Multiscale modeling advances in MOF-based membranes for heavy metals separation from aqueous solutions

Abstract

The pollution of the world’s water sources by toxic heavy metals represents a significant risk to human health and aquatic ecosystems. Membrane separation technology has emerged as an efficient strategy due to its high separation efficiency, ease of operation, and compact design. Among advanced materials, metal–organic frameworks (MOFs) have demonstrated outstanding potential to enhance membrane performance thanks to their high porosity, tunable functionality, and chemical stability. Recent advances in computational modeling enable the accurate design and optimization of MOF-based membranes for the selective removal of heavy metals. Through multiscale simulation approaches—including molecular dynamics (MD), density functional theory (DFT), artificial intelligence (AI), Coarse-Grained (CG) Simulations, and computational fluid dynamics (CFD)—researchers can predict adsorption properties, structural stability, and recyclability of MOFs under diverse conditions. This review presents a comprehensive summary of these modeling strategies, emphasizing their role in understanding structure–performance relationships and in guiding the rational design of next-generation MOF membranes for sustainable wastewater treatment.