Photocatalytic degradation of organic pollutants by porphyrin metal-organic framework: Structural engineering, design strategies, and environmental applications

As the economy and society evolve, there is a marked increase in pollutant production. This escalation coincides with heightened environmental quality expectations from the populace, necessitating urgent solutions to numerous environmental issues related to organic pollutants. Photocatalysis emerges as a sustainable and eco-friendly approach with significant potential for organic pollutant degradation. Porphyrin metal-organic frameworks (PMOFs) stand out as photocatalysts due to their modifiable structures, expansive specific surface areas, optimal band gaps, and commendable catalytic performance, positioning them as promising agents for the efficient photocatalytic degradation of organic contaminants. This paper commences by delineating the fundamental structure of PMOF materials and their photochemical mechanisms in catalysing organic pollutant degradation. Subsequently, recent advancements in the synthesis strategies for porous PMOFs have been reviewed, focusing on approaches that enhance photocatalytic efficiency and expand the range of their applications. These strategies encompass the construction of heterojunctions, introduction of mixed ligands, establishment of photo-Fenton (like) catalytic systems, implementation of defect engineering, regulation of two-dimensional material formation, loading of PMOF, and doping with magnetic materials. This review also highlights the key challenges that must be addressed to facilitate the successful implementation of PMOFs in environmental engineering applications. • The structure and photochemical mechanisms of PMOFs in degrading organic pollutants are explained. • Recent progress in synthesizing porous PMOFs is reviewed. • The relationship between PMOF structure, mechanisms, preparation and performance is emphasized. • The research path of PMOFs and their role in photocatalytic degradation of organic pollutants are explored. • The key challenges (scalability, environmental risks, and AI - guided design) are emphasized.