Journal of Analytical Atomic Spectrometry最新文献

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.

Underwater laser-induced breakdown spectroscopy (underwater LIBS) is gaining increasing attention as an on-site analytical technique available in water-confined environments, such as the deep sea. Use of a solid substrate in underwater LIBS is beneficial for detecting dissolved elements. In this study, we demonstrate the first use of porous silicon (Si) as a substrate for underwater LIBS, which is fabricated by metal-assisted etching (metal-assisted chemical etching) using silver nanoparticles. A long-nanosecond laser (30 ns) operating at a wavelength of 1064 nm and a low energy of 1.5 mJ was focused onto a substrate immersed in a 0.6 mol per L sodium chloride aqueous solution containing 2.5 ppm (mg L⁻¹) lithium (Li). By using porous Si instead of flat Si as the substrate, the spectral line intensity of Li was enhanced by 8.0 times. A linear calibration curve with a coefficient of determination of 0.999 was obtained using porous Si in the range of Li concentration from 0.5 to 10.0 ppm. This study suggests a new potential approach for utilizing porous Si and extends the applicability of LIBS to liquid analysis.

The rapid advancement of artificial intelligence (AI), particularly machine learning (ML), is revolutionizing stable isotope geochemistry, enhancing mass spectrometry-based analytical techniques and unlocking transformative capabilities in data interpretation and geochemical process modelling. This critical review offers a comprehensive synthesis of the integrated applications of ML in this field, encompassing a systematic survey of the commonly employed algorithms—such as random forests, support vector machines, and neural networks—along with an in-depth examination of their geochemical uses. We classify and evaluate these methods, highlighting their roles in improving the data processing efficiency, prediction accuracy, and mechanistic insight across diverse applications including provenance studies, paleoclimate reconstruction, and environmental forensics. Nevertheless, several pressing challenges impede their broader implementation, such as data scarcity for non-traditional isotope systems, limited model interpretability, and the persistent risk of geochemically implausible predictions. We argue that overcoming the “black-box” nature of ML demands the integration of domain knowledge through physics-informed neural networks (PINNs), improved explainable AI (XAI) frameworks, and strengthened interdisciplinary collaboration. Looking ahead, we emphasize the need to optimize the analytical accuracy through intelligent instrumentation, develop standardized data infrastructures, and foster algorithm innovation tailored to geochemical principles. This review aims to provide an authoritative reference by synthesizing the recent advances, openly addressing the current limitations, and outlining pragmatic research directions to accelerate the adoption of ML in stable isotope geochemistry. By tackling these priorities, ML stands to not only refine the existing methodologies but also open new scientific frontiers in understanding the Earth's dynamic systems, ultimately revolutionizing isotope-enabled discovery.

This update covers the literature published between approximately June 2024 and April 2025 and is the latest part of a series of annual reviews. It is designed to provide the reader with an overview of the current state of the art with respect to the atomic spectrometric analysis of various metals, polymers, electronic, nano and other materials. Data processing continues to be the major focus for LIBS and TOF-SIMS analyses, mainly to provide reliable analyte quantification data. A variety of machine learning algorithms and statistical approaches have been used for this, often in multiple steps. Although these algorithms have been used for some years, their use is expanding into new areas. Another development is the combination of complementary techniques on the same instrument platform. This enables data from the two techniques to be obtained simultaneously and from the same spot on the sample. The analysis of polymers and nanomaterials continues to develop, with the prominent platforms used being SP-ICP-MS, SP-ICP-TOF-MS and X-ray based techniques. In addition, efforts are now being accelerated to produce nanomaterial CRMs and RMs, the lack of which has hampered truly robust method validation. For electronic materials XPS, GIXRF, GEXRF and TOF-SIMS remain dominant for surface and depth profiling, whilst for bulk composition LIBS, ICP-MS, and XRF remain prominent. Work in this area is also focussing on the development of advanced sample preparation and microextraction approaches that expand the scope of laser-based spectroscopy.

Single particle inductively coupled plasma-mass spectrometry (sp-ICP-MS) produces time series—time scans—that require processing to extract meaningful information regarding the nanoparticles that are analysed. In this work, we present a stochastic algorithm to generate such time scans. We also introduce an open-source implementation of it, , a Python library designed to generate synthetic, yet realistic sp-ICP-MS time scans for a single mass-to-charge ratio. We argue that our library is an efficient and reliable testbed on which future studies can build to assess existing or new data processing strategies.

Niobium (Nb) is a critical strategic metal essential for advanced industrial and aerospace technologies. As aeschynite represents one of the primary natural sources of Nb, its geochronological characterization is of significant interest. In situ microbeam geochronology of aeschynite can provide a direct approach to constraining the timing of niobium mineralization and regional geological evolution. Accurate and precise in situ microbeam dating using SIMS or LA-ICP-MS requires well-characterized matrix-matched reference materials to correct for matrix-induced elemental fractionation. However, a critical gap exists due to the current absence of natural aeschynite reference materials. To address this deficiency, we propose Hidra aeschynite-(Y) from the Urstad Feldspar Mine, Norway, as a candidate of natural reference material for Pb–Pb and Lu–Hf dating. Multiple SIMS spot analyses demonstrate that it has a homogeneous age, with an arithmetic mean ²⁰⁷Pb/²⁰⁶Pb age of 902.1 ± 21.3 Ma (2SD; n = 46), corroborated by three LA-ICP-MS/MS analytical sessions (arithmetic mean ²⁰⁷Pb/²⁰⁶Pb age = 902.5 ± 22.5 Ma, 2SD; n = 55). Six ID-TIMS analyses yield an arithmetic mean ²⁰⁷Pb/²⁰⁶Pb age of 912.8 ± 6.9 Ma (2SD), recommended as the best estimate of the crystallization age. In addition, in situ LA-ICP-MS/MS Lu–Hf analyses of Hidra aeschynite-(Y) calibrated with NIST SRM 610 yield an isochron age of 912.5 ± 6.5 Ma (2σ; MSWD = 1.4; n = 68). The convergence of microbeam ²⁰⁷Pb/²⁰⁶Pb ages with the high-precision ID-TIMS benchmark, combined with reproducible Lu–Hf results, establishes Hidra aeschynite-(Y) as the first potential matrix-matched reference material for microbeam in situ Pb–Pb and Lu–Hf geochronology of aeschynite.