Publications by authors named "Dominik J Winterauer"
Article Synopsis
- A random decision forest model is developed to quickly identify the most common types of microplastics based on their Fourier-transform infrared spectra.
- Input data is optimized by selecting key wavenumbers with a machine learning classifier, enhancing speed and efficiency.
- The model's accuracy is validated against controlled samples, though it may not be as reliable for diverse environmental samples due to variation in materials.
This work introduces hyper-resolution (HyRes), a numerical approach for spatial resolution enhancement that combines hyperspectral unmixing and super-resolution image restoration (SRIR). HyRes yields a substantial increase in spatial resolution of Raman spectroscopy while simultaneously preserving the undistorted spectral information. The resolving power of this technique is demonstrated on Raman spectroscopic data from a polymer nanowire sample.
View Article and Find Full Text PDFRaman microscopy is a valuable tool for detecting physical and chemical properties of a sample material. When probing nanomaterials or nanocomposites the spatial resolution of Raman microscopy is not always adequate as it is limited by the optical diffraction limit. Numerical post-processing with super-resolution algorithms provides a means to enhance resolution and can be straightforwardly applied.
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