Next-generation nanomaterials for environmental remediation: smart design, hybrid materials and sustainable use

Reviewartikel, 2026

Environmental contamination has increased steadily over recent decades due to industrialization, urban expansion, intensive agriculture, and improper waste management. As a result, a wide range of pollutants, including per- and polyfluoroalkyl substances (PFAS), microplastics, pharmaceutical residues, endocrine-disrupting compounds, and heavy metals are now frequently detected in water, soil, and sediment systems worldwide. Many of these contaminants are chemically stable, persist for long periods in the environment, and can accumulate in living organisms, posing significant toxicological and ecological risks and making their removal particularly challenging. Engineered nanomaterials have emerged as promising tools for pollutant removal because of their tunable surface chemistry, and ability to interact with contaminants through multiple mechanisms. This review examines recent advances in eco-engineered nanomaterials for environmental remediation, with particular attention to green strategies, major material classes and their underlying removal mechanisms. Across the studies discussed, adsorption-based and hybrid systems frequently report high removal efficiencies for metals and dyes under controlled conditions, while framework-based materials show improved selectivity toward persistent pollutants (including PFAS) through combined electrostatic, hydrophobic, and hydrogen-bonding interactions. Photocatalytic and redox-active systems are highlighted for accelerating the degradation of recalcitrant organics through reactive oxygen species-mediated pathways. Recoverable designs, including magnetic and scaffold-immobilized composites, are also emphasized because they are often reported to retain substantial performance over multiple reuse cycles. Sustainability and deployment challenges are also discussed, including life-cycle assessment, material reuse, environmental fate, toxicity risks, and data-driven strategies for design and optimization.