Identifying plastics with photoluminescence spectroscopy and machine learning.

A quantitative understanding of the worldwide plastics distribution is required not only to assess the extent and possible impact of plastic litter on the environment but also to identify possible counter measures. A systematic collection of data characterizing amount and composition of plastics has to be based on two crucial components: (i) An experimental approach that is simple enough to be accessible worldwide and sensible enough to capture the diversity of plastics; (ii) An analysis pipeline that is able to extract the relevant parameters from the vast amount of experimental data. In this study, we demonstrate that such an approach could be realized by a combination of photoluminescence spectroscopy and a machine learning-based theoretical analysis. We show that appropriate combinations of classifiers with dimensional reduction algorithms are able to identify specific material properties from the spectroscopic data. The best combination is based on an unsupervised learning technique making our approach robust to alternations of the input data.

Lensless digital holographic microscopy as an efficient method to monitor enzymatic plastic degradation.

A big challenge of the 21st century is to cope with the huge amounts of plastic waste on Earth. Especially the oceans are heavily polluted with plastics. To counteract this issue, biological (enzymatic) plastic decomposition is increasingly gaining attention. Recently it was shown that polyethylene terephthalate (PET) can be degraded in a saltwater-based environment using bacterial PETase produced by a marine diatom. At moderate temperatures, plastic biodegradation is slow and requires sensitive methods for detection, at least at initial stages. However, conventional methods for verifying the plastic degradation are either complex, expensive, time-consuming or they interfere with the degradation process. Here, we adapt lensless digital holographic microscopy (LDHM) as a new application for efficiently monitoring enzymatic degradation of a PET glycol copolymer (PETG). LDHM is a cost-effective, compact and sensitive optical method. We demonstrate enzymatic PETG degradation over a time course of 43 days employing numerical analysis of LDHM images.

Exploring the potential of photoluminescence spectroscopy in combination with Nile Red staining for microplastic detection.

The significant amount of plastic litter in the form of microplastics (size <5 mm) is garnering attention owing to its potential threat to marine life. Reliable, cost- and time-efficient analysis methods for monitoring microplastic abundance globally are still missing. Several studies proposed a fast detection method by binding the solvatochromic dye Nile Red on the surface of microplastics and using fluorescence microscopy for their detection. All the staining approaches reported so far differ in terms of Nile Red concentration, solvents, and staining procedure. Here, we compare the staining protocols published prior to 2019 and propose an optimized staining protocol. Furthermore, we explore the potential of Nile Red staining in combination with photoluminescence spectroscopy to identify the polymer type and to distinguish plastics from non-plastics.