This study proposes a new method of detecting tungsten inclusions in nuclear fuel rod upper-end plug welds using energy-dispersive X-ray fluorescence (EDXRF) analysis. The Monte Carlo simulation method was used to simulate the process of detecting tungsten inclusions in nuclear fuel rod upper-end plug welds by the EDXRF. The detectable tungsten particle diameters in the zirconium alloy at different depths in welds and the detection limits of the trace tungsten dispersed in welds were obtained. Then, we constructed an experimental device that uses a CdTe detector with an X-ray tube. The results showed that the relative standard deviation of the net count rate of tungsten K-series characteristic X-rays [W (Kα)] was 1.46%, and the optimum parameters are a tube voltage of 150 kV and current of 0.5 mA. These values were used to perform energy-dispersive X-ray fluorescence analysis. These results were compared to the X-ray radiographic results, which were broadly similar. Furthermore, the results of EDXRF analysis were more legible and reliable than those from X-ray radiographic inspections. This study demonstrates the feasibility of applying EDXRF analysis to detect tungsten inclusions.

The interaction behavior of Fe3+ with transferrin and apotransferrin (iron-free form) was investigated in this study using affinity capillary electrophoresis. Change in the mass and charge of protein upon binding to the metal ion in the capillary tube led to variation in its migration time and was used to measure the noncovalent binding interactions by fast screening method. Acetanilide was used as the electroosmotic flow (EOF) marker to avoid possible errors due to the change in EOF during the experiment. The binding results were calculated from the mobility ratios of protein (Ri) and EOF marker (Rf) using the formula (Ri − Rf)/Rf or ∆R/Rf. For more comprehensive understanding, the kinetics of the interaction was studied and binding constants were calculated. Results showed that the Fe3+ displayed insignificant interaction with both proteins at lower metal ion concentrations (5–25 μmol/mL). However, transferrin exhibited significant interactions with the metal ion at 50 and 100 μmol/mL (ΔR/Rf = 0.0114 and 0.0201, resp.) concentrations and apotransferrin showed strong binding interactions (ΔR/Rf = −0.0254 and 0.0205, resp.) at relatively higher Fe3+ concentrations of 100 and 250 μmol/mL. The binding constants of 18.968 mmol−1 and −13.603 mmol−1 were recorded for Fe3+ interaction with transferrin and apotransferrin, respectively, showing significant interactions. Different binding patterns of Fe3+ with both proteins might be attributed to the fact that the iron-binding sites in transferrin have already been occupied, which was not the case in apotransferrin. The present study may be used as a reference for the investigation of protein-metal ion, drug-protein, drug-metal ion, and enzyme-metal ion interactions and may be helpful to provide preliminary insight into the new metal-based drug development.

The high-resolution terahertz spectra of the two structural isomers, mannose and galactose, have been measured by terahertz time-domain spectroscopy (THz-TDS) in the range of 0.5–4.0 THz at room temperature. Significant differences between these similar molecules have been found in their THz characteristic spectra, implying that THz-TDS is a powerful tool for identifying isomers. Structural analyses and normal mode calculations of the two systems were performed using solid-state density functional theory (DFT) with the PBE and PW91 density functionals as well as using gas-state DFT with B3LYP hybrid functional. Among these calculations, the solid-state simulated results obtained from the PBE method exhibit a good agreement with the experimentally measured spectra. According to the calculated results of PBE, the observed spectral features were assigned as primarily external lattice translations, deformations, and rotations with lesser contributions due to intramolecular motion of pyranose ring, CH2OH group, and hydroxyl groups.

Total suspended matter (TSM) is a core parameter in the quantitative retrieval of ocean color remote sensing and an important indicator for evaluating the quality of the aquatic environment. This study selects part of Nansi Lake in North China as the study area. Researchers used Hyperion remote sensing data and field-measured TSM concentration as data sources. Firstly, the characteristic variables with high correlation were selected based on spectral analysis. Then, seven methods such as linear regression, BP neural network (BP), KNN, random forest (RF), and random forest based on genetic algorithm optimization (GA_RF) are used to construct the inversion model of TSM concentration. The retrieval accuracy of each model shows that the machine learning models are much more accurate than the linear model. Among them, the GA_RF model retrieves the suspended solids concentration with the best performance and the highest prediction accuracy, with a determination coefficient R2 of 0.98, a root mean square error (RMSE) of 1.715 mg/L, and an average relative error (ARE) of 6.83%. Additionally, the spatial distribution of TSM concentration was inversed by Hyperion remote sensing image. The results showed that the concentration of TSM was lower in the northwest and higher in the southeast, and the concentration distribution was uneven, showing the characteristics of a typical shallow macrophytic lake. This study provides an effective method for monitoring TSM concentration and other water quality parameters in the shallow macrophytic lake and further proves the advantages of machine learning in ocean color inversion. All in all, this research provides some useful methods and suggestions for quantitative inversion of TSM concentration in shallow macrophytic lakes.

Introduction of modern technologies and methods and quality analysis for the gemstone industry are the main strategic initiatives of the Small and Medium Development Authority (SMEDA) of Pakistan. In this regard, four natural gemstones Quartz, Pyrope-Almandine Garnet, Black tourmaline, and Amethyst brought from Hunza valley Pakistan were analyzed by state-of-the-art spectroscopic techniques including EDX, UV-VIS, and FTIR spectroscopy. EDX revealed the traces of Fe, Mg, and Ca in Pyrope-Almandine garnet, Mg and Fe in Black tourmaline, Au and Ca in Amethyst. UV-VIS data revealed the values of Urbach energies 520, 210, 460, and 430 meV, and the values of direct bandgap energies 5.14, 6.12, 5.54, 5.74 eV, respectively. The higher structural disorder due to the presence of Fe and other impurities in stones except Quartz was attributed to the higher values of Urbach energies and decrease in band gaps: FTIR data Fe-O and Si-O stretching vibration in Pyrope-Almandine garnet, Si-O bending vibrations and O-H stretching vibration in Quartz, Si-O-Si bending and stretching vibrations and C=O stretching vibrations in Black tourmaline, Ca-O stretching vibrations and Si-OH weak-vibrations in Amethyst. Photoluminescence results also showed useful information in investigating the properties of gemstones.

Ibrutinib, a Bruton’s tyrosine kinase that plays an essential role in the B-cell development and cancer cells, has been recently approved to treat chronic, lymphocytic, and other types of leukemia. This study focused on investigating ibrutinib by its electronic transitions, vibrational frequencies, and electrospray mass spectra. The experimental peaks for electronic spectrum were found at 248.0 and 281.0 nm, whereas the νC = 0 stretching frequency was found at 1652.4 and 1639.19 cm−1. These experimental properties were compared with the corresponding theoretical calculations in which density functional theory was applied. The optimized structure was obtained with the calculations using a hybrid function (B3LYP) and high-level basis sets [6-311G++(d,p)]. Most of the calculated vibrational frequencies showed a relatively good agreement with the experimental ones. The electronic transitions of ibrutinib calculated using time-dependent DFT method were performed at two different solvation methods: PCM and SMD. The mass spectrum of ibrutinib, its fragments, and its isotopic pattern agreed well with the expected spectra.

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.

The identification of oil species and their characteristics in oil spills is particularly important for their efficient disposal. Since aromatic hydrocarbons (AHs) with various characteristics are the major fluorescent components of oils in seawater, they can be used to detect different oil species in seawater. Here, we developed a composition method using the fluorescence spectra of eight AH categories analyzed by gas chromatography-mass spectrometry (GC-MS) to evaluate the contribution of AHs to the total fluorescence of six different oil species. Qualitative and quantitative difference analysis of the experimental and composite fluorescence spectra was performed based on the redshift of the main peak position, the fluorescence intensity distance, and the generalized included angle cosine, while correlation analysis was used to establish the relationship between the different fluorescence spectral parameters and the American Petroleum Institute gravity and viscosity of the oil species. The fluorescence spectra recorded for heavy oil samples indicated a reduction in the fluorescence signal of fluorene series and an increase in the contribution of acenaphthene and pyrene series, indicating that the currently developed composition method by fluorescence spectroscopy combined with GC-MS can be used to distinguish and identify oil species in seawater samples.

The chemical composition of rape stalk is the physiological basis for its lodging resistance. By taking the advantage of NIRS, we developed a rapid method to determine the content of six key composition without crushing the stalk. Rapeseed stalks in the mature stage of growth were collected from three cultivation modes over the course of 2 years. First, we used the near-infrared spectroscope to scan seven positions on the stalk samples and took their average to form the spectral data. The stalks were then crushed and sieved; then the ratio of carbon and nitrogen, ratio of acid-insoluble lignin and lignin, and the content of soluble sugar and cellulose were determined using the combustion method, weighing method, and colorimetric method, respectively. The partial least squares regression (PLSR) method was used to establish a prediction model between the spectral data and the chemical measurements, and all models were evaluated by an internal interaction verification and an external independent test set sample. To improve the accuracy of the model and reduce the computing time, some optimization methods have been applied. Some outliers were removed, and then the data were preprocessed to determine the best spectral information band and the optimal principal component number. The results showed that elimination of outliers effectively improved the precision of the prediction model and that no spectral pretreatment method exhibited the highest prediction accuracy. In summary, the NIRS-based prediction model could facilitate the rapid nondestructive detection in the key components of rapeseed stalk.