Journal of Spectroscopy

Coal tar pitch (CTP) is a quite promising candidate for the production of porous carbons. Traditionally, the porous carbons are prepared by the heat treatment of carbon precursors in the presence of template and activator. In this paper, EDTA-Na₂Mg•4H₂O and CTP were mixed to produce porous carbons in the absence of template and activator, which were generated in situ by the heat treatment of EDTA-Na₂Mg•4H₂O. The pyrolysis and morphology behavior of the mixture of EDTA-Na₂Mg•4H₂O and coal tar pitch (EDTA-Na₂Mg•4H₂O@CTP) were studied by thermogravimetry and differential scanning calorimetry, Fourier transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy. The characteristics of the obtained porous carbons were characterized by N₂ adsorption-desorption isotherm. The results show that EDTA-Na₂Mg•4H₂O has a great influence on the pyrolysis and morphology of CTP. The pyrolysis behavior of CTP becomes complicated after the addition of EDTA-Na₂Mg•4H₂O for the physical and chemical changes of EDTA-Na₂Mg•4H₂O during the heat treatment. EDTA-Na₂Mg•4H₂O@CTP dehydrates at 160°C and decomposes Na₂CO₃ and MgO at 600°C. The surface morphology of EDTA-Na₂Mg•4H₂O@CTP changes with the EDTA-Na₂Mg•4H₂O content and heat treatment temperature. After acid washing of the product of EDTA-Na₂Mg•4H₂O@CTP heat-treated at 700°C, the obtained porous carbon material consists of micropores and mesopores. Its specific surface area is 574.18 m² g⁻¹ and the average pore width is 4.53 nm.

White-light OLED devices play an important application in information display fields. Optical interference of the microcavity structure has an important effect on device performances. According to the design of the band structure, ITO/MoO₃ composite films were used as the anode, and Mg : Ag (1%) composite films were prepared by coevaporation as the translucent cathode; CuPc was used as the hole injection layer and anode passivation layer, NPB as the hole transmission layer and yellow light main material, rubrene as yellow dopant material, ADN as blue light main material, DSA-Ph as blue dopant material, and TPBi and Alq3 as the electron transport layers. We realized the change of the microcavity structure by adjusting the thickness of each organic functional layer film and simulated and calculated the optimized thickness of each organic film layer and influence on OLED device performances using the SimOLED software system. The optimized OLED microdisplay structure is Si_(CMOS)/ITO (35 nm)/MoO₃ (2 nm)/CuPc (5 nm)/2-TNATA (20 nm)/NPB (10 nm)/NPB : rubrene (1.5%)ADN : DSA-Ph (5%) (25 nm)/TPBi (15 nm)/Alq3 (1.2 nm)/Mg (13 nm) : Ag (1%). The optimized OLED microdisplay was prepared by the vacuum coating system, and the photoelectric performances of the OLED device were characterized by a spectral testing system consisting of the Photo Research PR655 spectrometer and Keithley 2400 program-controlled power supply. The effect of the microcavity structure on OLED device performances was studied. The results show that the variation of the film thickness of each organic functional layer has an important effect on the performances of OLED microdisplay, such as brightness and color coordinate, and the OLED microdisplay reaches a higher brightness of 3342 cd/m² under the normal working voltage at 5.0 V after the structure is optimized, with CIE coordinate (0.28, 0.37), which is closer to the energy point of standard white light.

Detection of low-concentration molecules in liquids has been a challenge in sensing technologies. Raman spectroscopy is an effective approach for trace detection, which is in fact a “volume-excitation” and “volume-collection” technique in the analysis of liquid samples. However, for the commonly employed one-pass excitation and back-scattering detection scheme, a large portion of both the excitation laser energy and the Raman-scattering light energy is wasted without efficient reuse or collection. In this consideration, we demonstrate a broadband optical feedback scheme by a curved high-reflection mirror for both the excitation and the Raman-scattering light, so that the excitation and the forward-propagating Raman signal can be back-reflected and collected with a high efficiency. Using the “F+2f” design, where F and f are the focal lengths of the focusing lens and curved reflection mirror, respectively, we were able to not only produce two focuses of the excitation laser beam but also extend the Raman interaction by a doubled distance. For the detection of pure ethanol molecules and the R6G molecules in water with a concentration of 10⁻³ M, the Raman signal was enhanced by a factor of about 5.6. The optical feedback scheme and discovered optical mechanisms supply effective improvements to the Raman spectroscopic measurements on liquid samples.

In order to determine the concentration of melamine, nitrogen-doped carbon dots (NCDs) were synthesized in one step as a fluorescent probe. Uric acid and diethylenetriamine were used as carbon source and nitrogen source, respectively. The experimental results showed that the fluorescence of NCDs can be quenched by mercury ions (Hg²⁺). Due to the strong coordination affinity between the carbon-nitrogen heterocyclic of melamine and Hg²⁺, part of Hg²⁺ coordinated with melamine when melamine was mixed with Hg²⁺. Then, the fluorescence of the added NCDs was quenched by the remaining Hg²⁺. Therefore, the concentration of melamine could be determined. The results show that the method has high sensitivity in wide measuring range that the linear ranges are 50–400 μg/L and 800–2500 μg/L, and the R² is 0.997 and 0.988, respectively, with the limit of detection (LOD) of 21.76 μg/L. The NCDs are easy to fabricate, and the detection method is easy to implement. In this study, a new method for melamine detection was established, and the proposed method for melamine detection can provide some insights for food safety detection.

To facilitate the enhanced reliability of Raman-based tumor detection and analytical methodologies, an ex vivo Raman spectral investigation was conducted to identify distinct compositional information of healthy (H), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). Then, principal component analysis-linear discriminant analysis (PCA-LDA) and principal component analysis-support vector machine (PCA-SVM) models were constructed for distinguishing spectral features among different tissue groups. Spectral analysis highlighted differences in levels of unsaturated and saturated lipids, carotenoids, protein, and nucleic acid between healthy and cancerous tissue and variations in the levels of nucleic acid, protein, and phenylalanine between DCIS and IDC. Both classification models were principal component analysis-linear discriminant analysis to be extremely efficient on discriminating tissue pathological types with 99% accuracy for PCA-LDA and 100%, 100%, and 96.7% for PCA-SVM analysis based on linear kernel, polynomial kernel, and radial basis function (RBF), respectively, while PCA-SVM algorithm greatly simplified the complexity of calculation without sacrificing performance. The present study demonstrates that Raman spectroscopy combined with multivariate analysis technology has considerable potential for improving the efficiency and performance of breast cancer diagnosis.

Hyperspectral imaging technology can obtain the spatial information and spectral information of the simulated operational background and its camouflage materials at the same time and identify and classify them according to their differences. In this paper, we collected the hyperspectral images (400–1000 nm) of the desert background, jungle background, desert camouflage netting, jungle camouflage netting, and jungle camouflage clothing through the hyperspectral imaging system, and the samples were preprocessed by denoising and black-and-white correction. Then, we analysed the region of interest (ROI) of the training samples by principal component analysis (PCA). After the pixels in the region of interest and their surrounding areas were averaged, 60% of the data was used as the training samples, and the remaining 40% was used as the test samples. According to their similarities and differences between them and referenced spectrum, the models of classification were established by combining the Naive Bayes (NB) algorithm, K-nearest neighbour (KNN) algorithm, random forest (RF) algorithm, and support vector machine (SVM) algorithm. The results show that among the four models, SVM model has the highest accuracy of classification and the recognition rate of jungle camouflage clothing is the highest. This study verifies the scientific and feasibility of hyperspectral imaging technology for camouflage identification and classification in a simulated operational environment, which has some practical significance.