Journal of Analytical Atomic Spectrometry最新文献

Analytical techniques that offer accurate, sensitive, and high-resolution elemental mapping have significantly advanced our understanding of the role of inorganic chemistry in vital biological processes. Among these, synchrotron-based X-ray fluorescence microscopy (XFM) is a particularly powerful tool for providing reliable, non-destructive quantitation of endogenous elements in biological specimens. However, its broader application is constrained by limited beamtime availability. Recent advancements in laboratory-based imaging techniques-such as laser ablation inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOF-MS)-have significantly increased the availability and throughput of elemental mapping. Yet, quantitation with LA-ICP-TOF-MS is susceptible to matrix effects, making correlative mapping with XFM critical for validation. This presents a challenge: the two techniques require different sample preparations. LA-ICP-TOF-MS uses glass slides, while XFM requires thin, low-scatter substrates to minimize X-ray background signals. To address this, we evaluated twelve commercially available substrates previously reported for XFM to determine their suitability for LA-ICP-TOF-MS. Our goal was to identify substrates that (1) exhibit low elemental background and minimal interference with quantifying endogenous inorganic species, and (2) are compatible with both imaging modalities. As an initial step, we compared adjacent brain sections prepared on Ultralene (for XFM) and glass (for LA-ICP-TOF-MS) to establish a baseline correlative approach. Building on this, Ultralene, followed by Kapton film, emerged as the most promising candidates for enabling dual XFM and LA-ICP-TOF-MS workflows, offering low background, reliable XFM performance, and demonstrating robust elemental mapping in LA-ICP-TOF-MS. These findings support more accurate and accessible correlative imaging workflows for elemental mapping of biological samples with both modalities.

Heavy metal pollution in water poses a significant challenge in the field of environmental monitoring, representing a serious threat to both ecological systems and public health. Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as an effective tool for monitoring trace heavy metal contaminants in water due to its rapid response, in situ detection capability, minimal sample preparation requirements, and simultaneous multi-element analysis. However, conventional LIBS systems typically rely on large and costly spectrometers as core detection devices, which limits their widespread application in field-based and long-term online water quality monitoring. To address these limitations, this study proposes a compact spectral acquisition system based on a miniature spectral sensor combined with ultra-narrowband filters. By optimizing the optical structure and signal acquisition pathway, the system significantly reduces both the size and cost of the equipment. Experimental results demonstrate that, compared to traditional spectrometer systems, the proposed miniaturized system achieves a reduction in volume by three orders of magnitude and a cost reduction of approximately two orders of magnitude. In terms of detection performance, the optimized design incorporating ultra-narrowband filters enables the system to achieve a significantly lower detection limit for Ca, reaching as low as 26.9% of that obtained with conventional systems, demonstrating excellent sensitivity. For practical applications, the detection limits for heavy metal elements such as Pb and Cu meet the requirements of relevant national standards, while high sensitivity and stability are maintained. This study provides a portable, economical, and efficient solution for LIBS-based detection of heavy metals in water, substantially advancing the potential of LIBS technology for practical environmental monitoring applications, particularly in scenarios requiring on-site rapid screening and long-term online monitoring.

Highlights in the field of air analysis included: development of laboratory-based particle emission simulators to emulate real-world processes such as tyre-wear abrasion; ongoing performance verification of Hg calibrators; progress in laser and spark emission spectroscopic techniques for in situ aerosol measurements, advances in data processing software tools in supporting sp-ICP-MS measurements, feasibility of using aerosol mass spectrometers for measuring nanoplastics in air, and a comprehensive review of brown carbon aerosols, its sources, optical properties and measurement approaches used. While recent developments in water analysis include significant progress in sp-ICP-MS, including the introduction of new measurement protocols, determination of isotope ratios, and new reference materials. The number of studies employing LIBS for assessing metal burdens in water has also grown notably, particularly with respect to innovations in sample preparation. Advances were further reported in the development of portable analytical devices and systems for continuous environmental water monitoring. In addition, several comprehensive reviews were published, providing guidance for researchers on establishing robust measurement workflows, including split-stream and sp-ICP-MS methodologies. In the analysis of plants and soils, there has been increased interest in deep eutectic solvents as milder and greener alternatives to traditional extractants. Microwave plasma torch mass spectrometry methods have been developed that allowed concurrent measurement of trace elements and organic pollutants in liquid samples. Promising steps have also been taken towards application of techniques for direct analysis of solids. Advances in LIBS have largely focussed on data processing and modelling whilst in XRF, the influence of soil matrix composition on measurement accuracy was highlighted. Quantitative geochemical analysis faces continuous challenges, making the development of new RMs a persistent priority, especially for localized microanalysis. Application of LIBS is gaining increasing interest because of its portability and the use of machine learning tools to improve the quality of the obtained data. Interest is also increasing in the analysis of extraterrestrial samples. Novel ICP-MS instrumentation has offered highly precise isotopic analysis and spectral interference removal. Other important techniques in this review period have been nanoSIMS, NAA, and MS variants because they may provide new and enhanced chemical information. The fusion of data and the significantly increasing application of AI for rapid mineral identification and data integration marks a key trend that is expected to grow exponentially.

Boron (B) isotopes are a valuable tracer with applications ranging from geological, environmental, and nuclear studies because B isotopic fractionation is highly sensitive to chemical processes yielding distinct isotopic trends in natural and anthropogenic systems. Despite this wide applicability, there remain relatively few measurements on well-described reference materials and in some cases, poor agreement between various methods. We report a method for boron isotope ratio measurement in solution on the Neoma MS/MS MC-ICP-MS specifically targeting bulk silicates. We evaluate the performance of the method and instrument as it relates to the measurement of the absolute boron isotope ratio (¹⁰B/¹¹B). The results indicate that the method produces data in agreement with literature values and that the sample–standard bracketing technique is appropriate for the Neoma MS/MS MC-ICP-MS which has been in use for decades on previous generation instruments. Careful tuning of the MS/MS lenses is required to obtain precision comparable to non MS/MS equipped MC-ICP-MS. With careful tuning, internal and external precisions of ∼0.3‰ were achieved. However, when the MS/MS is not properly tuned external precisions exceed 3‰. Nevertheless, our results for IAEA B-6, BCR-2, BHVO-2 and W-2a reference materials overlap the 1σ range of previously reported ¹⁰B/¹¹B. Data are reported for total boron quantities down to a few tens of nanograms. Our procedure yielded blanks as low as 3 ng but up to 29 ng, making blank corrections important for small samples sizes in the few 10s of nanogram range. We report B isotope ratios for AGV-2G, SL-1G, GSC-2G, GSD-2G, GSE-2G, RLS-132, RLS-140, NKT-1G, and T1-G glass reference materials that have not been previously reported in the literature.

The aim of this investigation was to increase the tolerance limit of plumbane generation towards several transition metals by exploiting the hydrolysis products of borohydride – the hydridoboron intermediates (BH) – as a derivatization reagent instead of borohydride, alone or together with chemical masking agents (oxalic acid and thiourea). The BH intermediates were produced online at 0.1 mol L⁻¹ HCl sample acidity in the presence of K₃[Fe(CN)₆] (0.1 or 1% m/v) and by varying the hydrolysis coil volume (0–2000 µL) before mixing with the analyte. The simple use of BH intermediates alone obtained with 1500–2000 µL hydrolysis coil volume allowed the achievement of tolerance limits of 100 mg L⁻¹ for Ni(II), Co(II), and Fe(III) and 105 mg L⁻¹ for a mixture of Cr(III)/V(IV)/Mo(VI) (75 + 15 + 15 mg L⁻¹). The tolerance to the strong interfering action of Cu(II) was considerably improved but limited to 2 mg L⁻¹. To mitigate the interference caused by 1000 mg L⁻¹ Fe(III), BH intermediates were used in combination with thiourea and oxalic acid. However, this approach did not enhance the tolerance to Cu(II) (2 mg L⁻¹), even in the presence of the masking agents. The established conditions allowed for the accurate determination of Pb in the complex matrix of SRM 663 (Cr–V steel) after sample dissolution with inorganic acids at high temperature.

Lead in wet deposition samples collected from remote mountainous areas in Japan, which exhibited low Pb concentrations (0.0020–1.94 µg L⁻¹) comparable to those found in Antarctic snow, was preconcentrated and separated from interfering components using a solid-phase extraction (SPE) procedure with a chelating resin under non-clean-room conditions. Subsequently, Pb isotope ratios were measured by MC-ICP-MS equipped with a desolvating nebulizer, applying mass discrimination correction based on Tl isotope ratio as an external standard. For snow samples, the relative standard deviation (RSD) of ²⁰⁸Pb/²⁰⁶Pb improved slightly from 0.0037% without the SPE to 0.0028% with the SPE. In contrast, the RSD of ²⁰⁷Pb/²⁰⁶Pb showed an improvement from 0.014% to 0.0017% with the SPE. Notably, the RSD of ²⁰⁴Pb/²⁰⁶Pb improved significantly from 0.19% without the SPE to 0.032% with the SPE. For rain samples, the RSDs were 0.0042% for ²⁰⁸Pb/²⁰⁶Pb, 0.0019% for ²⁰⁷Pb/²⁰⁶Pb, and 0.024% for ²⁰⁶Pb/²⁰⁴Pb. Without the SPE, the ²⁰⁶Pb/²⁰⁴Pb ratio exhibited a large error, making it difficult to distinguish between potential sources. However, with the SPE, the ²⁰⁶Pb/²⁰⁴Pb ratio was measured with sufficient precision to enable source discrimination.

Estimating the aging state of the main pipeline steel Z3CN20-09M during its service life is critical for the safe operation of nuclear power plants. This study proposes an innovative approach combining fiber laser-based laser-induced breakdown spectroscopy (FL-LIBS) with mutual information-random forest (MI-RF) to estimate the aging grade of Z3CN20-09M steel. The spectral characteristics corresponding to ten distinct aging grades of the steel were analyzed. Considering the surface elemental inhomogeneity of alloy steels, the impact of various spectral characterization scales (SCS) on the classification accuracy of the RF-based model was investigated. The model's performance was further optimized through the application of the MI feature extraction method. Finally, the robustness of the model was evaluated under conditions of limited training data. The results demonstrate significant inhomogeneity in the distribution of elemental concentrations across the surface of the Z3CN20-09M samples. The RF model achieved optimal performance at an SCS of 1.2 mm. By extracting the first 1476 high-scoring features via mutual information, the classification accuracy of the prediction set rose to 99.0%, with notable enhancements in both precision and recall. Finally, the robustness of the MI-RF model was verified even when the number of samples obtained was insufficient. These findings indicate that the combination of FL-LIBS with MI-RF provides a promising approach for the in situ, rapid estimation of the aging state of essential metal components in nuclear facilities.

Soil heavy metal contamination poses a serious threat to agricultural product safety and public health, which urgently calls for the development of rapid and accurate in situ detection techniques. LIBS enables simultaneous multi-element analysis and requires minimal sample preparation, and has been widely applied in the field of elemental analysis. However, under practical field conditions, moisture in soil significantly interferes with the stability and intensity of LIBS signals, thereby limiting its capability for large-area, in situ, and accurate detection in real-world environments. To address this issue, this study proposes a novel approach for simultaneous multi-element quantitative analysis by integrating neural networks with physical correction strategies. Adaptive Iteratively Reweighted Penalized Least Squares (airPLS) and Random Forest were employed to optimize spectral data and screen characteristic spectral fingerprints. An ablation factor model was established to correct spectral intensity under moisture interference, and a Multi-Task Convolutional Attention Network (MT-CAN) was constructed to predict both moisture content and multiple heavy metal concentrations. The results demonstrated that the root mean square error for moisture prediction reached 0.83%, and the relative errors for simultaneous quantification of Zn, Cr, Cu, and Pb were all below 8%. Finally, a transfer learning strategy based on model parameters was adopted to further enhance the cross-regional generalization capability of the model. This study provides an effective technical foundation for achieving in situ heavy metal detection in field soil environments.

A novel analytical framework is presented that combines a physics-informed broad learning system network (PI-BLS-Net) with multimodal spectral fusion to enable rapid, low-cost, and interpretable chromium speciation in industrial tailings. The PI-BLS-Net integrates visible–near-infrared (vis-NIR) and X-ray fluorescence (XRF) spectral data, transforming one-dimensional spectra into two-dimensional images via Gramian Angular Field (GAF) techniques to enhance feature extraction. Discriminative features are learned from the fused spectral images using a Principal Component Analysis Network (PCANet), with key geochemical parameters (measured pH and Eh) and physicochemical constraints—such as redox equilibrium and mass balance based on the Nernst equation—explicitly embedded into both the model architecture and its loss function. This approach enables accurate quantification of Cr(III) and Cr(VI) concentrations in rare earth tailings, supporting scientific waste classification and risk assessment. Validation on 218 tailings samples demonstrates that the PI-BLS-Net achieves excellent performance in classifying tailings hazard based on chromium speciation, with an accuracy of 89.5%, F1-score of 89.0%, and area under the receiver operating characteristic curve (AUC) of 0.95 on an independent test set. Ablation studies further confirm the significant contributions of spectral-to-image transformation, multimodal fusion, PCANet feature extraction, and especially the physics-informed module to overall model performance. This work provides a rapid, interpretable, and robust approach for pollutant speciation analysis in complex matrices, offering valuable technical support for the scientific management and environmental risk assessment of Cr-bearing tailings.

Laser-induced breakdown spectroscopy (LIBS) is an ideal method for the online elemental analysis of Laser Powder Bed Fusion (LPBF) components due to its in situ and rapid characteristics. However, the inherent surface roughness of LPBF-fabricated parts causes fluctuations in LIBS spectral signals, consequently affecting the accuracy and stability of quantitative analysis. The underlying physical mechanism by which roughness influences spectral signals remains unclear, which restricts the development of relevant spectral correction algorithms. To address this, we prepared alloy steel samples with surface roughness (S_a) ranging from 4.52 to 12.82 µm by adjusting LPBF process parameters and performed LIBS analysis. By analyzing spectral intensity, signal-to-noise ratio (SNR), and relative standard deviation (RSD), we found that the characteristic spectral line intensities of Fe, Cr, Mn, and Ni initially increased and then decreased with increasing roughness, reaching a peak at S_a = 5.61 µm. This peak intensity was 46.4% higher than that of the roughest sample (S_a = 12.82 µm). Plasma temperature and electron density also reached their maximum values at S_a = 5.61 µm (15 790 K and 1.84 × 10¹⁷ cm⁻³ respectively). The observation results of the volume morphology of the ablation plume and the ablation pit both indicate that roughness affects the LIBS signal through a triple coupling effect: low roughness (S_a = 4.52 µm) leads to energy loss due to high reflectivity, while high roughness (S_a ≥ 7.15 µm) weakens ablation efficiency due to an increased ablation threshold and non-uniform energy distribution caused by microstructures. The sample with S_a = 5.61 µm represents an optimal balance between reflectivity and ablation threshold, exhibiting the largest integral of plume area and time (IPAT) and largest volume of ablation pits uniform ablation, which generates stable plasma and, consequently, high-quality spectral signals. This study elucidates the physical mechanism by which roughness influences LIBS spectral signals through a chained pathway of “laser ablation-plasma evolution-spectral response,” laying a theoretical foundation for the development of spectral correction algorithms tailored for complex LPBF surfaces.