Raman Spectroscopy

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The advantages of machine-learning methods have been widely explored in Raman spectroscopy analysis. In this study, a lightweight network model for mineral analysis based on Raman spectral feature visualization is proposed. The model, called the fire module convolutional neural network (FMCNN), was based on a convolutional neural network, and a fire-module was introduced to increase the width of the network, while also ensuring fewer trainable parameters in the network and reducing the model’s computational complexity. The visualization process is based on a deconvolution network, which maps the features of the middle layer back to the feature space. While fully exploring the features of the Raman spectral data, it also transparently displays the neural network feature extraction results. Experiments show that the classification accuracy of the model reaches 0.988. This method can accurately classify Raman spectra of minerals with less reliance on human participation. Combined with the analysis of the results of feature visualization, our method has high reliability and good application prospects in mineral classification.

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In the past decades, we have witnessed the evolution of imaging technologies based on vibrational spectroscopy. In particular, the technical developments in Raman, coherent anti-Stokes Raman spectroscopy (CARS), and stimulated Raman scattering (SRS) microscopy allow researchers to gain new insights in biological, medical, and pharmaceutical studies.

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Portable spectroscopic instruments have not had significant visibility within the scientific community compared with, for instance, the current generation of high-performance laboratory mass spectrometers.

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Of the 78 million tons of plastic packaging manufactured every year, approximately one-third ends up in the ocean, the air, and most foods and beverages. To monitor the proliferation of these plastics, an ultrasonic capture method is demonstrated that produces a 1500-fold enhancement of Raman signals of microplastics in water.

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Raman spectroscopy is proving to be a powerful technique for characterizing the structural and morphological properties of nanopowders. Specifically, Raman spectroscopy can provide details of the grain size and thickness of titanium dioxide (TiO2) nanopowder films. These measured film properties affect the efficiency of photovoltaic devices, such as solar cells, and also the effectiveness of nanopowders in catalysis applications.