Synergistic Integration Of Machine Learning And Advanced Nanomaterials For The Water-Energy Nexus: A Critical Review

Abstract

The convergence of advanced nanomaterials and artificial intelligence (AI) holds the promise of revolutionizing efforts to tackle water scarcity and energy crises across the globe. Conventional methods of wastewater treatment and renewable energy production have come a long way, but they are hindered by trial-and-error methods, scalability challenges, and complicated optimization needs. This review article critically summarizes the recent developments of MXenes, metal-organic frameworks (MOFs), photocatalytic heterojunctions, polymer composites and their incorporation with machine learning (ML) paradigms. A key research need: the need for unified, data-driven frameworks that can effortlessly connect nanomaterials design to macro process optimization. The novelty of this work is a comprehensive examination of the potential for using predictive algorithms (such as Artificial Neural Networks, XGBoost, Support Vector Regression), in conjunction with experimental materials science, to enable the deployment of decentralized desalination, synergistic pathogen/dye degradation and hybrid renewable energy systems.