Metal organic frameworks-based adsorbents for environmental remediation: A focus on persistent micropollutants

Access to clean water is of essential prominence for the well-being of living beings, and it is crucial due to the significant impact of globalization in polluting our water bodies. Consequently, securing access to clean water becomes necessary for a range of essential uses, such as drinking, hygiene, and other vital needs. In the pursuit of future security and ecological equilibrium, prioritizing the removal of harmful contaminants from water resources is crucial. The adsorptive removal of toxic pollutants using porous adsorbents stands out as a highly promising approach for water decontamination. Metal–organic frameworks (MOFs) incorporated composites have garnered considerable attention in scientific investigations, particularly in the context of eliminating unwanted toxic contaminants from aqueous environments. This review thoroughly surveys the literature published from 2010 to present and highlights the main findings related to the adsorption and removal of persistent micropollutants using MOF-based adsorbents. MOFs stand out as highly effective adsorbents, owing to their expansive surface area, customizable pores, and abundant active sites, rendering them exceptional candidates for water and wastewater applications. The diverse interactions, encompassing π − π , acid-base, electrostatic, and H-bonding, drive the adsorption of pollutants on various MOF surfaces. Simultaneously, the removal of contaminants by MOFs is contingent upon factors such as pH, temperature, and initial concentration. Innovative MOF composites, integrating MOFs with diverse materials like magnetic particles, assorted polymers, and graphene oxide, have been formulated to augment the metal adsorption capabilities of MOFs. In this literature review, a comprehensive summary of the current utilization of MOF-based composites, renowned for their exceptional adsorption capabilities, is provided. The synthesis techniques and applications of these composites in eliminating persistent micropollutants are further discussed. Artificial intelligence (AI) and machine learning (ML) integration into MOF research are one of key recommendations to enhance discovery pace, reduce production cost, and sustain the rational development of high-efficiency MOF-based adsorbents able to target diverse persistent micropollutants in complex environment.