Spectral-temporal autofluorescence analysis for rapid and label-free microplastic differentiation in aquatic environments.

Abstract

Although plastic products facilitate our life essentially, the resulting pollution increasingly harms the terrestrial and aquatic environment and our health. To mitigate this pollution and to establish effective recycling strategies, analytical methods to specifically detect different plastics are required. However, common analytical methods such as Raman, near-infrared (NIR), and Fourier transform infrared (FTIR) spectroscopy are constrained by their complex instrumentation, their signal absorption in water and/or their low signal intensities, resulting in long measurement times. In this work, we demonstrate an analytical method to overcome these limitations and detect different plastic materials based on their intrinsic autofluorescence. We investigated 22 different plastic materials and identified characteristic fingerprints in their spectral and temporal autofluorescence using a phasor-based analysis. With the spectral and temporal autofluorescence, we could identify different plastic materials with an accuracy of up to 87.9% and 83.1%, respectively. The accuracy can even be improved to 95.7%, when combining the analysis of individually measured spectral and temporal autofluorescence signals. These results showcase that using spectral and temporal autofluorescence signals is a viable approach for plastic detection and differentiation, particularly when combining spectral and temporal dimensions. The, here, demonstrated analytical method comprises the advantage of high signal intensities, working in the spectral water window and needing no labels. Consequently, it can be implemented into high-throughput methods for microplastic detection in liquid sample analysis such as flow cytometric, as demonstrated in this work. This has huge potential for environmental in-field monitoring which would support the effective mitigation of plastic pollution.

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