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Cationic Nanoparticle Networks (CNNs) with Remarkably Efficient, Simultaneous Adsorption of Microplastics and PFAS
Summary
Researchers developed a new type of polymer-based material called cationic nanoparticle networks that can simultaneously remove both microplastics and PFAS (so-called forever chemicals) from water. The best-performing material achieved record-high microplastic removal capacity and worked effectively even in seawater and at varying pH levels. The technology offers a promising dual-purpose approach for addressing two of the most persistent water pollutants at the same time.
Of the past decade, micro/nanoplastics (MP/NP) and per- and polyfluoroalkyl substances (PFAS) have become two of the most pervasive persistent organic pollutants leading to significant accumulation within waterways. Various sorbent materials have been evaluated for PFAS and MP/NP removal, but their simultaneous removal has rarely been explored. Herein, we report a library of polymer-based, cationic nanoparticle networks (CNN) with systematic variation in surface charge density, polymer molecular weight, and nanoparticle size for the removal of anionic MP/NP and PFAS from aqueous solutions. These materials are synthesized in three, one-pot steps starting with polymerization-induced self-assembly (PISA) followed by rapid photocuring and quaternary ammonium salt formation resulting in 3D networks consisting solely of cationic polymer nanoparticles. Our best performing CNN material demonstrated record-high MP removal capacities of Qmax = 1865 mg/g and KF = 58.0 (mg/g)(L/mg)1/n based on Langmuir and Freundlich isotherm model estimations, respectively. Furthermore, the CNN materials demonstrated efficient removal of NPs and MPs in complex water media, such as in seawater and at different pH values, demonstrating the overall material applicability. Finally, simultaneous and efficient removal of MPs and perfluorooctanoic acid (PFOA) was accomplished with similar Qmax (MP) = 478.4 mg/g and Qmax (PFOA) = 134.6 mg/g allowing for dual use.