Journal of Analytical Atomic Spectrometry最新文献

Resonance ionization mass spectrometry of gadolinium can be used for nuclear forensics and to further the understanding of stellar nucleosynthesis but has been used only a handful of times due to the high laser power required and interference from non-resonant ionization of molecules of other elements. Herein we present the development of two novel resonance ionization spectroscopy schemes for gadolinium that provide improvements in isotopic fractionation and ionization efficiency, respectively, opening new applications for gadolinium analysis. The schemes are demonstrated and compared in a mixed sample of gadolinium and neodymium.

The performance of a new inlet system, the Nu Sil, for the automated determination of the isotopic abundances of silicon in SiF₄ gas generated from the thermal decomposition of BaSiF₆ is reported. The inlet system is coupled to an isotope ratio mass spectrometer through a conventional dual inlet system. The method uses straightforward and proven sample preparation chemistry that is suitable for converting the silicon in biogenic and lithogenic solids, or that dissolved in fresh or salt waters, to BaSiF₆. Yields of silicon tetrafluoride are 99.8 ± 0.16%. δ³⁰Si values obtained with the method for a variety of natural and synthetic materials agree with published values to better than 0.05‰. External long-term average δ³⁰Si values of the solid standard NBS28, the secondary standards Big Batch and diatomite, and the reference seawater ALOHA₁₀₀₀ are all within 0.03‰ of their respective consensus values. Typical loading of BaSiF₆ represents 10 to 40 μg of Si. The analytical rate is 30 unattended analyses daily.

Selenium (Se) and arsenic (As) are essential or toxic trace elements that require sensitive on-site monitoring. However, conventional atomic spectrometric techniques such as inductively coupled plasma atomic emission spectrometry (ICP-AES) are restricted to laboratory use due to high power consumption and bulky infrastructure. This study developed a field-deployable analytical system by integrating a self-designed, lithium-ion battery-powered microplasma excitation source with an optimized hydride generation module, resulting in a miniaturized atmospheric pressure glow discharge atomic emission spectrometer (HG–APGD-AES). The instrument achieves laboratory-grade analytical sensitivity under ultra-low resource consumption conditions. Under optimized conditions for the HG system and APGD excitation system, detection limits of 0.24 μg L⁻¹ for As and 0.36 μg L⁻¹ for Se were achieved, with excellent linearity (R² > 0.998) and precision (<1.5% RSD, n = 10). The spectrometer's performance was validated using certified reference materials (BWB2261-2016, BWB2007-2016F) and natural water samples from the Yangtze River, Tushan Lake, and Binan River. Results demonstrate high accuracy, stability, and analytical efficiency, supporting the spectrometer's suitability for on-site environmental monitoring of Se and As.

This study comprehensively characterizes the natural chromite reference material UG1-W and establishes a methodological framework for its quantitative LA-ICP-MS analysis. Integrated mineralogical and geochemical analysis confirms exceptional homogeneity and validates optimized calibration approaches for UG1-W. High-resolution BSE imaging combined with automated mineralogy (TIMA) reveals a chromite–plagioclase dominated microstructure (80.8% chromite and 17.3% plagioclase) with minor accessory phases. Multi-analytical methods (LA-ICP-MS, EPMA, ICP-OES, ICP-MS, and TXRF) are employed to verify the homogeneity of both major and trace elements. Systematic calibration assessments yield three key findings: (1) persistent matrix-induced analytical biases occur when using synthetic glass standards; (2) calibration strategies yield divergent results despite employing internal standards; (3) matrix-matched calibration achieves superior accuracy for chromite analysis, exhibiting relative deviations below 5% against certified values. Collectively, this work establishes UG1-W as a homogeneous chromite reference material and unequivocally demonstrates the necessity of matrix-matched standardization for accurate LA-ICP-MS analysis of chromite. These findings significantly improve the measurement accuracy for refractory mineral systems and provide a robust analytical framework for geochemical studies of chromite-bearing lithologies.

Herbals play a crucial role in maintaining human health owing to their therapeutic properties. However, herbals with excessive copper (Cu), manganese (Mn), and lead (Pb) are common due to pollution. Conventional detection methods for toxic elements are time-consuming and prone to contamination. Laser-induced breakdown spectroscopy (LIBS), a technology based on plasma analysis, offers rapid detection. Combining LIBS with chemometrics has become a popular approach for herbal detection. However, identifying toxic elements remains challenging due to difficulties in extracting relevant spectral variables. This study proposed using normalized mutual information (NMI) for variable extraction, while the student psychology-based optimization (SPBO)-kernel extreme learning machine (KELM) was used to classify herbals based on Cu, Mn, and Pb contents. The results showed that the number of extracted variables was only 0.018%, 0.073%, and 0.66% of total variables. Compared with principal component analysis-KELM, NMI-KELM improved average accuracy and F₁ by 5.00% and 2.87%. With SPBO optimization, NMI-KELM's average accuracy and F₁ increased to 94.00% and 93.14%. This study provided a foundation for the rapid and accurate classification of herbals based on toxic element content.

Boron isotopes serve as a effective tracers for fluid-related geological processes. Tourmaline is a boron-rich mineral, making it an ideal medium for B isotopic studies. However, matrix effects, particularly instrumental mass fractionation (IMF), can significantly affect the accuracy of B isotope analysis performed using secondary ion mass spectrometry (SIMS). Conventional correction methods typically rely on offline coupling of major element contents determined via electron probe microanalysis (EPMA) and B isotope ratios measured using SIMS; however, these methods are time-consuming and susceptible to spatial mismatch. This study introduces an online matrix effect correction method using NanoSIMS, eliminating the need for EPMA data. B isotope analysis revealed a strong linear correlation (R² > 0.93) between IMF and the FeO^T + MnO content of tourmaline, suggesting that Fe/Mn substitution is likely the primary factor governing IMF. Subsequently, an online matrix correction for the B isotope ratio was established by simultaneously measuring the ⁵⁸Fe⁺/¹⁰B⁺, ⁵⁵Mn⁺/¹⁰B⁺ ratios and the B isotope ratio (¹¹B⁺/¹⁰B⁺), utilizing a binary linear regression model. Nine tourmaline reference materials with diverse compositions were analyzed and corrected using this online correction method, yielding δ¹¹B values that are consistent with the recommended reference values within the uncertainty range. Overall, this approach enhances analytical efficiency and reliability, enabling high-precision B isotope tracing in complex geological processes.

Barium (Ba) isotopes have emerged as powerful tracers in geochemical, environmental, and cosmochemical studies. However, achieving high-precision Ba isotope measurements remains challenging due to matrix removal, procedural blanks, isotopic ratio measurement uncertainties, and accurate mass bias correction. Here, we develop a robust analytical protocol for δ^137/134Ba determination using a ¹³⁰Ba–¹³⁵Ba double spike on a Nu Plasma II MC-ICP-MS. Our method employs an in-tandem micro-column chromatography (AG50-X12 cation-exchange resin followed by Sr-Spec™ resin) to efficiently purify Ba from matrix elements with minimal acid consumption. By eliminating intermediate evaporation and re-dissolution steps, we achieve rapid purification of Ba with a procedural blank of only 278 pg, negligible for most geological samples. Both MATLAB simulations and experimental validation suggested an optimal of ∼20% double-spike proportion in the spike-sample mixture. Additionally, we found that a 200 ppb Ba concentration balances sample consumption, signal intensity and Faraday cup performance. To further refine sampling strategies and minimize isobaric interferences, we mapped the spatial distributions of Ba and Xenon (Xe) ion intensities, and isotope ratios in the ICP both in wet and dry plasma conditions, identifying a stable plasma region where Ba isotope ratios show minimal variability and Xe interference is low. We demonstrate that even trace matrix elements (a few millivolts in intensity) can significantly impact the precision in isotope ratio measurements. The method achieves a long-term external reproducibility better than 0.03‰ (2SD). Analyses of twelve geological reference materials (AGV-2, BCR-2, BHVO-2, BIR-1a, COQ-1, DTS-2B, GSO-2, GSP-2, GSR-8, JF-1, RGM-2, and SCo-1) yield δ^137/134Ba values consistent with published data except for three previously unreported materials (DTS-2B, JF-1, and SCo-1), confirming the reliability of the proposed method. This protocol provides a robust foundation for the mechanism of ion interaction in the ICP and contributes to high-precision Ba isotope applications across diverse geological processes.

Scheelite is a common accessory mineral and an essential component of tungsten ores. Its U–Pb dating, chemical compositions, and oxygen isotopes provide critical information on the timing and genesis of scheelite-bearing ores and the nature and evolution of the ore-forming fluids. In situ analysis by secondary ion mass spectrometry (SIMS) is the only option for oxygen isotope investigation of natural scheelite crystals, as they commonly exhibit complex growth zoning and contain inclusions. However, there is a current lack of well-characterized scheelite reference materials for SIMS oxygen isotope analysis. This study thus characterizes two natural scheelite samples (XBD-1 and PT-1) as working reference materials for in situ oxygen isotopic analysis of this mineral by employing secondary ion mass spectrometry (SIMS). Our SIMS analyses reveal that both XBD-1 and PT-1 scheelite samples exhibit homogeneous oxygen isotopic compositions with their 1SD being 0.2‰ (n = 117) and 0.3‰ (n = 101), respectively, supporting their use as reference materials for high-precision SIMS δ¹⁸O analysis of scheelite. Laser fluorination isotopic ratio mass spectrometry yields mean δ¹⁸O values of 8.57 ± 0.20‰ (1SD, n = 2) for XBD-1 and −6.21 ± 0.20‰ (1SD, n = 3) for PT-1, which are recommended as reference oxygen isotopic values of these materials.

Heavy metal concentrations in soils near smelters are critical indicators for assessing soil quality, ecological risks, and potential health threats. However, accurate monitoring remains challenging due to soil matrix complexity and limited labeled spectral data. This study presents a semi-supervised learning framework based on a teacher–student model combined with GraphSAGE. The approach incorporates intra-group consistency constraints to map laser-induced breakdown spectroscopy (LIBS) spectra to the concentrations of Cr, Cu, Cd, Pb, and Zn. Spectral data were preprocessed using Savitzky–Golay filtering, normalization, feature selection, and PCA. The resulting components served as inputs to the model. Under the fully labeled dataset, the GraphSAGE-based framework outperformed conventional sequence models (LSTM and Transformer), achieving lower mean absolute percentage error (MAPE), generally reduced root mean squared error of prediction (RMSEP), and improved precision reflected by a lower mean relative standard deviation (mean RSD) on the labeled test set. By integrating unlabeled samples via the semi-supervised strategy, the teacher–student framework further improved model robustness and predictive stability, lowering MAPE to 5.23% (Cu), 2.73% (Cr), 5.82% (Pb), 4.90% (Zn), and 6.29% (Cd), with corresponding reductions in RMSEP and mean RSD. Finally, the optimized model mapped heavy metal distributions across the study area. Concentrations were high near the smelter and peaked in downwind zones. These patterns align with the atmospheric transport of smelter-derived particulates, confirming their dominant role in dispersion. The proposed method offers a practical tool for environmental monitoring and supports precision remediation strategies.

Numerous methods and techniques have been developed over time in gamma spectrometry to analyze spectra using specialized software. Review-type publications in gamma spectroscopy generally do not present certain methods that nevertheless exist and are used. This article presents a collection of various approaches, both old and new, applied at each stage of gamma spectrum analysis to identify and quantify the radioisotopes present in a sample, while also discussing the strengths and limitations of each method. The objective is to offer the reader a clear and structured view of the available tools, thereby facilitating the development of more efficient and accurate analysis software. It covers the entire process, from the raw spectrum to the final data interpretation, including peak detection, centroid fitting, peak shape correction, 511 keV peak correction, coincidence correction, radioisotope identification, and activity calculation. Finally, several data resources developed by our team, including source code, are freely available to support developers in designing or improving spectral analysis software, particularly for HPGe detectors.