Journal of Raman Spectroscopy最新文献

This study examines the effects of varying concentrations of zinc oxide nanoparticles (ZnO NPs) on the biochemical profile of mung plants, utilizing Raman and ultraviolet–visible (UV–Vis) spectroscopy combined with machine learning algorithms for data analysis. Mung plants, developed in the lab under optimized growth conditions, were exposed to different ZnO NPs concentrations (0.2, 0.4, 0.6, 0.8, 1.0, 1.2, and 1.4 mM, particle size < 30 nm). UV–Vis measurements show that concentrations of photosynthetic pigments declined with higher ZnO NPs treatment, implying a decrease in photosynthetic efficiency due to oxidative stress. The analysis of acquired Raman spectra shows disruptions in key biochemicals, including carotenoids, pectin, lignin, cellulose, protein, and aliphatics on exposure to nanoparticles, indicating effects on the metabolic processes of plants. To assess these changes, various machine learning algorithms were employed, including unsupervised methods (k-means clustering, density-based spatial clustering of applications with noise, agglomerative clustering, and principal component analysis) and supervised methods (support vector machine, random forest, k-nearest neighbor, decision tree, logistic regression, gradient boosting, and linear discriminant analysis). The support vector machine and random forest models achieved the highest classification accuracy, precision, recall, and f1 scores, effectively differentiating between NPs-induced biochemical changes. Additionally, unsupervised algorithms revealed distinct clustering patterns, aiding in the identification of NPs treatment effects on plants. These findings demonstrate the potential of integrating confocal micro-Raman and UV–Vis spectroscopy with machine learning as a rapid, early, nondestructive, and robust tool for providing valuable insights for sustainable agricultural practices.

The room temperature (RT) single-crystal polarized Raman spectra of 6-bromopyridine-2-carbaldehyde (BPCA) have been obtained and interpreted based on fully periodic DFT calculations for the P2₁/a (No. 14; monoclinic) crystal of the compound. The calculations, performed with the CRYSTAL software employing the Becke three parameters Lee, Yang, and Parr (B3LYP) functional and the polarization-consistent triple-zeta valence plus polarization basis set (pob-TZVP), were able to reproduce very well the experimental data, thus allowing a detailed assignment of the Raman active A_g and B_g modes to individual bands. The isotropic non-polarized Raman spectrum of BPCA was also calculated and shown to agree very well with the corresponding experimental Raman spectrum. Finally, the RT infrared spectrum of BPCA was also revisited in light of the performed periodic calculations, improving on previously reported interpretation based on extrapolation of the interpretation of the spectra obtained for the isolated molecule of the compound to the crystalline phase. In this crystalline system, intermolecular interactions exert only a minor influence on the intramolecular vibrational potential. As such, this study also serves as a benchmark for the employed computational approach, demonstrating its ability to capture the effects of both crystallographic periodicity and symmetry on the polarization features of vibrational spectra.

The accurate classification of ink samples, which are inherently complex mixtures, is critical for verifying the authenticity of historical artworks and financial documents such as contracts, insurance claims, wills, and tax records. Herein, one-dimensional convolutional neural network (1D CNN) models were developed for Raman spectral data acquired from red stamp ink pastes. The 1D CNN model trained on the first-derivative Raman spectra showed an F1 score of 0.964 for the classification of 15 distinct products. Furthermore, the use of data point attribution via gradient-weighted class activation mapping enhanced the interpretability of the model, revealing the spectral features that contributed the most to the classification decisions. The practical utility of the proposed model was assessed through the classification of unknown samples. The results indicate that 1D CNNs are promising tools for the identification of red stamp inks and can advance forensic and analytical methodologies in this field.

A group of richly decorated devotional jewelry from Sicily (18^th–19^th centuries) has been studied noninvasively by portable Raman spectroscopy, with the aim of identifying the gemstones that embellish these ex-voto. Different levels of richness were observed in the various objects, from those displaying rubies and diamonds to those decorated with garnets and quartz. Thanks to the high number of garnets found, a focus could be carried out on this mineral, proposing two methods for the treatment of the Raman spectra, involving also a group of reference garnets. On one hand, a development of the software MIRAGEM, called MIRAGEM+, was tested to establish the composition of the garnets. On the other hand, Principal Component Analysis (PCA) was applied to the whole complex of spectra to check the possibility of distinguishing groups of garnets with different compositions. The combination of the results obtained with the two methods demonstrated that both, coupled to Raman spectroscopy, are reliable tools for the noninvasive characterization of garnets and can be used with a longer pretreatment when few samples are involved (MIRAGEM+) or with a shorter preparation for a higher number of spectra (PCA).

Egyptian blue (EB), considered the first artificial pigment in history, exhibits an intense blue color and chemical stability over time. Throughout antiquity, EB was widely used to decorate architectural structures, sculptures, and artifacts. The aim of this research is to identify and characterize the EB paint layers found on the Parthenon and the Propylaea, used for the painting of entire areas and/or ornamental patterns (e.g., double meander). The paint layers are largely concealed under decay crusts and orange-brown patina layers, developed over the centuries. For the EB identification, a visible-induced luminescence imaging technique was used in situ, while μ-Raman spectroscopy, stereomicroscopy, polarized microscopy, and SEM/EDX analysis were applied in lab scale. The investigation revealed that the EB from both the Parthenon and the Propylaea consists primarily of cuprorivaite, quartz, and calcite, with no evidence of a glassy phase. The pigment's grain size ranges from 15 to 110 μm, with the most common size observed around 50 to 80 μm, which typically provides the dark blue hue on EB. The examined EB demonstrated remarkable durability over millennia, with only a few isolated grains showing slight discoloration. At the Propylaea, the EB paint layer was found to have a thickness ranging from 500 to 750 μm. A thick orange-brown patina layer, approximately 1.60 mm in thickness, was observed above the EB paint layer on the Parthenon. The patina is composed primarily of Ca, Si, Al, Mg, K, Na, and Fe—elements typically associated with surface deposits and weathering products.

Raman spectroscopy is a key technique for planetary exploration, providing mineralogical insights under strict constraints on power, mass and data transmission. This study applies principal component analysis (PCA) to Raman imaging data from the Sericho pallasite meteorite, composed mainly of Mg-rich olivine and Fe-Ni alloy. PCA efficiently reduced the complex dataset to only five principal components, retaining most of the molecular information. Using PCA scores, averaged Raman spectra were calculated to significantly simplify spectral interpretation, highlighting olivine as the dominant mineral and goethite, disordered hematite as well as disordered carbon as minor phases in the sample. In addition, PCA scores associated with the x-y coordinate of the Raman image enable identification of distinct mineralogical domains, revealing the spatial distribution of iron oxyhydroxides primarily at interfaces and fractures of the olivine inclusions. Additionally, PCA-filtered spectra enabled spatially resolved quantification of olivine composition, showing a 5%–10% magnesium enrichment in olivine cores compared to interfaces with iron oxyhydroxides, suggesting weathering origins of the Fe-Ni alloy. These results demonstrate the strong potential of PCA for data reduction, visualization and interpretation of complex Raman datasets, making it a powerful tool for in situ mineralogical analysis during future robotic or human planetary missions where fast real-time data processing is key for informed decision-making.

The water quantification in nominally anhydrous minerals (NAMs) is always a scientific issue of great interest. Raman spectroscopic technique, which has been widely employed in planetary explorations (e.g., SHERLOC, SuperCam, and LRS), offers potential for in situ mineralogical and volatile analysis. However, the direct assessment of H₂O in silicates via Raman spectroscopy remains technically challenging, primarily due to the instability of water in silicate mixtures and the weak Raman signals of H₂O in such matrices. In this study, we propose an indirect approach by using K₂SO₄ as an intermediary to avoid the instability of water in silicate mixtures, thereby establishing a correlation between the content of silicate minerals (e.g., pyroxene) and water with the Raman peak intensity in silicate mixtures. The results indicate a strong linear relationship between the ratio of K₂SO₄ to pyroxene and the intensity ratio of the Raman peaks. Additionally, the Raman peak intensity of K₂SO₄ increases with the concentration of K₂SO₄ solutions. Based on the linear relationship, a model is proposed to evaluate the Raman peak intensity ratio between different ratios of water and silicate minerals. The Raman signal of water in homogeneous mixtures (silicate minerals and water) also weakens with decreasing water content. Therefore, we propose that Raman spectroscopy is a valuable technique for quantifying H₂O content in NAMs without the need to prepare unstable water-containing mixtures.