, and photoelectrochemical tests). The band gap of surface-modified BiVO₄ is slightly narrower compared to pristine material, which is further validated by HOMO-LUMO gaps obtained through theoretical modeling approaches. The recombination of photogenerated charges was successfully reduced in the case of Ag–Fe–BiVO₄, as proven by lower PL intensity and increased current density. The comparative photocatalytic degradation of the CECs ciprofloxacin (CIP) and perfluorooctanoic acid (PFOA) was conducted employing pristine BiVO₄ and its two surface-modified analogues (Ag–BiVO₄, and Ag–Fe–BiVO₄) under solar light. Ag–Fe–BiVO₄ was shown to be the most efficient; however, its effectiveness differed depending on CEC type. Under the same process conditions, degradation of CIP reached 93.9%, while PFOA was degraded only partially (22.9%).

Contaminants of emerging concern (CECs), including pharmaceuticals and perfluorinated compounds, pose a growing threat to water quality due to their persistence and resistance to conventional treatment methods. In this context, photocatalytic processes capable of promoting both oxidative and reductive transformations have attracted increasing attention. This study explores the synthesis and performance of a SnS₂-TiO₂ heterojunction photocatalyst, designed to facilitate such reactions under solar and UV-A light. The composite was synthesized via the hydrothermal method and thoroughly characterized for its morphological, structural, surface, and semiconducting properties. The results confirmed the formation of a type-II heterojunction with improved visible-light absorption and suppressed charge recombination. Photoelectrochemical measurements indicated enhanced charge separation and favorable band-edge alignment for reductive processes. Photocatalytic experiments with amoxicillin (AMX) and perfluorooctanoic acid (PFOA) revealed distinct degradation behaviors: AMX was predominantly degraded via superoxide-mediated reductive pathways, whereas PFOA exhibited limited transformation, likely proceeding via a combination of oxidative and reductive mechanisms. While overall removal efficiencies were moderate, this study highlights the role of band structure engineering and heterojunction design in tailoring photocatalytic behavior. The SnS₂-TiO₂ system serves as a promising platform for further development of composite materials to address the challenge of CECs in water treatment.

Water pollution caused by micropollutants has been a global issue for decades, prompting the scientific community and industry professionals to develop new and effective wastewater treatment methods. Understanding the interactions of these compounds in real water samples is particularly challenging, as they contain complex mixtures that may alter the mechanism of action and toxic effects of these compounds on aquatic organisms. To address such challenges, computational methods and mathematical models have been developed to complement experimental research and predict the toxicity of micropollutant mixtures in water. This narrative review summarises current literature on such mathematical models, including the concentration addition (CA), independent action model (IA), and their combinations to predict the toxicity of mixtures involving pharmaceuticals, pesticides, and perfluorinated compounds. We also discuss computational methods like quantitative structure-activity relationship (QSAR) modelling and machine learning (ML). While the CA and IA models provide basic frameworks for predicting toxicity in chemical mixtures, their practical application is often limited by the assumption of additivity and by the complexity of real water mixtures. QSAR and ML approaches, though promising, face challenges such as limited data availability, overfitting, and difficult interpretation. Future research should focus on enhancing model robustness, incorporating mechanistic data, and developing hybrid approaches that integrate experimental and computational methods to improve the reliability of toxicity predictions for complex environmental mixtures.