The increasing presence of emerging contaminants (ECs) has attracted considerable attention due to their potential harm to human health and ecosystems. Graphitic carbon nitride (g-C3N4), a semiconductor devoid of metals, stands out due to its distinctive optical properties and strong resistance to chemical degradation, which holds significant promise in the photocatalytic degradation of ECs. However, the inherent limitations of g-C3N4, such as its reduced specific surface area and the swift recombination of photogenerated electron-hole pairs, have prompted extensive research on modification strategies to enhance its photocatalytic performance. Current research on g-C3N4-based materials is often constrained in scope, with most reviews focusing solely on modification strategies or its application in degrading a single category of emerging contaminants (ECs). In this review, a systematic overview of synthesis methods and advanced modification strategies for g-C3N4-based materials is discussed, highlighting their recent advances in the photocatalytic degradation of various ECs using g-C3N4-based materials, which underscores their potential for environmental remediation. Moreover, this article critically examines the current challenges and outlines future research directions, with particular emphasis on integrating artificial intelligence and machine learning to accelerate the development of g-C3N4-based photocatalysts and optimize degradation processes, thereby promoting their efficient application in the photocatalytic degradation of ECs.
Xu et al. (Fri,) studied this question.