Recent advances in g-CN-based photocatalysis for water treatment: Magnetic and floating photocatalysts, and applications of machine-learning techniques.

Over the past decade, there has been a substantial increase in research investigating the potential of graphitic carbon nitride (g-CN) for various environmental remediations. Renowned for its photocatalytic activity under visible light, g-CN offers a promising solution for treating water pollutants. However, traditional g-CN-based photocatalysts have inherent drawbacks, creating a disparity between laboratory efficacy and real-world applications. A primary practical challenge is their fine-powdered form, which hinders separation and recycling processes. A promising approach to address these challenges involves integrating magnetic or floating materials into conventional photocatalysts, a strategy gaining traction within the g-CN-based photocatalyst arena. Another emerging solution to enhance practical applications entails merging experimental results with contemporary computational methods. This synergy seeks to optimize the synthesis of more efficient photocatalysts and pinpoint optimal conditions for pollutant removal. While numerous review articles discuss the laboratory-based photocatalytic applications of g-CN-based materials, there is a conspicuous absence of comprehensive coverage regarding state-of-the-art research on improved g-CN-based photocatalysts for practical applications. This review fills this void, spotlighting three pivotal domains: magnetic g-CN photocatalysts, floating g-CN photocatalysts, and the application of machine learning to g-CN photocatalysis. Accompanied by a thorough analysis, this review also provides perspectives on future directions to enhance the efficacy of g-CN-based photocatalysts in water purification.