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Détection des microplastiques dans les systèmes à fibres creuses par fluorescence et fluorimétrie in situ
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This study developed a combined Nile Red fluorescence staining and in-situ fluorometric method for real-time tracking of microplastics in hollow-fiber membrane filtration systems, revealing preferential particle accumulation in central fiber regions. The approach provides a fast, reproducible tool for monitoring microplastic dynamics during water treatment processes.
The detection and behavior of microplastics (MP) in membrane filtration systems remain key challenges in water treatment and filter media characterization. This study introduces an integrated experimental approach combining hydrophobic Nile Red/n-heptane (NR-H) staining and in-situ fluorometric detection using a Cyclops 7F sensor, applied to model microplastic suspensions (polyethylene, polystyrene, polycarbonate, vinyle polychloride, vinyle polyalcohol) Suspensions were prepared in natural saline water (La Rochelle, France) to simulate representative filtration and dye conditions of real water, doped with MP. Fluorescence spectra recorded with a SAFAS Xenius spectrofluorometer (λ_exc = 500 nm) displayed emission peaks between 590 and 650 nm, matching the Cyclops 7F detection range. Experiments conducted on a polyethersulfone (PES) hollow-fiber module enabled real-time tracking of fluorescence during filtration and microplastics particles concentration along the membrane. Axial intensity profiles revealed preferential particle accumulation in the central fiber region, attributed to the combined effects of pressure-driven flow (ΔP) and potential electrostatic interactions between polymer surfaces and the membrane wall. Micro Fourier Transformation Infra-Red spectroscopy (μFTIR) analyses confirmed the polymeric nature of the fluorescent particles. This combined fluorescence–μFTIR method provides a fast, reproducible and operational tool for characterizing MP particles dynamics and fouling behavior in membrane systems, paving the way for opticalbased process monitoring and quantification.
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