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Synchrotron-based Spectromicroscopy for Microplastic Detection and Characterization
Summary
Researchers reviewed how synchrotron-based imaging techniques — which use powerful X-ray beams to see extremely fine details — can detect and chemically identify micro- and nanoplastics that conventional methods miss, including plastics absorbed into biological tissues. These high-resolution tools are still in early stages but show strong potential for mapping microplastic contamination at the nanoscale.
Microplastics are widely detected across diverse environmental media.However, the wide variability in particle size, physicochemical properties, combined with the complexity of environmental matrices, make it difficult to accurately assess their distribution or quantify their abundance.Conventional analytical techniques such as Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy are limited in their ability to detect nanoscale particles, whereas synchrotron-based techniques provide superior spatial resolution and sensitivity.In this study, researches using synchrotron radiation-based Fourier transform infrared spectroscopy (SR-FTIR), scanning transmission X-ray microscopy (STXM) coupled with near-edge X-ray absorption fine structure (NEXAFS), and X-ray fluorescence microscopy (XFM) to detect microplastics and characterize their chemical properties were introduced.In addition, the potential of using SR-FTIR for assessing microplastics absorbed into biological samples was suggested.SR-FTIR enables polymer identification and spatial mapping of microplastics by analyzing polymer-specific functional groups.STXM-NEXAFS distinguishes plastic types by probing carbon bonding environments through X-ray absorption spectra.Although XFM does not directly detect carbon-based polymers, it can trace microplastics by visualizing the distribution of metal additives incorporated into plastics.Synchrotron-based microplastic research is still in its early stages and ongoing advances in sample preparation, analytical techniques, and machine learning-based spectral interpretation are expected to facilitate the development of highly refined analytical platforms capable of comprehensively elucidating microplastic detection, distribution, and biological interactions across complex matrices.
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