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Carbon quantum dots from waste and biomass for water treatment: A review on nanomaterials AI-enhanced synthesis and characterization
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Scientists are turning food scraps, shells, and plant waste into tiny particles called carbon quantum dots that can help clean up drinking water — pulling out harmful stuff like antibiotics, dyes, heavy metals, PFAS ("forever chemicals"), and even microplastics. This review rounds up existing research showing these particles can remove over 90% of certain pollutants in about 2 hours, and explores how AI could speed up finding the best recipes. It's early-stage science, but if it pans out, it could offer a cheaper, more sustainable way to make our water safer to drink.
In the face of escalating global water pollution and the increasing demand for even higher efficiency in water treatment systems, carbon quantum dots (CQDs) have emerged as promising nanomaterials for advanced wastewater remediation. This review provides a comprehensive synthesis of the recent progress in the production of CQDs from biomass and waste-derived precursors, emphasizing their dual role in photocatalysis and adsorption. A particular focus is devoted to the diverse array of natural feedstocks, ranging from agricultural residues and marine by-products to food and cellulose wastes, highlighting their physicochemical advantages and sustainability potential. The current work dissects synthesis techniques, including hydrothermal, pyrolytic, and microwave-assisted routes, analyzing the influence of key reaction parameters (temperature, residence time and carbonization medium) on the morphology, quantum yield, and surface functionalities of CQDs. Furthermore, the mechanisms by which CQDs facilitate pollutant degradation via radical generation, electron shuttling, and selective adsorption are described, with demonstrated efficiency against antibiotics, dyes, polyfluoroalkyl substances (PFAS), heavy metals, and microplastics reaching, for example, 97% ciprofloxacin degradation within 150 min and 90% methylene blue removal within 120 min, highlighting their potential in advanced water treatment applications. The review also examines emerging efforts to use artificial intelligence (AI) and machine learning (ML) to relate synthesis conditions to CQD properties and to reduce experimental trial-and-error. At the same time, current applications remain constrained by limited datasets, inconsistent reporting practices, and insufficient benchmarking across studies.
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