Spectrochimica Acta Part B: Atomic Spectroscopy最新文献

Material science is an interdisciplinary field that draws on elements of chemistry, physics, engineering and deals with designing, producing, and using a wide range of materials. The methodological approach of materials investigation is very significant in cultural heritage.

The study of Cultural Heritage materials is useful for dating, unraveling production technologies, sources and trading, and their restoration and preservation.

Archaeometry is the tool for material characterization, but it cannot always be applied in a non-destructive way: as a result, analytical techniques requiring minimum sampling are of great interest. For this, Total reflection X-ray fluorescence (TXRF) spectrometry is an effective technique, thanks to its microanalytical capabilities and the possibility of preserving the sample after its analysis, either for additional investigations or archiving purposes. It is very sensitive to period four transition elements but less effective with lighter ones. Thus, to gather the most comprehensive analysis of historic enamelled ceramics, Scanning Electron Microscopy-Energy Dispersive Spectroscopy (SEM-EDS) and TXRF spectrometry were combined in a novel analytical approach.

Soil standards and natural soil pigment samples were tested to validate the novel analytical approach. Na, Mg, Al, Si, K and Ca were determined with SEM-EDS analysis and used for TXRF quantification of heavier elements without adding any internal standard. The methodology to determine the total composition of artifacts, by integrating the concentrations of light elements in EDS with the data of the heavier elements obtained with TXRF, is here developed expressing elements as oxides recalculated to 100%. Recovery values for standards were found mostly within 20% of the certified values for MgO, Al₂O₃, SiO₂, K₂O, TiO₂, Cr₂O₃, MnO, Fe₂O₃, NiO, CuO, PbO and SrO. The detection capabilities for major, minor and trace elements in soil pigments prove that this novel, practically non-destructive, analytical approach has a high potential for obtaining the elemental composition of Cultural Heritage materials with broad applications.

The high-resolution broadband LIBS spectrum is typically acquired using an echelle spectrometer and array detectors. However, the full frame (i.e., echellogram) captured by detector is commonly converted to 1D spectrum for the further analysis, which would both limit the detection speed of LIBS and potentially degenerate the analytical performance. In this work, we propose an echellograms-based analysis method for the quantification of minor metal elements in steel. Two metal elements, Mn and Ti, are chosen to validate the feasibility of the analytical method. The LASSO algorithm is used to build the multivariate regression model using LIBS spectra and echellograms respectively. The analytical performance obtained with the echellograms-based model is compared with that of spectra-based model. The efficiency of the echellograms-based quantitative analysis method is demonstrated with better prediction accuracy and precision. Notably, the echellogram-based regression model involves only tens to thousands of pixels from the echellogram, which can be selectively read out using a CMOS detector. The combination of echellogram-based quantitative analysis method and CMOS detector provides new ideas for high-frequency LIBS detection.

This paper introduces a new method for the simultaneous determination of lead, aluminum, and iron in plant samples using high-resolution continuum source electrothermal atomic absorption spectrometry (HR-CS ETAAS). The method is suitable for covering a wide range of concentrations for all three elements, by utilizing two spectral lines for Al and employing the wavelength-selected absorbance (WSA) approach, which combines the reading of absorbance signals at both the central and wing parts of the spectral lines. The method was validated against certified reference materials and was then applied in a large-scale analysis of Antarctic flora collected from Nelson Island in the South Shetland Islands, Antarctica. The method was found to be a useful biomonitoring tool for assessing Pb pollution in various plant materials, including lichens, mosses, grass and mushrooms, while Al and Fe contents may serve as normalizing elements in calculations of environmental indices. The observed Pb levels in lichens (median content 0.19 mg Pb/kg) were lower than those reported in other Antarctic regions. These findings indicate that the Stansbury Peninsula on Nelson Island is relatively unaffected by local pollution, compared to other Antarctic regions, and that the data might serve as an example of background levels in the South Shetland Islands.

The direct determination of 18 elements (B, Ba, Ca, Cd, Cs, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Pb, Rb, Sr, Tl, and Zn) in 1% aqueous honey solutions (dilution factor = 100) by ICP MS was presented. The presence of sugar in sample solutions caused a decrease in analytical signals, the appearance of carbon-containing polyatomic ions, and the deposition of carbonaceous products on a sampler cone. On the flip side, these effects did not destabilize the ICP, while the internal standard calibration (⁹Be, ⁸⁹Y, and ¹⁵³Eu as standards) made it possible to correct matrix effects. Additionally, in the case of ¹¹B, ²³Na, ⁴³Ca, and ⁵⁷Fe isotopes, using a collision cell (He, 2 mL min⁻¹) was required. Due to lower sample blanks, the analysis of water-diluted honey samples offered on average 3 times lower detection limits of elements than achieved for microwave-digested samples. The proposed method, characterized by high analytical throughput, was used for the analysis of 120 samples of honey from the apiaries in the Lower Silesia province. The average content of the major elements in the analyzed honey samples was: B 10.4 mg kg⁻¹, Ca 61.0 mg kg⁻¹, Fe 1.0 mg kg⁻¹, K 1049 mg kg⁻¹, Mg 27.8 mg kg⁻¹, Mn 3.4 mg kg⁻¹, Na 9.1 mg kg⁻¹, Rb 1.4 mg kg⁻¹, Zn 2.0 mg kg⁻¹, and the average content of the trace elements: Ba 130 μg kg⁻¹, Cd 3.2 μg kg⁻¹, Cs 4.8 μg kg⁻¹, Cu 315 μg kg⁻¹, Li 3.8 μg kg⁻¹, Ni 90 μg kg⁻¹, Pb 8.0 μg kg⁻¹, Sr 119 μg kg⁻¹, Tl 1.2 μg kg⁻¹. It was found that (1) the concentrations of all tested elements (except B and Tl) were higher in dark honey, (2) the content of Cs and Rb in honey from the mountain areas of the voivodeship was much higher than this in honey from the lowland areas, and (3) the best discriminators in terms of the color and the botanical origin of honey were the concentrations of Cu and Mn, and to a lesser extent also B, Ca, K, Mg, and Na; e.g., honeydew and buckwheat honey contained much higher amounts of Cu and Mn, sunflower honey was characterized by a significantly higher content of Ca, and rape honey showed an increased B content. Given that the proposed sample preparation procedure allows the simultaneous, reliable, and fast determination of major, trace, and ultra-trace elements, it seems to be a convenient method for the profiling of many honey samples in a very short time.

This study investigates the impact of sample temperature T_s on Laser-Induced Breakdown Spectroscopy (LIBS) emission characteristics and Calibration-Free LIBS (CF-LIBS) quantitative analysis in vacuum and air. Using an aluminum‑tin‑copper alloy, we analyze spectral lines within a 20 °C to 170 °C temperature range. Evaluation encompasses emission line intensities, plasma parameters, expansion imagery, and laser ablation crater morphologies. CF-LIBS analysis underlines that elevated T_s maintains quantitative accuracy even for minor elements with weak lines. In atmospheric conditions, tin segregation occurs, yet CF-LIBS remains robust. These results highlight CF-LIBS's potential for varied applications by mitigating T_s-induced effects.

The Molten Salt Reactor (MSR) represents a significant addition to India's clean energy initiative through nuclear energy. Noteworthy for its inherent safety features and the utilization of mixed fluoride salts (U, Th, Li) as molten-liquid fuel, this reactor stands out as a unique advancement. This paper showcases the application of Laser-Induced Breakdown Spectroscopy (LIBS) to ensure the chemical composition of the fuel. The micro-destructive nature of the analysis, minimal sample preparation requirements and the potential for remote analysis position LIBS as a viable alternative to Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The study encompasses the quantification of UF₄ and ThF₄, demonstrating both univariate and multivariate chemometrics calibration approaches. Various normalization methods on the raw spectral data are explored, and the results are meticulously compared. Upon evaluating different analytical methods, the optimal accuracy and precision achieved were in the range of approximately 2.6% and 3.9% for UF₄, and about 0.4% and 0.5% for ThF₄, respectively. This underscores the effectiveness of LIBS in ensuring the accurate determination of chemical composition in the context of MSR fuel.

The combustion of fuels in industries is one of the sources of greenhouse gases, posing a considerable threat to human health and the natural environment. The monitoring of the combustion process and the measurement of combustion degrees, therefore, are crucial and significant. In this work, an experimental system was developed based on Laser-induced breakdown spectroscopy (LIBS). Charcoals with different combustion degrees were taken as samples, and in the LIBS spectra of charcoals, the characteristic line of C and the molecular bands of CN and CaO were observed. Moreover, a univariate calibration model based on the exponential fitting curve was constructed to predict the combustion degree according to the variation of the C I line intensity under different combustion degrees. Furthermore, different algorithms including support vector machine (SVM), partial least squares (PLS), random forest (RF), and convolutional neural network (CNN) were applied to establish the multivariate calibration model, and the prediction performance was compared according to the 10-fold cross-validation. Particle swarm optimization (PSO) was used to optimize hyperparameters of the selected SVM-based multivariate calibration model. Principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to extract spectral features and reduce dimensions for the improvement of the model's performance, with the final root mean square error of prediction (RMSEP) and mean relative error (MRE) of the LDA-PSO-SVM calibration model of 0.035 and 3.877%, respectively. Finally, c. The results prove feasible to realize the real-time determination of combustion degree to control carbon emissions and monitor carbon concentration through the LIBS-based method with machine learning, which also provides new ideas for the application of LIBS in the atmospheric field.

Total reflection X-ray fluorescence (TXRF) is widely used for trace element analysis. This method is advantageous because of its simple sample preparation. However, the sample must be placed within the analytical volume (region and height). In this study, the analytical volume was experimentally evaluated and visualized using thin, localized Au layers. The intensity distributions of the Au Lβ lines and background Au Lβ peaks were visualized. Images of the intensity ratio of the Au Lβ line and the background peak were drawn and analyzed to determine the analysis region on the glass substrate depending on the glancing angle. A glancing angle of 0.025° was optimal for TXRF analysis, resulting in a large signal-to-background (SB) ratio. Glancing angles of 0.05° and 0.075° were also effective for obtaining large SB ratios for a relatively large area (>4 mm²). To reduce the thickness of the dried residue and the self-absorption of the XRF emitted from the residue, a glass substrate was subjected to He plasma jet treatment. X-ray photoelectron spectroscopy confirmed that the carbon contamination was reduced from the surface of the glass by the plasma jet treatment, thereby modifying the chemical properties to confer hydrophilicity to the glass surface. The time variation of the C1 s peak intensity suggests that the hydrophilic property was maintained for several hours. The plasma-treated glass was effective in obtaining a thin layer-like residue from a white wine sample. The TXRF results indicated improved recovery and RSD, particularly for low-Z elements, because the absorption effect was reduced in the thin layer-like residue.

In laser-induced breakdown spectroscopy (LIBS), identifying key emission lines for accurate elemental quantification has long posed a challenge. Traditional methods rely on experimental knowledge, atomic databases, and intricate spectral analyses. Although machine learning techniques – such as boosting algorithms and neural networks – offer efficient processing for large datasets, the complexity of these techniques often compromises interpretability. To address this issue, our study integrates the SHapley Additive exPlanations (SHAP) algorithm with gradient boosting models in order to interpret the most important spectral features, thus enhancing our understanding of how specific emission lines contribute to the carbon (C) concentration predictions in soils. Deployed on a large dataset of 1019 soil samples, a wrapper method with a random forest regressor reduced the initial spectral intensity features from 13,748 to 1098. Subsequent application of a LightGBM regression model calibrated via the Optuna framework yielded – for training and validation sets, respectively – an $R^2$ of 0.98 and 0.77, and RMSE values of 1.55 and 4.54 g kg⁻¹. The SHAP summary plot showed that C emission lines influenced the model's predictions positively, as anticipated, whereas silicon (Si) emission lines produced a negative impact, suggesting a lower C concentration in sandy soils. Our findings not only validate the efficacy of SHAP in improving LIBS-based soil C quantification, but they also offer a sophisticated framework for decoding the complex interplay between emission lines and target elemental concentrations.

Laser-induced breakdown spectroscopy (LIBS) is an established technique used for remote geochemical analysis. Recent successes on Mars rovers have made LIBS a likely technique for use on future missions to Venus, the Moon, and other airless bodies in the Solar System. To evaluate the accuracy of major element predictions in the latter environments, a large-scale database of LIBS rock spectra was collected under Earth, Mars, and vacuum conditions. Use of calibration transfer was evaluated by training models with spectra collected in one atmosphere and testing on spectra from another. A variety of metrics was used to evaluate model performance, including root mean-squared errors and the slope and y-intercept of the linear relationship between predicted and measured compositions. Prediction accuracies of major elements were similar among all atmospheres and best when conditions of training and test spectra matched. Calibration transfer facilitates respectable accuracy among spectra acquired under mismatched conditions, indicating its usefulness when difficult atmospheric conditions such as those on Venus limit the collection of calibration spectra.