Journal of Analytical Atomic Spectrometry最新文献

Laser-induced breakdown spectroscopy (LIBS) suffers from shot-to-shot fluctuations that constrain signal intensity and stability. While spatial confinement effectively enhances emission signals, the quantitative relationship between plasma plume morphology and spectral characteristics remains unclear, limiting mechanistic understanding and optimization strategies. This study establishes a three-tier hierarchical correlation framework connecting spectral characteristics, global plasma morphology and local morphological features using Pearson and Spearman correlation analysis. We employed optical emission spectroscopy (OES), high-speed photography, and shadowgraphy to analyze with plate wall spatial confinement (PWSC), correlations between morphological parameters (axial length, radial length, plume area, total integrated image intensity, average pixel intensity, region of interest integrated intensity and axial-to-radial ratio) and spectral characteristics (intensity, stability, enhancement factors). Results show PWSC altered morphology–spectrum correlations: at 7 μs, spectral enhancement reached 1.88 fold with RSD reduced from ∼12% to 5.8%. Notably, the AL/RL-intensity correlation reversed from strong positive (0.94 to 0.997) to moderate negative (−0.38 to −0.46), while RL intensity correlation strengthened dramatically (0.13 to 0.95), highlighting RL dominant role under confinement. These findings provide mechanistic insights into confinement-induced plume dynamics and establish a foundation for correlation-weighted plasma image-spectrum fusion optimization in high-precision elemental analysis.

As a real-time in situ elemental detection technology, Laser-Induced Breakdown Spectroscopy (LIBS) has been applied to the detection and exploration of marine environments and mineral resources. However, due to the influence of high pressure, there have been no reported LIBS detection results for solid samples at depths exceeding 4000 m on the seabed. This study developed a novel elemental chemical sensor system using high-pressure gas ventilation, creating a high-pressure gas environment on the seabed to avoid the influence of seawater on the LIBS results. In 2024, the elemental chemical sensor system was deployed on the Haixing 6000 remotely operated vehicle (ROV) for three deep-sea trials, achieving the world's first spectral line results for carbon steel samples on the seabed at 6000.8 m depth. The research also investigated the influence of delay time on spectral results at 6000.8 m depth. Comparative analysis revealed that the gas-ventilation method effectively extends plasma lifetime compared to direct solid detection in aqueous environments. The above results demonstrate that the elemental chemical sensor possesses the capability for in situ detection of solid samples in deep-sea environments, providing a novel and effective solution for submarine geochemical research and submarine mineral exploration.

The determination of long-lived radionuclides by inductively coupled plasma tandem mass spectrometry (ICP-MS/MS) is a well-established approach. However, such determinations can still be hindered by isobaric interferences from stable isotopes of neighbouring elements. As such, investigations towards novel gas cell approaches for removing interfering ions are required in order to improve the reliability of the analysis. Nitrous oxide (N₂O) is a reaction gas that has been well studied for stable isotope analysis. Studies towards its applicability to radionuclide analysis have so far been limited. Here, the use of N₂O, as well as a mixture with ammonia (NH₃), have been evaluated for determinations of 10 radionuclides of interest for nuclear decommissioning: ⁴¹Ca, ⁶³Ni, ⁷⁹Se, ⁹⁰Sr, ⁹³Zr, ⁹³Mo, ⁹⁴Nb, ¹⁰⁷Pd, ¹³⁵Cs, and ¹³⁷Cs. Single element solutions of stable isotope analogues of the radionuclides, as well as solutions of the interfering ions, were used to observe the reactions with the ICP-MS/MS reaction cell gases. Abundance-corrected sensitivities were used to assess the achievable separation factors and sensitivities for the determination of the radionuclides of interest. The N₂O/NH₃ gas mixture was found to provide a significant enhancement in the removal of isobaric interferences, as well as instrument detection limits (given in brackets), compared to N₂O alone for determinations of ⁴¹Ca (0.50 pg g⁻¹ (0.0016 Bq g⁻¹)), ⁷⁹Se (0.11 pg g⁻¹ (5.4 × 10⁻⁵ Bq g⁻¹)), ⁹⁰Sr (0.11 pg g⁻¹ (0.56 Bq g⁻¹)), ⁹³Mo (0.12 pg g⁻¹ (0.0044 Bq g⁻¹)), ¹³⁵Cs (0.1 pg g⁻¹ (7.5 × 10⁻⁶ Bq g⁻¹)), and ¹³⁷Cs (0.1 pg g⁻¹ (0.33 Bq g⁻¹)).

This study evaluated the micro-homogeneity of seven different commercially available nanoparticulate pressed pellets based on a CaCO₃ matrix and their utility for quantitative elemental mapping of biogenic carbonates using laser ablation-inductively coupled plasma-time-of-flight-mass spectrometry (LA-ICP-TOF-MS). The analytical performance of matrix-matched calibration (using the aforementioned nano-pellets) was compared against that of non-matrix-matched calibration using a silicate glass (NIST SRM 610) reference material for quantification. Calibration with nano-pellets, combined with the use of Ca as an internal standard, significantly improved the quantification accuracy, providing recoveries between 80–120% for the majority of the 18 elements selected and spread across a wide concentration range (from sub-μg g⁻¹ to tens of wt%). However, some nano-pellets (e.g., BPLM-NP and BAM-RS3-NP) exhibited higher heterogeneity, leading to biased recoveries. Also, an inverse correlation between the mass fraction and the relative standard deviation (RSD) was observed. Throughout the work, elemental mapping was conducted with a laser beam size of 20 × 20 μm² as a compromise between spatial resolution, sensitivity (sub-μg g⁻¹ limits of detection required for trace elements), linear dynamic range, total analysis time and size of the region-of-interest. The quantitative mapping approach enabled the generation of high-resolution, multi-elemental 2D-maps of various CaCO₃-based natural chronological archives, including fish otoliths and bivalve shells, revealing detailed elemental distribution patterns for both trace (e.g., Mn, Ba) and major elements (e.g., Sr). This LA-ICP-TOF-MS methodology provides a powerful tool for resolving intricate microstructures and thus, for chronologically tracking bioaccumulation of environmentally relevant metals, offering significant advantages over traditional 1D (line scanning) analysis as the latter may lead to misinterpretation of elemental distributions.

Ice cores serve as unique paleo-archives allowing access to long-term records of trace elements, which are important for our understanding of global biogeochemical cycles and air pollution. However, analysis of trace elements in ice cores is a challenge, because of very low concentration levels and their presence in dissolved and particulate form. The commonly used and well-established technique for analysis of trace elements in ice cores is inductively coupled plasma sector field mass spectrometry (ICP-SF-MS). Recently, inductively coupled plasma time of flight mass spectrometry (ICP-TOF-MS) was introduced as a new, promising technique. Due to its fast acquisition time for the near-full mass spectral range, it allows for the detection of short transient signals, offering the opportunity to determine the elemental composition of single particles. In this study, the performance of this new technique for analyzing total trace element concentrations was tested and compared to the one established in the field of ice core research. The focus was on sensitivity, precision, and optimization of sample preparation in sections from two ice cores (Colle Gnifetti, Cerro Negro), characterized by different impurity levels. The performance of the ICP-TOF-MS was excellent for the investigated trace elements within the nominal mass range from 23 to 238, except for ⁴⁵Sc because of insufficiently resolved mass interferences. This is very promising for many applications in ice core research, especially in view of the great benefit to analyze single particles simultaneously in the same sample.

Soil elemental analysis is vital for assessing nutrient availability and contamination. For soil analysis, conventional calibration-free LIBS (CF-LIBS) methods suffer from unavoidable uncertainty of the Einstein coefficients and optical coefficients. Although one-point calibration LIBS (OPC-LIBS) can tackle these problems to some degree, it is still limited by the self-absorption effect and strict requirement of accuracy of all-elemental content. In this work, a modified OPC-LIBS method was proposed, which combines an internal reference element strategy with self-absorption correction and columnar-density Saha–Boltzmann (CD-SB) plotting. Only one major element, silicon, needed to be certified in the reference soil sample. All other elements across diverse soils were deduced. Moreover, self-absorption and plasma parameters (temperature and electron density) were corrected via CD-SB plots, providing a more reliable basis for elemental determination. The results demonstrated that mean relative errors (MRE) were reduced to 16.324–42.358% in the proposed method, much lower than in conventional CF-LIBS and in CF-LIBS with self-absorption correction. The analytical accuracy also matched or outperformed conventional OPC-LIBS (requiring certification of all elements in the reference sample). This work provided a more convenient and accurate method for multi-element soil analysis.

Accurately quantifying rhenium (Re) and osmium (Os) at ultra-trace concentrations in geological matrices, such as soils and rocks, is essential for progress in Earth sciences. The isotopic compositions of these elements serve as critical geochronometers and geochemical tracers, providing valuable insights into mantle evolution, crustal recycling, and ore deposit formation. However, their extremely low natural abundances, often ranging from picograms per gram (pg g⁻¹) to nanograms per gram (ng g⁻¹), present significant analytical challenges that require methods with exceptionally low detection limits.This review presents a detailed comparative analysis of leading analytical approaches for Re and Os determination in geological samples, emphasizing their lower limit of detection (LLD) performance. Mass spectrometry-based techniques, particularly Negative Thermal Ionization Mass Spectrometry (N-TIMS) and Multi-Collector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS), are identified as the most effective methods for achieving ultra-low detection limits. N-TIMS has historically demonstrated superior performance in reaching the lowest absolute LLDs for osmium, frequently attaining picogram-level sensitivity due to its high ionization efficiency and advanced detector technologies. For rhenium, MC-ICP-MS, especially when integrated with advanced chromatographic separation protocols, achieves LLDs in the sub-pg g⁻¹ range. Attaining this level of sensitivity depends not only on instrumental capabilities but also on comprehensive analytical strategies, including meticulous sample preparation, stringent control of procedural blanks, and sophisticated measures for reducing spectral and matrix interferences.To ensure methodological reliability and precision, this review also examines the use of certified reference materials in geological sample analysis. Ongoing advancements in these analytical methodologies remain vital for furthering geochemical research and enhancing our understanding of Earth's intricate history.

The increasing demand for new energy sources, such as lithium batteries, has driven exploration of lithium sources like brines and pegmatites, with spodumene being the only mineral with an economically viable extraction route. In 2020, the European Commission listed lithium as a critical raw material. To ensure the quality of spodumene, rapid and accurate analytical methods are essential. Laser-induced breakdown spectroscopy (LIBS) is a promising technique for direct, rapid multi-element analysis, but it usually suffers from intense matrix effects. This study proposes a partial matrix matching multi-energy calibration approach (PMM-MEC) for the determination of Al, Fe, Li and Si in spodumene samples from different locations in Minas Gerais, Brazil, using LIBS. A mixture of spodumene samples, previously characterized by XRF and ICP OES, was used as the standard. The PMM-MEC method was validated by determining Al, Fe, and Ti in bauxite (CRM BXMG-2); Ca, Si and Mn in manganese ore (CRM NCS47009); and Al, Ca and Si in portland cement (CRM 1889a). Sodium and boron, or lithium and boron, were used as internal standards to mitigate matrix effects and improve the method's accuracy. The method provided relative errors between −12% and 10% and relative standard deviations (RSD) ranging from 0.2% to 2.5%. The limits of detection for all analytes were in the 0.0003–0.7% range.

Kα _1,2, Lα_1,2, and Lβ₁ X-ray spectra linewidths have been measured in elements Nb, Mo, and Rh, using a high-resolution double-crystal X-ray spectrometer. From the obtained experimental values, Γ_K, Γ_L2, and Γ_L3 level widths were estimated and compared with the recommended and semi-empirical ones, and our theoretical results. The overall tendency of the corrected full width at half maximum of the Kα₁ and Kα₂ lines as a function of Z agrees with the data in the literature.

Laser-induced breakdown spectroscopy (LIBS) is a swift and potent analytical method utilized for element detection, owing to its distinct advantages in online/in situ detection. However, the analysis of LIBS spectra encounters a significant challenge during the acquisition phase due to measurement uncertainty caused by matrix effects and self-absorption. To improve the performance of LIBS analysis, LIBS combined with a deep learning method based on a back propagation (BP) neural network optimized by the improved sparrow search algorithm (ISSA) was proposed here for the recognition of seven different types of tea. In order to realize rapid green identification of tea, a total of 1050 spectral datasets from seven tea samples were obtained by laser-induced breakdown spectroscopy. The spectral datasets were preprocessed to eliminate noise and background interference, and eight principal components were extracted by principal component analysis (PCA). In order to solve the issues of slow convergence of the traditional BP neural network model and its tendency to fall into the local optimal value, a hybrid strategy incorporating a tent chaotic map, adaptive T-distribution variation, number of producers and dynamic adjustment of search space were introduced to improve the SSA, and then the BP neural network hyperparameters were optimized with the ISSA. Subsequently, the hyperparameters of the BP neural network were optimized using the ISSA. Finally, the model is compared with K-Nearest Neighbors (KNN), BP, SSA-BP and other models. The results show that the ISSA-BP model has the best performance, and the recognition accuracy is superior to other models, with a classification accuracy reaching 99.1%. In conclusion, LIBS combined with the ISSA-BP neural network model can quickly and accurately recognize tea types.