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Multi-Parameter Analysis of Nanoplastics in Flow: Taking Advantage of High Sensitivity and Time Resolution Enabled by Stimulated Raman Scattering
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Stimulated Raman scattering was demonstrated for the first time as a method to detect and characterize nanoplastic particles in flow, simultaneously measuring size, chemical composition, and concentration with high sensitivity.
Here, we demonstrate the detection of nanoplastics (NPLs) in flow with stimulated Raman scattering (SRS) for the first time. NPLs (plastic particles <1000 nm) have recently been detected in different environmental samples and personal care products. However, their characterization is still an analytical challenge. Multiple parameters, including size, chemical composition, and concentration (particle number and mass), need to be determined. In an earlier paper, online field flow fractionation (FFF)-Raman analysis with optical trapping was shown to be a promising tool for the detection of particles in this size range. SRS, which is based on the enhancement of a vibrational transition by the matching energy difference of two laser beams, would allow for much more sensitive detection and, hence, much shorter acquisition times compared to spontaneous Raman microspectroscopy (RM). Here, we show the applicability of SRS for the flow-based analysis of individual, untrapped NPLs. It was possible to detect polyethylene (PE), polystyrene (PS), and poly(methyl methacrylate) (PMMA) beads with diameters of 100-5000 nm. The high time resolution of 60.5 μs allows us to detect individual signals per particle and to correlate the number of detected particles to the injected mass concentration. Furthermore, due to the high time resolution, optically trapped beads could be distinguished from untrapped beads by their peak shapes. The SRS wavenumber settings add chemical selectivity to the measurement. Whereas optical trapping is necessary for the flow-based detection of particles by spontaneous RM, the current study demonstrates that SRS can detect particles in a flow without trapping. Additionally, the mean particle size could be estimated using the mean width (duration) and intensity of the SRS signals.
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