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Removal Efficiency for Micro-Polystyrene in Water by the Oil-Based Ferrofluid Employ Response Surface Methodology
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Researchers investigated palm oil-based ferrofluid containing magnetite nanoparticles for removing micro-polystyrene particles from water, optimising six process variables using Plackett-Burman and central composite response surface designs. The optimised conditions yielded a removal efficiency of 91.09%, with the ferrofluid reusable for up to five cycles and achieving low relative standard deviation below 5%.
This research article presents a study on the potential use of oil-based ferrofluid for the efficient removal of microplastics from water. The targeted analyte, micro-polystyrene (micro-PS), was chosen along with palm oil as the carrier liquid. Fourier Transform Infrared (FTIR) analysis was conducted to identify the main peaks in the ferrofluid, including carboxyl group (1542 cm-1), C-H bonding (1022 cm-1), CH2 bonding (2941 cm-1), CH3 bonding (3461 cm-1), C=C bonding (1255 cm-1), and Fe-O (597.34 cm-1). A comprehensive investigation of the synergistic effect of six variables was performed: volume of oil (4-15 mL), weight of magnetite nanoparticles (0.1-0.2 g), stirring rate (132-468 rpm), contact time (3-12 min), pH value of water samples (pH 6-8), and effect on ionic strength (0-16 g/L). Response surface methodology, including 26-Plackett-Burman and 24-central composite design, were employed to establish the relationship between the variables. The optimum operational settings proposed by the model were as follows: volume of oil (14.6 mL), weight of magnetite nanoparticles (0.1 g), stirring rate (216 rpm), contact time (3.29 min), pH value of water samples (pH 6-6.5), and effect on ionic strength (16 g/L), resulting in a remarkable removal efficiency of 91.09 ± 0.99%. The method exhibited desirable figures of merit, including a low bias (%RSD) of below 5% and the ability to reuse the ferrofluids for up to five cycles. Additionally, an analytical greenness metric was employed to assess the environmental impact of the sample preparation process, with a green score of 0.69/1.0 (indicating a light green colour). Future work in this field could focus on the scalability of the developed method and its applicability to real-wastewater treatment.
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