Journal of Analytical Atomic Spectrometry最新文献

Laser-induced breakdown spectroscopy (LIBS) has the potential to serve as a valuable tool in the field of metal failure estimation. In this work, 12Cr1MoV steel, a material with different grain size grades, was selected as the experimental sample. Spectral and acoustic data were recorded during the laser ablation process. Initially, it was revealed that the acoustic energy did not exhibit a significant downward trend with the continuous laser shots, but the acoustic energy fluctuations became more intense. In order to enhance the capacity to assess the grain size grade of heat-resistant steel, we advanced a novel proposition to integrate acoustic data with spectral data. Two data fusion strategies were proposed for the integration of spectral and acoustic data: first, dimensionality reduction followed by combination, and second, combination followed by dimensionality reduction. Subsequently, two classification models, linear discriminant analysis (LDA) and support vector machines (SVM), were constructed utilising three data types: spectral data, acoustic spectral data, and the aforementioned combined data set. The performance of the model trained on the combined data obtained based on the first strategy is superior to models trained on a single data type (spectral data or acoustic spectral data), achieving a classification accuracy of 92.29%. The second strategy yielded unsatisfactory results due to the significant difference in dimensions between spectral data and acoustic spectral data. To address this, a modification was proposed by carrying out spectral feature screening on spectra data using RFE before data fusion and studying the impact of the number of remaining variables after RFE processing on model performance. The results showed that the model achieved the highest classification accuracy of 98.85%. The measurement illustrates the effectiveness of integrating spectral and acoustic spectral data for enhanced metal assessment.

Rare earth elements (REEs) are widely used as important geochemical tracers in earth and planetary sciences. The laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) technique has been routinely used for the determination of REE concentrations in various minerals. Nevertheless, it remained challenging to determine ultra-low REE contents (down to ng g⁻¹). Cassiterite with ng level REE contents shows false positive Gd and Tb anomalies in various datasets obtained by LA-ICP-MS. Herein, a novel analytical protocol for the accurate determination of ultra-trace REEs by LA-ICP-MS/MS is developed using oxygen as a reaction gas in mass shift mode, which avoids analytical artifacts caused by polyatomic interferences. Its application to cassiterite effectively eliminates Gd and Tb false positive anomalies. Both laser and solution cassiterite results have been used to prove the robustness of our protocol. The accuracy and precision of our approach is better than 10%. Our method can greatly facilitate the analysis of other geological, archeological, and environmental materials with large amounts of tin in the matrix that disturbs the REE measurement.

Sample preparation is a critical step to achieve reliable in situ chemical analysis. Sample mounting technique with a tin-based alloy was developed in recent years, which is particularly useful for high-precision volatile analyses by secondary ion mass spectrometry (SIMS). However, the success of this technique is hindered by challenges, such as complex alloy preparation and potential Pb contamination. Herein, we introduce a new Sn–Bi alloy preparation method that may overcome these hurdles and assess its potential as a standard preparation method for in situ volatile and isotope analyses. This new alloy can be manufactured with commercially available pure tin and bismuth metal (atomic Sn : Bi = 42 : 58), and its production requires only a heating plate and clean containers. This ensures its high accessibility to laboratories worldwide. The Pb content of the alloy is dependent on the tin and bismuth used. The material (Sn and Bi) from three different manufacturers were evaluated in this study, resulting in the virtually Pb-free MAC alloy (Pb <0.2 μg g⁻¹). The SIMS U–Pb dating results of the zircon standards (Qinghu, Plešovice, and SA01) are consistent with the recommended values (within error). Furthermore, the mounted samples exhibit satisfactory relief on this alloy, suggesting that this alloy material is appropriate for the analysis of oxygen isotopes. The routine external precision of oxygen isotope ratios is better than 0.30‰ (2sd), on par with that obtained with epoxy mounts. The water background in the SIMS sample chamber can be recovered rapidly after sample transfer from the storage to the sample chamber. Hence, this tin-based alloy is suitable for sample mounting for SIMS volatile and isotope (incl. U–Pb) analyses.

Investigating the interactions between energetic electrons and metals, which generate diagram lines and satellite lines, can provide insights into atomic de-excitation dynamics, chemical compositions, and ionization cross-sections. In this paper, we introduce a self-developed multi-channel Focusing Spectrometer with Spatial Resolution (FSSR) and measure K-shell X-ray spectra from an Al target impacted by an electron beam. Our results show the K_α diagram line and satellite lines K_αLⁿ (n = 1–4). The high-contrast spectral result demonstrates the efficiency and sensitivity of our FSSR in detecting these satellite lines. Additionally, we compare the energy shifts of different satellite line groups relative to the diagram line with theoretical models. We also calculate the Al KL double ionization cross-sections and estimate the TS2 process cross-sections.

Metal Additive Manufacturing (AM) holds significant importance in advancing intelligent manufacturing and sustainable development. However, due to the unique manufacturing process of AM, defect detection in AM components has always been a challenging issue. This study employed Laser-Induced Breakdown Spectroscopy (LIBS) technology to capture spectral information and utilized a high-speed camera to record plasma images, comprehensively extracting pertinent details from each laser event. LIBS spectral scores were obtained via principal component analysis (PCA) and plasma image features were extracted to generate a bimodal fusion descriptor. This descriptor was employed to enhance the detection capability of three common surface defects in metal AM, specifically holes, cracks and bulges. Building on this foundation, a mid-level data fusion technique was employed to integrate the scores of LIBS spectra derived from PCA with seven features extracted from plasma images, resulting in the development of a bimodal fusion approach. Subsequently, the distribution of spectral data, plasma image features and bimodal fusion descriptors was discussed. Finally, three models, namely Random Forest (RF), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA), were used to evaluate the recognition accuracy of component defects. Additionally, the application scenarios of these three different models in spectral data, plasma image features and bimodal fusion descriptors were compared. The results indicate that the LDA model, utilizing bimodal fusion descriptors, yields the most effective classification. For samples #1–#100, the accuracy increased from 99.08% and 97.94% for spectral data and plasma image features to 99.92% for fusion data. Similarly, for samples #101–#120, the accuracy increases from 97.19% and 96.21% for spectral data and plasma image features to 97.34% for fusion data. This method improves the recognition of different defects of metal AM components. This study represents a first attempt to enhance the capability of LIBS in distinguishing various surface defects of metal AM components by inputting laser plasma image data and spectral information to generate statistical descriptors. The bimodal fusion approach offers an efficient method for detecting surface defects of metal AM components, characterized by low data complexity.

The meticulous task of soil region classification is fundamental to the effective management of soil resources and the development of accurate soil classification systems. These systems are crucial for the targeted restoration, safeguarding, and enhancement of land resources. In this research, we introduce an innovative soil classification model that combines the Joint Skewness (JS) algorithm, which is grounded in tensor theory, with a Back-Propagation Neural Network (BPNN). This combination is utilized for the rapid categorization of soil samples in specified areas, making use of spectral data from Laser-Induced Breakdown Spectroscopy (LIBS). The process begins with the application of JS to identify key variables, followed by the optimization of the JS-BPNN model's structure. The effectiveness of the model is then evaluated using metrics such as the confusion matrix, Kappa coefficient, and precision, which all highlight the model's reliability. Our experimental results validate the use of JS in filtering LIBS spectral features, effectively minimizing unnecessary data while preserving the spectral data's intrinsic physical characteristics. This leads to a significant enhancement in the model's analytical capabilities. The JS-BPNN model has demonstrated remarkable classification accuracy, achieving a peak accuracy of 99.8% on the test dataset. To further validate the JS approach for reducing data dimensionality and emphasize the superiority of the JS-BPNN model, we conducted a comparative analysis with other algorithms, such as k-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM), for the classification and recognition of soil geographic regions. The results confirm that the JS algorithm is a potent method for reducing the dimensionality of LIBS spectral data, and for different classification models, there are different optimal characteristic variables, with the JS-BPNN model proving to be exceptionally effective in soil classification and recognition tasks.

The determination of rare earth elements (REEs) in seawater, especially marine sediment porewater and open-ocean seawater, is challenging because of their ultra-trace concentrations (ng L⁻¹ to pg L⁻¹) and the high salinity of the matrix (approximately 35‰ NaCl), which limits their application in marine science. Herein, we developed an online method for accurate analysis of ultra-trace REEs in seawater using a traditional Q-ICP-MS. The key aspects were: (i) high sensitivity detection in standard mode with no collision/reaction cell functioned, (ii) online automated matrix removal and preconcentration using a commercially available seaFAST system, (iii) use of membrane desolvation to enhance the sensitivity and limit the interferences of LREE oxides on HREEs, and (iv) monitoring and correction of variations in REE signal intensities caused by instrument drift using standard–samples–standard bracketing and an indium internal standard for normalization. The detection limits (0.1–8.0 pg L⁻¹) and procedural blank values (<3 pg L⁻¹ except for La, Ce, and Nd) of this method were low enough for accurate determination of REEs in seawater, even for REE concentrations at tens of picograms per liter level. The good accuracy and long-term precision (30 h, average: 3.5%, 1σ RSD, n = 10) were achieved for all the REEs as verified using certified seawater reference standards NASS-7 and CASS-6, and a 10 ng L⁻¹ artificial seawater standard, respectively. Each run required only approximately 8 mL of sample and 12 min for the measurement, which are suitable values for practical application. The developed method was used to analyze various natural seawater samples, which demonstrated its effectiveness for exploring subtle changes in REE concentrations, fractionation patterns and anomalies in different marine environments.

Fe and Mg isotopes have increasingly served as combined proxies for geological processes. Fe and Mg isotope determination requires consuming different splits of samples and multi-column chromatographic purification to obtain pure Mg and Fe fractions in conventional chemical procedures, which is time-consuming and not suitable for rare and valuable samples. This study presents a novel and efficient chromatographic procedure to purify both Fe and Mg from geological matrices, using a single column loaded with AGMP-50 resin, followed by precise measurements of Fe and Mg isotopes by multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS). In our experiment, the Fe fraction was first collected using 7 mL of a mixture of 0.2 M HCl and 0.5 M HF, and then the Mg fraction was collected using 9 mL of 1.3 M HCl. This procedure is suitable for processing different types of rock samples and enabling an Fe recovery of >98% and full recovery of Mg, with effective removal of matrix elements such as Al, Ti, Na, K, Ca, and other trace elements. Using this method, the Fe and Mg isotopic compositions of various geological reference materials were reported. All of the Fe and Mg isotopic analytical results were in agreement with the reported data within analytical uncertainties, verifying that the method established here is robust and reproducible. Thus, this procedure will serve as a great option for obtaining both Fe and Mg isotopic compositions of geological samples and tracing geochemical or astrochemical processes in the future.

Samples with very high Ca/Mg ratios present challenges for measuring their Mg isotope ratios. Here, we present an efficient method to separate Mg from samples with high Ca/Mg matrices, which also allows for quantitative separation of Sr, Ca and K. The method comprises a three-step chromatographic separation using DGA and AG50W-X12 (200–400 mesh) cation exchange resin. By utilising the automated sample purification system prepFAST MC™ for two of the three separations, the labour is substantially minimised. This analytical approach results in a quantitative Mg yield and a pure Mg solution, with other cations reduced to below the limit of detection (<53 ng mL⁻¹). We demonstrate the efficacy of this method using a set of geochemical reference materials with Ca/Mg ratios ranging from 1.32 to 1271 mol mol⁻¹. This approach enhances sample throughput and ensures high-quality separations in carbonate samples characterised by high Ca/Mg ratios.

Near-Edge X-ray Absorption Fine Structure (NEXAFS) spectra tend to be damped due to self-absorption effects when measured in fluorescence-yield mode in samples which are neither thin nor dilute. While established self-absorption correction methods are only valid for infinitely thick samples and partly inapplicable if the samples are too concentrated, the novel forward correction presented here is widely applicable, especially for intermediately thick and concentrated samples. Aiming towards quantitative analysis supporting the development of lithium sulfur battery materials, which are intermediately thick and not dilutable, the forward correction is applied to organo-sulfur liquid films as a proof of concept.