Investigation of Fe-Doped Graphitic Carbon Nitride-Silver Tungstate as a Ternary Visible Light Active Photocatalyst

The rapid population growth and economic development have largely contributed to environmental pollution. Various advanced oxidation processes have been used as the most viable solution for the reduction of recalcitrant pollutants and wastewater treatment. Heterogeneous photocatalysis is one of the broadly used technologies for wastewater treatment among all advanced oxidation processes. Graphitic carbon nitride alone or in combination with various other semiconductor metal oxide materials acts as a competent visible light active photocatalyst for the removal of recalcitrant organic pollutants from wastewater. Rational designing of an environment-friendly photocatalyst through a facile synthetic approach encounters various challenges in photocatalytic technologies dealing with semiconductor metal oxides. Doping in g-C3N4 and subsequent coupling with metal oxides have shown remarkable enhancement in the photodegradation activity of g-C3N4-based nanocomposites owing to the modulation in g-C3N4 bandgap structuring and surface area. In the current study, a novel ternary Fe-doped g-C3N4/Ag2WO4 visible light active photocatalyst was fabricated through an ultrasonic-assisted facile hydrothermal method. Characterization analysis included SEM analysis, FTIR, XRD, XPS, and UV-Visible techniques to elucidate the morphology and chemical structuring of the as-prepared heterostructure. The bandgap energies were assessed using the Tauc plot. The ternary nanocomposite (Fe-CN-AW) showed increased photodegradation efficiency (97%) within 120 minutes, at optimal conditions of pH. 8, catalyst dose. 50 mg/100 ml, an initial RhB concentration of 10 ppm, and oxidant dose 5mM under sunlight irradiation. The enhanced photodegradation of rhodamine B dye by ternary Fe-CN-AW was credited to multielectron transfer pathways due to insertion of a Fe dopant in graphitic carbon nitride and subsequent coupling with silver tungstate. The data were statistically assessed by the response surface methodology.