Machine Learning-Driven Advances in Metal-Organic Framework Nanomaterials for Wastewater Treatment: Developments and Challenges

Abstract

ABSTRACT Researchers engaged in tackling the problem of removing contaminants from wastewaters are much relieved through the development of metal organic frameworks (MOFs) as adsorbents. The use of MOFs to sequester organic pollutants, such as dyes and emerging toxins, within wastewaters has increased recently. These organic contaminants have been thoroughly examined and elaborated as it is separated, adsorbed, and reversed within the liquid phase. In addition to a MOF overview, this review highlights some insights into the features that render MOFs distinctive as adsorbents, including their synthetic approaches for improved performance, structural characteristics, MOFs water stability, and corresponding ability to separate organic pollutants. It focuses also on applications of machine learning in the removal and degradation of pollutants such as organic pollutants, dyes, pharmaceutical, pesticides, micropollutants, antibiotics, and other industrial chemicals onto MOF-based adsorbents. It also provides an overview of the various organic pollutants, their hazardous environmental effects, and the use of numerous MOFs to eradicate it. Finally, a brief discussion is made of the difficulties and chances for improving MOF performance in the future to support more industrial applications along with challenges, limitation and opportunity of machine learning in wastewater remediation using MOF nanomaterial.