Journal of Raman Spectroscopy最新文献

Nickel-cobalt oxyborates (Ni,Co)₃B₂O₆ with a kotoite-type orthorhombic structure are known as antiferromagnetic materials and are of interest for optical applications and as promising battery anode materials. We report and analyze the results of experimental low temperature Raman studies of the phonon spectra measured in some polarizations of the Ni₃B₂O₆, Ni_2.81Co_0.19B₂O₆, Ni_2.4Co_0.6B₂O₆, Ni_2.07Co_0.93B₂O₆, and Co₃B₂O₆ crystals. Simulations of the density functional theory of Raman spectra of the Ni₃B₂O₆ and Co₃B₂O₆ crystals have been performed and analyzed. The vibrational modes of the Ni₃B₂O₆ and Co₃B₂O₆ crystals were interpreted. Low-wavenumber modes in the Raman spectra are associated with Ni and Co atoms vibrations accompanying BO₃ group deformations. Clear evidence of spin-phonon interaction was found for some specific phonons below $��{�T�}_{�N�}�$. The anomalies in the behavior of these phonon modes as a function of the nickel concentration in the crystal have been presented. The position and intensity of the Raman modes decrease when the Ni atoms are replaced by Co atoms.

Chemical composition dependent vibrational modes of manganese-based bismuth telluride (MBT) magnetic topological insulators thin films grown by molecular beam epitaxy were studied by Raman spectroscopy and compared with the vibrational modes of Bi₂Te₃ films. Intensity of $��E_g^3�$ TO mode was much stronger in Bi efficient MBT film. In contrast, vibrational cross-section of $��A_{�1�g�}^2�$ LO mode was stronger in Bi deficient MBT compound. $��E_g^3�$ TO mode corresponds to in-plane vibrations of Bi-Te atomic layers. Deficiency of Bi atoms in the MBT indicated that the vibrational degree of freedom of Bi-Te in the septuple unit cell was less probable. Antisymmetric out-of-phase vibration of Te-Te atoms resulted in $��A_{�1�g�}^2�$ LO mode in which Bi atom was stationary, therefore vibrational cross-section of this mode for Bi deficient MBT film was stronger. A resonantly excited E_g and A_1g modes was observed at excitation energy 1.57 eV associated to deformation potential and Frohlich electron–phonon coupling, respectively.

Surface-enhanced Raman spectroscopy (SERS) is considered powerful analytical technique that significantly enhances the Raman scattering which enables the detection of low concentration of biomolecular analytes of blood serum, which can lead to early diagnosis of diseases. The aim of this study is Surface enhanced Raman spectral monitoring of 50 KDa filtrate portions of blood serum samples of patients with iron deficiency anemia. The blood serum components include higher molecular weight fractions (HMWF) and lower molecular weight fractions (LMWF) of biomolecules. The biomarkers of iron deficiency anemia, which are low molecular weight fractions are suppressed by high molecular weight fraction hence hindering them from contributing in the diagnosis of the disease. The ultra-filtration of the blood serum samples is performed using filtration devices of 50 KDa. The specific SERS spectral features related to biochemical markers of iron deficiency anemic subgroups (with different values of hemoglobin) from different blood serum samples are identified including 479, 535, 888, and 1300 cm⁻¹. These SERS features can be associated with different biomarkers of iron deficiency anemia consisting of ferritin, hepcidin, iron bound transferrin, and erythropoietin. Moreover, chemometric statistical techniques including principal component analysis (PCA) and partial least square regression (PLSR) are used for qualitative analysis of variations between spectral data and quantitative prediction of hemoglobin concentration of iron deficiency anemia samples, respectively.

Micro-Raman spectroscopy was employed to analyze apatite-containing samples from the Tomtor complex of ultrabasic rocks and carbonatites, located in the sharply continental region of Yakutia, Russia. Raman spectra excited at a wavelength of 532 nm revealed the characteristic vibrational bands of apatite, as well as bands attributed to carotenoid-type cyanobacteria. Additionally, as an artifact, the spectra exhibited bands resulting from the laser-induced photoluminescence of trivalent rare earth elements (REEs). Preresonance Raman spectra recorded at least two distinct C=C stretching modes of the β-carotene polyene chain, indicating the presence of this carotenoid component associated with apatite. Furthermore, three characteristic carotenoid bands were recorded in samples from the specific weathering crust on carbonatites that lacked apatite-related bands. These findings provide direct evidence of cyanobacteria in the studied samples, suggesting the likely involvement of microorganisms in the formation of Nb-REE-rich ores within the Tomtor field in Russia.

Rapid and accurate identification of plastic beverage bottles is of great importance because plastic beverage bottles can be encountered as physical evidence in cases involving assaults, thefts, and homicides. In this experiment, 40 commercially available plastic beverage bottles were collected as experimental samples, and their Raman spectral data were collected. Initially, the samples were classified into two categories of polyethylene terephthalate (PET) and polyethylene (PE), and the 35 PET samples were further clustered into three categories by K-means clustering. Savitzky–Golay algorithm smoothing, standard normal variate, multiple scattering correction, and first-order derivatives were utilized to improve the quality of the Raman spectra. A convolutional neural network (CNN) model was constructed for the classification and identification, and four evaluation indexes, such as accuracy, precision, recall, and F1-score, were utilized to compare the model's performance under the four types of preprocessing. The results show that the spectral data preprocessing combining SG and MSC has higher accuracy than other preprocessing methods, and the CNN classification model has the best performance, with 100% correct classification rate in both the training set and the test set, respectively. In conclusion, the results show that convolutional neural networks, when used in combination with Raman spectroscopy, can quickly detect the type of plastic beverage bottle, which is crucial for solving crimes.

Baseline background tends to cause the Raman spectral signal to be hidden or distorted, making it difficult to accurately acquire spectral information. Although polynomial fitting has been widely proven to be an effective baseline correction method, it is difficult to achieve both high speed and high accuracy. In this paper, we propose a piecewise polynomial fitting method based on a sliding adaptive window(S-ModPoly). S-ModPoly relies on the adaptive width selection of a sliding window to automatically split the original spectrum into many different length segments containing complete peak information. Low-order iterative polynomial fitting is performed for each segment separately, which greatly reduces the computational effort while improving the baseline fitting accuracy. By further correcting the piecewise fitting results, the discontinuities between different intervals after piecewise fitting are eliminated. Furthermore, the true intensity information of the spectral signal is retained. Here, S-ModPoly is compared with three representative automated methods. The experimental results show that the S-ModPoly method has lower mean error and higher stability in a very short time (less than 20 ms). Additionally, the experimental results on measured Raman spectra demonstrate the effectiveness of the method in automatically processing various real spectral baselines. It performs well in both low and high intensity background baselines.

Raman spectroscopy is an ideal tool in the characterization of materials including PuO₂. The wavelength-dependent absorptivity of the material defines the light penetration depth and the relative Raman scattering contribution from the bulk and the surface. The surface contribution to the total Raman scattering was investigated for PuO₂ calcined at various temperatures and recorded with laser wavelengths of 355, 325, and 244 nm. These experiments provided the first glimpse of the wavelength-dependent disappearance and emergence of new phonons and electronic bands from the PuO₂ surface layers. The first indication of the wavelength transition in the Raman spectra was the loss of the 2LO2 (overtone, ~1155 cm⁻¹) band and the weakening intensity of the Г₁ → Γ₅ electronic band (~2135 cm⁻¹) with the 355-nm excitation laser. The Γ₅ electronic band was barely visible with the 244-nm excitation. The electronic band located at ~1050 cm⁻¹, corresponding to the Г₁ → Γ₄ electronic transition was observed to dramatically increase in intensity while the Г₁ → Γ₃ electronic band (2640 cm⁻¹) sharpened as the UV wavelength was increased in energy from the near- to deep-UV (355–325–244 nm). The FWHM of the T_2g band was found to vary with calcination temperature (450°C and 900°C) with the 325-nm laser and the 244-nm laser. The T_2g band attributes, the strong emergence of the Г₁ → Γ₄ electronic band, and the disappearance of the 2LO2 overtone acquired with the 244-nm excitation for the different calcination temperatures suggest a shallow penetration depth.