Spectrochimica Acta Part B: Atomic Spectroscopy最新文献

Elemental imaging using Laser-Induced Breakdown Spectroscopy (LIBS) mapping is increasingly applied to a wide range of materials. In geomaterials, phase identification and characterization (mineralogy) are crucial for solving a variety of problems in earth sciences and mining industry.

Here we applied LIBS mapping to ore samples from the world-class Kipushi deposit, Democratic Republic of Congo, because these ores are known for their great mineral complexity and economic value, especially for their high potential in critical metals such as Ge and Ga.

The mineralogy of the samples was reconstructed by combining two approaches: 1) element colocalization in LIBS maps that were selected based on the knowledge of occurring mineral species at the site and 2) correlation maps obtained by applying the Interesting Features Finder (IFF) algorithm. Reconstructed minerals include chalcopyrite, bornite, chalcocite, sphalerite, galena, pyrite, tennantite-(Zn), renierite, tungstenite, betekhtinite, carrollite, gallite, and a series of non-sulphide gangue minerals (quartz, carbonate and clay minerals). SEM-EDS was used to confirm the LIBS-derived mineralogy, especially for sub-pixel inclusions.

Among other results, Ge and Ga are hosted as major elements in renierite and gallite, respectively, and also as trace-elements in chalcopyrite, bornite and tungstenite (for Ge), and chalcopyrite, bornite, sphalerite and renierite (for Ga). Particular attention was paid to iron interfering with gallium at 417.20 nm, for which simple background-corrected peak intensities were compared to a more advanced chemometric method (MCR-ALS). All the results were obtained with minimal time and efforts and, while only qualitative in nature, they already provide essential information about ore texture, mineralogical composition and trace-element distribution across the ore.

Conventional tactile hardness testing methods such as Brinell, Rockwell or Vickers rely on direct mechanical contact, which results in significant surface damage and prohibits their application to complex specimen geometries. Furthermore, the sample must have a certain thickness for tactile testing methods to be applied. In this work, we investigated the use of laser-induced breakdown spectroscopy as a fast non-quantitative alternative for visualizing surface hardness gradients. To eliminate the influence of different chemical compositions, we manually heat-treated low-alloy steel pieces cut from the same raw material and batch. By partially quenching a sample piece, we were able to obtain a hardness gradient along the longitudinal axis. We found a positive correlation between the ratio of ionic to atomic line intensities of iron (I _{Fe II 263.1 nm} / I _{Fe I 358.1 nm}) and the mechanical hardness of the sample surface. By scanning the surface and measuring the line intensity ratios, we were able to obtain a spatially resolved map directly correlating with the surface hardness distribution. Additionally, it was observed that the irradiation of laser pulses resulted in significant surface alterations, thereby invalidating subsequent measurements and scans at identical positions.

Segregation monitoring plays a crucial role in the quality control of steels, and elemental microanalysis using laser-induced breakdown spectroscopy (LIBS) can be employed as an approach in this process. In this study, we established a LIBS system utilizing a 9-ps pulsed laser operating at 355 nm and 35 Hz repetition to investigate steel segregation. Successful analysis of Aluminum (Al), Copper (Cu), and Manganese (Mn) segregation in steels was achieved. The LIBS mappings with a spatial resolution of 2 μm demonstrated agreement with results obtained from Electron Probe Microanalysis (EPMA), thereby affirming their reliability. Furthermore, by employing an even higher spatial resolution of 1 μm, fine results could still be acquired through LIBS on a steel area of 100 μm². These findings demonstrate the effectiveness of ps-LIBS for resolving elemental segregation and suggest its potential as an alternative tool for steel inspection.

Portable X-ray fluorescence (pXRF) spectrometry is a non-destructive technique that has been successfully used to analyze different matrices. Foliar analysis is challenging because some plant nutrients cannot be detected by pXRF. Even so, the uptake interactions among nutrients which reflect different concentrations of macro- and micronutrients can be accessed via pXRF, constituting a basis to obtain predictive models of plant nutrients. The objective of this work was to compare and assess the accuracy of linear regression and non-linear models (support vector machine – SVM; and random forest – RF) to predict the actual concentration of macro- (N, P, K, Ca, Mg, and S) and micronutrients (B, Cu, Fe, Zn, and Mn) in coffee leaves. A greenhouse experiment was conducted using Hoagland and Arnon solution to obtain leaves with contrasting elemental composition. Ground and oven-dried leaf samples were analyzed via pXRF using: i) a manufactured pXRF calibration developed for general earth-materials (Geoexploration mode); ii) the Spectrometer mode with varying voltage and current (15 kV and 25 μA; 10 kV and 10 μA). The same samples were also analyzed via conventional acid digestion and quantified via inductively coupled plasma-optical emission spectroscopy (ICP-OES). The best predictions were obtained using SVM and RF algorithms, with high R² (0.82 to 0.99) and high residual prediction deviation (RPD) (2.35 to 9.34) values. However, some elements (e.g., K, Ca, Cu, Mn) were successfully predicted using linear models (LR and MLR). Even elements not detected (N and B) by pXRF were accurately predicted using the RF model. The pXRF operational conditions influenced the performance of the models. However, by parsimony, Geoexploration mode provided data for accurate prediction of macro- and micronutrients. This comprehensive study can potentially spark further investigations into examining coffee leaves from plants cultivated under various environmental and management conditions. Additionally, the methodological framework outlined here holds promise for ongoing experimentation across diverse crops, offering a streamlined, non-invasive, eco-friendly, and rapid approach for foliar analysis.

Soil type significantly influences the detection accuracy of heavy metals using X-ray fluorescence (XRF) technology. Rapid and accurate soil type identification is crucial for selecting appropriate XRF quantitative analysis methods for soil heavy metals, thereby enhancing analysis accuracy. This study utilized 26 soil samples from 10 distinct soil types, extracting 13 feature factors for soil type identification by analyzing XRF spectral variability. These factors were then integrated with three machine learning methods: Random Forest (RF), Support Vector Machine (SVM), and Backpropagation Neural Network (BPNN). The effectiveness of these methods in soil type identification was compared, highlighting the importance of XRF spectral feature factor extraction. The results demonstrate that identification based on feature factor extraction from XRF spectral variability markedly improves identification accuracy, stability and speed compared to full-spectrum XRF analysis. When identifying soil types by the gross area of spectral peaks of XRF feature factors, the accuracies of three machine learning methods—RF, SVM, and BPNN—were 99.62%, 99.04%, and 98.85%, respectively. Random Forest achieved the highest accuracy (99.62%) and fastest operation speed (0.179 s). Therefore, by extracting the differential features of XRF spectra and combining them with machine learning methods, it is possible to quickly and accurately recognize and judge soil types. This study demonstrates the successful and accurate identification of soil types using machine learning combined with XRF spectroscopy.

It establishes an important methodological foundation for the future development of fast and accurate field testing equipment for soil heavy metals using XRF technology.

This study presents an investigation of solution cathode glow discharge (SCGD) - optical emission spectroscopy (OES) using an artificial neural network (ANN) for Cu determination. The glow discharge from the SCGD cell was generated to collect 3600 spectra of twelve Cu concentrations from 2.2 mg/L to 40.7 mg/L as the training/validation and testing dataset for two ANN models. Their performances were then compared with the conventional linear regression calibrated at the Cu emission line of 324.8 nm. The accuracy of the ANN models was improved from 3% to 5% in the second and the first ANN models, respectively, while their precision can enhance from 2% at a Cu concentration of 40.7 mg/L to 12% at a Cu concentration of 2.2 mg/L. The detection limit can be reduced from 1.2 mg/L by linear regression to 0.3 mg/L by ANN models. We also confirmed that the performance of ANN models is in agreement with inductively coupled plasma-optical emission spectroscopy (ICP-OES), with a difference of accuracy below 3% in tracking three different Cu concentrations. To interpret the ANN model, ANN ‘s weight characterization was analyzed, and its results show that ANN can recognize the critical emission lines that affect the prediction results and can separate the spectral line even in spectrum superposition. This demonstrated the ability of ANN in improving accuracy and precision to trace heavy metal using the SCGD-OES spectra.

The aim of this study was to determine whether the degree of pulse overlap affects the analytical response of LA-ICP-MS when utilized in scanning mode. The investigation focused not only on the analytical signal but also on the properties of the generated aerosol and the morphology of ablation craters. Ablation was carried out using a nanosecond ablation system on single-element samples of metals and metalloids, specifically Cr, Hf, Sb, Si, Ta, and W. Pure single-element standards were selected to eliminate elemental fractionation and ensure the chemical composition of the generated aerosol was consistent across all particle sizes. The effect was studied on the surfaces of the samples prepared in various ways, including polished and two types of roughened finishes. It was found that the degree of laser pulse overlap significantly influences the quantity of generated particles, their size, and consequently the measured analytical signal, altering it by tens of percent. Slightly different properties were observed in the groups of metal and metalloid samples. For metals, an increasing overlap of pulses resulted in a decrease in the analytical signal across all types of surfaces. For metalloids, the results were different on polished surfaces. The study shows that adjusting the pulse overlap by combining the scan speed and laser repetition rate can have a significant effect on the LA-ICP-MS analysis results.

Lithium is one of the key elements in today's materials and battery industry, and the interest in non-destructive characterization techniques at a micron scale for materials containing lithium is steadily increasing. The electron microprobe is a reliable and accessible analysis tool widely used for this purpose, but performing X-ray emission spectroscopy in the low-energy range is still challenging.

In this work, we demonstrate that spectroscopy in the Li K energy range is feasible by integrating a new detection system composed of a multilayer and ultra-thin separation windows into a commercial wavelength dispersive spectrometer of a microprobe. The detection system is described in detail, and the results, in form of spectra showing the Li K emission in LiF and in a ternary quasicrystalline sample, are presented and analyzed. In LiF the emission band is centered at 54.5 eV and has varying intensities for different beam exposure times. The spectra obtained in the ternary quasicrystal clearly demonstrate that the detection system has sufficient energy resolution to separate the Li emission band and the Al $L_{2,3}$ and Cu $M_{2,3}$ emission bands, enabling chemical state analysis by comparing the shapes of the emissions. To our knowledge, this is the first time such a detection system, implemented in a WDS spectrometer and enabling the acquisition of different Li K spectra in various compounds has been described in detail. This kind of system can be used for Li quantification using a common procedure in electron probe microanalysis.

Atmospheric pressure air plasma is widely used in material processing, plasma-assisted combustion, life science and agriculture. The active-particle density is considered a key factor in monitoring the plasma states, and it is related to the electron temperature. However, there is a lack of an efficient optical emission spectroscopy (OES) method to obtain the electron temperature in atmospheric pressure non-equilibrium air plasma, especially when the electron energy distribution deviates from the Maxwellian distribution. So, in this work, we propose a novel combined OES line-ratios method to determine the high/low-energy electron temperature. This method is based on two summarized excited-state systems, the “N(2p^{3 4}S^o) excited-state system” and “O(2p³(⁴S^o)3s ⁵S^o) excited-state system”, which are presented to find key excited-states sensitive to the high/low-energy electron temperature, respectively. Besides, a collisional-radiative model, considering the atomic nitrogen and oxygen including their excited states, is built to provide the mapping relation and verify the kinetic properties from the above excited-state systems. Our combined line-ratio method has been applied in an air glide arc plasma igniter.