Journal of Analytical Atomic Spectrometry最新文献

Tungsten impurities deposited on the plasma-facing components (PFCs) of the inner walls of tokamak devices pose a significant risk to steady-state operation and safety. Nevertheless, quantitative chemical analysis of the co-deposited layer on PFCs is a critical task. Laser-induced breakdown spectroscopy (LIBS) is a promising technique for assessing the deposition of impurities on PFCs, enabling in situ monitoring, real-time analysis, and simultaneous detection of all elements. However, the accuracy of the quantitative analysis is affected by the material matrix. Diffuse reflectance spectroscopy (DRS) provides complementary information about the material matrix, such as optical properties. Herein, we present a novel data fusion model of LIBS and DRS techniques, providing more accurate results compared to models based on each of these single methods. 66 standard samples were prepared to simulate the impurities deposited on PFCs in EAST. 15 samples were randomly selected as the prediction set, while the remaining 51 samples were used as the calibration set. The average relative errors of the data fusion model for W, Li, Fe, and O were 2.23, 1.95, 2.08, and 0.39%, respectively. Compared with the single LIBS data model, the root mean square errors of prediction (RMSEP) for the four elements W, Fe, Li, and O in the data fusion model were reduced by 19.4, 18.5, 21.4, and 20.9%, respectively. The results show that the fusion of LIBS and DRS data enables improved quantitative analysis of co-deposited impurities on PFCs.

Accuracy of the in situ major element analysis of silicate glasses is critical for insights into magmatic evolution, anatectic processes, and melt–fluid interactions. Electron probe microanalysis (EPMA), a widely used technique for microanalysis, faces limitations due to alkali ion migration (particularly Na⁺ and K⁺) under high current densities typical of micron-beam spots. Furthermore, the correction procedures are constrained by standard availability and reference data. This study addresses these challenges by analyzing a suite of H₂O-bearing aluminosilicate glass standards using a JEOL JXA-8530F EPMA under the optimized conditions of 1 μm beam spot size, 15 kV accelerating voltage, 1–5 nA beam currents, and counting times of 10 s on peak and 5 s on background. Our results demonstrated that Na₂O loss correlated linearly with both current intensity and H₂O content, exhibiting consistent proportionality across varying water contents and current conditions. In contrast, K₂O loss exhibited a threshold-dependent behavior, exhibiting a significant loss (≥5% loss) only in glasses with ≥4 wt% H₂O or under higher beam currents (≥3 nA). Notably, Na⁺ migration occurred more readily than K⁺ migration under identical analytical conditions. The observed alkali depletion was accompanied by an increase in Al₂O₃ and SiO₂ concentrations. These findings indicate that alkali mobility is controlled by both external factors (beam parameters) and internal conditions (specifically, glass composition, with volatile content playing a particularly important role). To minimize the impact of measurement variablility, we developed a correction protocol utilizing standard-derived calibration factors based on measured, analyzed and known concentration ratios. We recommended optimal analytical conditions (1 μm spot beam and 3–4 nA current) combined with matrix-matched H₂O-bearing standards. This methodology maintains the superior spatial resolution of EPMA while significantly improving the analytical accuracy of H₂O-bearing glasses. This approach is especially advantageous for analyzing minute melt inclusions in minerals and experimental melt quench products.

Lunar soils are rare but have great importance for unraveling the evolutionary history of the Moon. The identification of various minerals in lunar soil is vital to explore petrology, geochronology and isotope chemistry of the Moon. However, the rapid and accurate identification of lunar minerals remains technically challenging. Although an automated mineral identification (AMI) method was initially developed to perform mineral identification, the accuracy and analytical conditions of the AMI method are not well-defined. Other microbeam methods, such as electron probe microanalysis (EPMA), Raman spectroscopy (RS) and transmission electron microscopy (TEM), have also been used to identify minerals with various sizes. The applicability of these analytical methods is unclear due to complex mineral phases and diverse grain sizes. In this paper, three Chang'e-5 lunar basaltic clasts were systematically investigated using AMI, EPMA, RS and TEM techniques. Monte Carlo simulation on various minerals was conducted for the first time to optimize the analytical conditions (e.g. accelerating voltage and step size) of the AMI method, which could promote the quick identification of specific lunar minerals. By comparing the minerals identified by AMI, EPMA and RS, the reliability of the AMI method was well validated. Finally, based on the characteristics and applicability of the four methods, an AMI-RS-TEM technical route for identification of lunar minerals was established. This study provides an optimal method for the rapid and accurate identification of lunar minerals, and could also offer valuable insights into mineral identification in other extraterrestrial precious samples (e.g. asteroidal materials and meteorite samples).

Well-characterized reference materials (RMs) are essential for advancing laser ablation (LA) U–Pb carbonate geochronology, yet Quaternary carbonate RMs with stringent accuracy constraints are still lacking. Here, we present a comprehensive inter-laboratory study of the speleothem sample SB19 (U: 3.5 ± 1.8 μg g⁻¹) employing both LA and isotope dilution (ID) methods. LA-MC-ICPMS analyses of the stratigraphically distinct subsamples demonstrate excellent inter- and intra-laboratory reproducibility. High-precision measured δ²³⁴U data were also obtained for SB19, allowing accurate corrections for the initial disequilibrium. The resultant chronology reveals a robust correlation between the SB19 δ¹⁸O record and theoretical solar insolation curves on the precession scale, providing a verification of its age accuracy (±6 kyr, 2σ). Notably, (²⁰⁷Pb/²⁰⁶Pb)₀ ratios are quite similar among subsamples (RSD < 0.5%), which can be used as a monitoring reference for studying the initial lead isotope composition and is applicable to related research such as paleoclimate reconstruction. Syntheses of ID (n = 15) and LA datasets of the SB19 yield recommended values: age = 1.091 ± 0.006 Ma and (²⁰⁷Pb/²⁰⁶Pb)₀ = 0.809 ± 0.004. SB19 meets the stringent requirements of LA carbonate U–Pb dating RMs (∼0.5% relative uncertainty in age and initial Pb isotopic ratios at 2σ), particularly for dating Quaternary speleothem samples. This integrated work establishes a new paradigm for carbonate RM validation by combining radiometric dating, geochemical proxies, and insolation tuning.

Recent studies have shown that the sulfur isotopic composition of apatite can be determined using secondary ion mass spectrometry (SIMS). However, the matrix effect during SIMS analysis of sulfur isotopic composition of apatite has not been well constrained. We investigate this topic by analyzing a set of reported apatite standards (i.e., Big1, SAP1, Durango-B, Hormuz and Mdg-1) with various sulfur concentrations (160–3003 ppm). Our results show that the instrumental mass fractionation (IMF) is negatively correlated with sulfur concentration, indicating that there is a significant matrix effect during sulfur isotope determination of apatite using SIMS. But fortunately, the matrix effect can be corrected using the linear correlation between IMF and sulfur concentration. Using the method reported in this study, we investigated the potential of using the sulfur isotopic composition of bioapatite to reconstruct the sulfur isotopic composition of ancient seawater. Two bioapatite samples collected from modern marine sediments were analyzed. Both samples show significant variation in their δ³⁴S values, and both samples have average δ³⁴S values lower than that of modern seawater. The data can be explained if sulfur sourced from reoxidation of the sulfide produced by bacterial sulfate reduction (BSR) and evolved seawater sulfate in porewater was incorporated into bioapatite during early diagenesis. This means that the primary sulfur isotope signal in bioapatite was possibly altered during early diagenesis, and the sulfur isotopic composition of bioapatite cannot directly be used to reconstruct the sulfur isotopic composition of ancient seawater. However, we note that one bioapatite has δ³⁴S values very close to that of modern seawater, indicating that the primary sulfur isotope signal may be retained in well preserved bioapatite samples. Future research is required to identify the exact location where the sulfur isotopic composition remains unaltered, prior to utilizing the sulfur isotopic composition of bioapatite to reconstruct the sulfur isotopic composition of ancient seawater.

High-precision metal(loid) isotope abundance ratios are a powerful research tool across various disciplines. These ratios are typically measured using multi-collector mass spectrometry with ion sources such as gas source, thermal ionization, or inductively coupled plasma (i.e., IRMS, TIMS, and MC-ICP-MS). This study presents the first integration of the recently developed microwave inductively coupled atmospheric-pressure plasma (MICAP) ion source, which sustains a plasma using N₂, with a multi-collector mass spectrometer and offers the first characterization of the resulting MC-MICAP-MS instrument for high-precision metal isotope abundance ratio measurements. The performance of the MC-MICAP-MS instrument was evaluated by measuring Sr isotope abundance ratios and directly comparing the results with those obtained using established technology (i.e., MC-ICP-MS) with an Ar-ICP as the ion source. Initial results using the MICAP ion source show that the ⁸⁷Sr/⁸⁶Sr intensity ratio precision (approx. 0.007%) and the repeatability of the ⁸⁷Sr/⁸⁶Sr intensity ratio (approx. 0.010%), as well as the intermediate precision of the conventional ⁸⁷Sr/⁸⁶Sr isotope abundance ratio (approx. 0.0013%) are fully comparable to those of conventional MC-ICP-MS systems. The instrumental isotopic fractionation (IIF) observed for the new MC-MICAP-MS instrument was predominantly mass-dependent for Sr. This allowed the successful application of common IIF correction strategies, such as internal normalisation and standard-sample bracketing, for the determination of Sr isotope abundance ratios. The conventional ⁸⁷Sr/⁸⁶Sr isotope abundance ratios and δ⁸⁸Sr/⁸⁶Sr_SRM987 values measured for various geological and biological reference materials (i.e., seawater, basalt, slate, and bone) using MC-MICAP-MS were consistent with previously reported values obtained from established technologies such as TIMS and MC-ICP-MS. Overall, this study demonstrates that MICAP is an applicable and viable alternative ion source for multi-collector mass spectrometry, maintaining both double-focusing properties and high-precision performance without compromising the accuracy and reliability of the measurement results.

To address the limited accuracy in quantitative elemental analysis caused by insufficient integration of multi-energy state X-ray fluorescence (XRF) spectral data, an effective deep learning method is proposed to optimize element quantitative analysis. This method constructs a novel Multi-energy State Attention Fusion Network (MSAF-Net). Firstly, to prevent important peaks from being obscured by noise, a Spectral Feature Extraction Module (SFEM) is proposed to adaptively weight spectral data, enhancing meaningful peaks while suppressing background interference. Secondly, to ensure balanced information integration across energy states, a Dynamic Fusion Scoring Module (DFSM) is developed to learn and apply distinct weights to each state and evaluate the fused output through a pre-training scoring mechanism. Finally, a two-stage optimization strategy is implemented to overcome local optima and promote comprehensive information sharing during model training: individual pre-training of each energy branch followed by constrained joint training, yielding stable and cumulative performance improvements. Transfer learning was employed to evaluate network generalization. The model was trained on 9855 simulated soil spectra and validated using 118 field samples. Compared to other advanced models, MSAF-Net achieved the highest coefficients of determination (R²) of 0.9832, 0.9844, 0.9891, 0.9695, 0.9854, and 0.9801 for Si, Al, Fe, Mg, Ca, and K, respectively, each with a Ratio of Performance to Deviation (RPD) above 7.5. Heavy metal concentrations were predicted with comparable fidelity, with a mean R² above 0.98, demonstrating excellent fit quality and robust error control. These results establish MSAF-Net as an efficient and reliable tool for quantitative elemental analysis in XRF spectroscopy.

Non-invasive and multi-analytical approaches are crucial for analyzing cultural heritage artifacts, particularly for rare and fragile specimens that must be studied in situ. The disadvantage of non-invasive techniques is their lower sensitivity and the limited information that can be collected without sampling the artifact; however, they allow the collection of multiple data sets on the same specimen. Besides, non-invasive techniques can collect more sample points or even a map on the same artifact, getting information about the whole object, considering its inhomogeneities due to possible alterations, without being biased by the chosen points. In this work, we employ an integrated instrument capable of simultaneously acquiring X-ray fluorescence and reflectance mapping in the visible-short wave infrared range to analyze orichalcum powder samples, then discussing the results in comparison with the analysis carried out using both in situ non-invasive techniques and laboratory-based non-destructive methods. These reference samples, derived from ingots recovered from a 6th-century BC shipwreck discovered off the coast of Gela, serve as a controlled dataset to validate the performance of the combined mapping approach. The aim is to assess the potential of this dual-modality system, enabling a comprehensive, bulk, and surface, characterization for future in situ applications to the ingots to check and follow the surface degradation phenomena, without the need for sampling.

Advanced oxidation processes (AOPs) are increasingly adopted in wastewater treatment to degrade persistent pollutants, including emerging targets such as microplastics (MPs). These particles enter aquatic systems through the fragmentation of bulk plastics and, as their size decreases, exhibit enhanced mobility, surface reactivity, and biological uptake potential. However, the efficiency of AOPs in removing MPs and their nanoscale derivatives (nanoplastics, NPs) remains poorly understood, partly due to the lack of suitable analytical tools. Small MPs and NPs often occur at trace levels and are obscured by colloidal and dissolved background in complex matrices. Moreover, growing evidence suggests that AOPs may promote fragmentation rather than complete degradation. Thus, the focus of this study is to investigate ozone as a reactive agent for MP degradation, using single-particle inductively coupled plasma – mass spectrometry (SP ICP-MS). The formation of nanoscale plastics was qualitatively assessed using dynamic light scattering (DLS). The degradation behaviour of primary MPs such as polystyrene (PS) and polytetrafluoroethylene (PTFE), and secondary MPs generated from bulk poly(methyl methacrylate) (PMMA) and polyvinyl chloride (PVC) was assessed. Ozone exposure led to progressive mass reduction for PS and PMMA, while PTFE and PVC showed greater oxidation resistance. SP ICP-MS revealed detailed transformations in mass, which were projected into size distributions, while DLS confirmed the formation of nanoscale particles in all cases. These findings highlight that ozone-based AOPs can promote nanoplastic formation, underscoring the need to evaluate treatment efficiency not only by particle removal but also with regard to the nature and behaviour of transformation products. The combined use of SP ICP-MS and DLS offers unique insights into MP degradation and the unintended formation of NPs during oxidative treatment, an aspect of particular relevance as AOPs are increasingly integrated into wastewater treatment under the revised European Urban Wastewater Treatment Directive (2024/3019).

The increasing use of nanomaterials (NMs) in materials science, health, and technology has raised concerns regarding their environmental distribution, stability, and toxicity. Iron oxide nanoparticles (IONPs) are notable for their widespread applicability and reactivity. In this context, developing simple, rapid, and environmentally friendly methods for separating and quantifying them is essential. This study proposes a cloud point extraction (CPE) method for the selective separation of hematite nanoparticles (HemNPs) from ionic iron, allowing for their subsequent quantification using flame atomic absorption spectrometry (FAAS). The synthesized HemNPs utilized in this study exhibited 61% crystallinity, forming pseudo-spherical aggregates with a size of 68 ± 15 nm, an individual particle size of 5 ± 1 nm, and a hydrodynamic diameter of 10 ± 1 nm. The optimized CPE conditions involved Triton X-100 (5% v/v) as the nonionic surfactant, EDTA as the complexing agent, a pH of 5, and 0.15 mol L⁻¹ of CaCl₂ to lower the cloud point temperature. Under these conditions, HemNPs were effectively separated, with recoveries ranging from 85.7% to 103.4% and an enrichment factor of 5. The method was also applied to real water samples, where HemNPs were not quantified, and spike recovery tests showed values above 70%, demonstrating the method's efficiency. To the best of our knowledge, this is the first report combining CPE with FAAS for the determination of HemNPs, providing a cost-effective, solvent-free, and robust alternative for monitoring iron-based nanomaterials in aqueous matrices.