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.

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.

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.