Smart Nanomaterials And AI-Enhanced Technologies For Environmental Separation And Sensing

Abstract

The escalating global environmental challenges, such as water contamination, air pollution, and resource depletion, demand innovative solutions for effective monitoring and remediation. Smart nanomaterials, with their unique physicochemical properties, combined with artificial intelligence (AI)-enhanced technologies, offer transformative potential for environmental separation and sensing. This paper explores the synergy between advanced nanomaterials—such as graphene-based structures, carbon dots, and metal-organic frameworks—and AI-driven techniques, including machine learning and predictive modeling, to optimize pollutant detection, separation processes, and environmental monitoring. By leveraging AI’s data-driven insights, these technologies enhance the sensitivity, selectivity, and efficiency of nanomaterial-based sensors and separation systems. The review highlights recent advancements, challenges, and future prospects, emphasizing sustainable applications in water purification, gas separation, and real-time environmental sensing. This interdisciplinary approach underscores the critical role of smart nanomaterials and AI in achieving a cleaner, more sustainable environment.