Journal of Analytical Atomic Spectrometry最新文献

Inductively coupled plasma mass spectrometry (ICP-MS) is increasingly used for rapid measurement of medium and long-lived radionuclides. Of the techniques available, tandem ICP-MS/MS is of growing interest owing to the enhanced online interference removal capabilities offered by an additional mass filter and a collision-reaction cell. This can reduce or remove the need for offline chemical separation, further reducing the procedural time. This online interference removal approach can reduce analyte sensitivity, which is an issue for trace-level measurements, particularly for relatively short-lived radionuclides. To help address this, this study investigates the use of desolvating sample introduction combined with ICP-MS/MS for enhanced measurement of multiple radionuclides. Results are shown for actinides, difficult-to-measure radionuclides of high priority for nuclear decommissioning, and shorter-lived radionuclides relevant to paleoclimate measurement. The improved sensitivity and additional interference removal achieved compared to the standard sample introduction system are demonstrated, with the results benefitting end users interested in improved waste characterisation, environmental radioactivity, nuclear forensics, and improved historical climate measurements.

The atmosphere-induced interference reduction device (AIRD) shows promise in mitigating interferences in inductively coupled plasma-mass spectrometry (ICP-MS) analysis. The effectiveness of AIRD in reducing interferences caused by atmospheric gases such as H, C, N and O was evaluated by using argon (Ar) and helium (He) as shielding gases due to their inert properties. Krypton (⁸²Kr) served as an internal standard for monitoring and correcting instrumental sensitivity drift. The shielding gas flow rate was optimized using gas dynamics simulations, revealing a threshold of 10 L min⁻¹ for optimal interference reduction. Results indicate the superiority of He in reducing interferences, with a reduction of 32% for ¹²C⁺, 51% for ¹⁵N⁺, 56% for ¹⁶O⁺, 54% for ¹⁶O¹H¹H⁺, 51% for ⁴⁰Ar¹⁴N⁺, and 42% for ⁴⁰Ar¹⁶O⁺ compared to Ar. Moreover, AIRD maintained low oxide yields even after shielding gas cessation, with oxide yield maintained at approximately 0.03% for 24 hours. Analyses of LA-ICP-MS coupled with AIRD demonstrate that ThO/Th can be reduced from 0.92% to 0.15% compared to normal analysis without AIRD. Experimental investigations further revealed that AIRD influenced elemental sensitivity, particularly with He as the shielding gas, with an ∼25.7% enhancement observed in the signal intensity of ⁸²Kr.

Nanotheranostics aims to perform a premature and non-invasive diagnosis combined with therapy focused on the specific place where the disease is by using nanomaterials. To evaluate the ability to penetrate and retain the inorganic nanoparticles (NPs) in the cells, analytical techniques such as Single-Particle ICP-MS (SP-ICP-MS) are required to characterize these NPs. SP-ICP-MS provides not only the size distribution and concentration of NPs but also the concentration of the dissolved elements. In recent years, direct alkaline dilution of blood, serum, and urine is performed in clinical laboratories for routine analysis. This alkaline diluent is named clinical diluent and it is a mixture of ammonia, EDTA, 2-propanol, Triton X100, and purified water. In this work, a methodology to characterize AuNPs in blood and urine samples using SP-ICP-MS has been developed. Samples were directly diluted with clinical diluent before multi-quadrupole ICP-MS analysis. The effect of this clinical diluent on the behaviour and stability of AuNPs has been studied. Good stability of AuNPs was observed for both the particle size and particle concentration (<17% difference in 10 days). Moreover, analytical parameters of this method such as linearity, detection limit, accuracy, and precision in blood and urine samples were studied for both the particle size and particle concentration. Linearity was evaluated for particle size (from 10 to 100 nm) and particle concentration (from 5 × 10³ to 1 × 10⁴ NP per mL). Furthermore, recoveries between 88% and 103% for the NP concentration and between 100% and 110% for the nanoparticle size were obtained. Dissolved and gold nanoparticle detection limits have also been estimated.

For the construction of the international linear collider, mass production of niobium (Nb) superconducting cavities is essential. In the surface treatment of the Nb cavities, on-site analysis of electropolishing solution composed of hydrofluoric acid and sulfuric acid is desired. In this work, we analyzed the electropolishing solutions containing from 1.0 g L⁻¹ to 10.0 g L⁻¹ Nb by surface-enhanced laser-induced breakdown spectroscopy (surface-enhanced LIBS) that needs only a microvolume sample and simple operations. The sample solution was trapped on porous silicon (Si) fabricated by metal-assisted etching (metal-assisted chemical etching) through a wiping process. Nb emission lines were detected with low laser energy irradiation (2.0 mJ per pulse) onto the substrate. A regression model was built by partial least squares regression, and the Nb concentrations of test samples were predicted with a mean absolute error of approximately 0.4 g L⁻¹. To the best of our knowledge, this is the first report that applied LIBS to the analysis of the highly toxic electropolishing solution. The proposed method would be helpful for the quality control of surface treatment and the efficient use of solution.

Uncovering the nature of dark matter microscopic particles is one of the most important disciplinary goals of physics and astronomy in the 21st century, and how to reduce background signals and environmental interference in dark matter experiments is one of the key factors to improve the sensitivity of the detector and to take the lead in obtaining significant detection results. High-purity nitrogen, as a crucial gas for detector purging, scintillator purification and pipe cleaning, among other things, contains the radioactive gases ⁸⁵Kr and ⁸¹Kr in natural Kr, which emit β-rays that can interfere with the detection of dark matter signals. Therefore, it is necessary to measure the concentration of ultra-trace level Kr in high-purity nitrogen, and screen high-purity nitrogen complying with the standard for use in dark matter experiments. This study develops a novel analytical method to determine ultra-trace level Kr in high-purity nitrogen using a static noble gas mass spectrometer coupled with a newly designed sample processing system. A large amount of reactive gases from the original sample are removed by the large-volume high-temperature purification device, and then we explore a simple and iterative trapping method for Ar–Kr separation. This method improves the noble gas separation factor with the promise of ensuring recovery. The separated Kr is fed into a static vacuum mass spectrometer. The detection limit of this method for natural Kr is as low as 10⁻¹⁴ L L⁻¹ with an uncertainty of about 8%.

As a kind of plant with complex chemical composition, the different compositions of tobacco determine the quality of tobacco, which in turn determines the quality of its cigarette products, so high-precision and rapid identification of different brands of cigarettes is of great significance for combating the market of counterfeit and shoddy cigarettes and safeguarding people's life and health. Traditional cigarette detection methods are time-consuming and subjective, and the analysis results are not objective and precise enough, whereas this study proposes a combination of cavity-constrained laser-induced breakdown spectroscopy (LIBS) and gray wolf optimization algorithm optimized bidirectional long short-term memory (GWO-BiLSTM) networks for classifying and identifying cigarette samples of 10 different brands. The signal-to-noise ratio and enhancement factor of the spectral intensity signal, LIBS plasma temperature and density are compared for different sizes of cavity constraints, and an optimal spectral enhancement size of 5 mm in both cavity height and diameter is selected. Comparing four different spectral downscaling methods, namely, principal component analysis (PCA), robust principal component analysis (RPCA), linear discriminant analysis (LDA), and t-distribution-stochastic neighborhood embedding (t-SNE), the LDA downscaling model is selected to achieve effective downscaling of the LIBS spectral data. By comparing the classification performance of the three models, the long short-term memory (LSTM) network, bidirectional long short-term memory (BiLSTM) network, and GWO-BiLSTM network, the GWO-BiLSTM model can achieve a classification accuracy of up to 98.31% in the test set. The results show that the classification method for different brands of cigarettes proposed in this study can effectively solve the technical pain points of traditional tobacco detection methods and provide a technical means to prevent the circulation of counterfeit cigarettes.

As a remote and in situ diagnostic technique for the first wall of tokamaks, laser-induced breakdown spectroscopy (LIBS) has shown promising potential for depth profile analysis of deposition layers on plasma-facing components (PFCs). However, due to the complexity of the interface of deposition layers and the limitations of laser profiles, achieving an accurate deposition layer thickness is often more difficult for an in situ LIBS system in tokamaks. In previous studies, a Laser Profile & Interface Roughness model (LPIR model), which considers the laser beam profile and interface roughness factors, has been developed to identify the interface of deposition layers. In this study, the effect of ablation rates from different materials in the deposited layers on the accuracy of their thickness has been investigated. The depth profiling of a Ni–Cu–Ni–Cu multilayer sample, which has a four-layer structure, has been carried out using the LIBS technique under different focusing conditions as well as various laser pulse energies, with the pressure maintained at 10⁻⁵ mbar. The LPIR model was used to reconstruct the depth distribution profile of the Ni–Cu–Ni–Cu multilayer sample and quantify the interfacial positions of the deposited layers. A layer thickness correction method for multilayer sample is proposed based on the dependence of the ablation rates of different layers on laser fluence. The correction ability has been evaluated based on the relative errors between the calculated and the scanning electron microscope (SEM) values for different layer thicknesses. The relative errors of the corrected layer thicknesses are all significantly improved, and the accuracy of the layer thicknesses has been substantially improved. The proposed method will not only help us better understand the LIBS depth profiling of multilayer samples under different laser fluence conditions, but it will also further improve the accuracy of the layer thickness analysis of multilayer samples. This result is of positive significance for the application of in situ LIBS diagnostics in plasma–wall interaction (PWI) studies.

The various analytical indices of coal are important criteria for evaluating the quality of commercial coal. Coals of different qualities exhibit different physical and chemical characteristics in their utilization. In the case of multiple coal types, the spectral characteristics of different coals may overlap within certain wavelength ranges, or be affected by interference or noise from other coal types, leading to low accuracy in coal quality prediction. Rapid and accurate coal quality testing is of great significance for improving industrial production efficiency and enhancing corporate profitability. This study employs near-infrared spectroscopy (NIRS) and X-ray fluorescence spectroscopy (XRF) combined techniques to explore the accuracy and feasibility of predicting coal quality based on coal type classification models. In terms of classification algorithms, coal samples are identified and classified using Support Vector Machine (SVM) based on fusion spectra. Regarding the modeling approach, Partial Least Squares (PLS) is utilized to establish both an overall model for all coal samples and individual classification models corresponding to each coal type. The results show that the precision, accuracy, recall, and F₁ score of this classification algorithm reached 96.49%, 97.50%, 95.83%, and 96.41%, respectively. The determination coefficients (R²) for the classification model's predictions of ash, volatile matter, and sulfur in coal quality indicators reached 0.992, which represents improvements of 1.85%, 5.31%, and 10.10% over the overall model. The root mean square errors of prediction (RMSE_P) for these indicators were 0.062, 0.080, and 0.008, showing reductions of 0.24%, 0.68%, and 0.05% compared to the overall model. It indicates that the method of first identifying the coal type and then predicting coal quality indicators using the corresponding classification model can significantly improve the accuracy of coal quality detection in complex coal type scenarios.

The main purpose of this work is to thoroughly describe sensitivity and resolution enhancement by systematically optimizing key parameters in laser-induced breakdown spectroscopy analysis. Simultaneous analysis of biogenic (C, P, Mg, and Ca) and contaminating (Pb) elements, which are commonly detected in selected biotic matrices (mammal teeth), was performed. Hydroxyapatite reference pellets were utilized as model matrices, which successfully reflect human dental tissue. The optimization involved precise adjustments of the used laser wavelengths (1064, 532, and 266 nm), relative defocus of the laser pulse, ablation pulse energies, and gate delays for collecting characteristic spectra. In addition, for Ca analysis, the signals of different ionization line types (Ca I 364.44 nm; Ca II 370.60 and 396.85 nm) were compared; in the case of Pb analysis, the limits of detection were established for each used laser wavelength, and the revealed differences were discussed in detail. We intend to demonstrate the benefits of rapid, low-cost analysis and also the importance of measurement parameters used in biotic sample testing.

The poor spectral stability of laser-induced breakdown spectroscopy (LIBS) seriously affects its analytical performance, which is a key obstacle to its further development. To overcome this challenge, an improved spectral standardization method based on plasma image-spectrum fusion (ISS-PISF) was proposed in this study. This method, for the first time, considers and quantifies the influence of plasma morphology on spectral intensity based on the line-integrated intensity formula of LIBS spectra. It recognizes that the spectral fluctuations mainly stem from variations in total number density, plasma temperature, electron number density, and plasma morphology. Therefore, ISS-PISF innovatively utilizes easily accessible features from plasma images and spectra to eliminate the influence of these four plasma parameters, thereby improving the spectral stability and analytical performance of LIBS. To validate the effectiveness of this method, the spectra of aluminum alloy samples obtained under complex detection conditions simulated by varying laser energy and defocusing amount were analyzed. After correction by ISS-PISF, the R² for Mg I 516.73 nm, Mn II 294.92 nm, and Si I 288.16 nm improved to 0.990, 0.976, and 0.961, and the average RMSE of the validation set decreased by 46.154%, while the average STD of the validation set decreased by 37.405%. These experimental results indicate that this study provides a simple, effective, and physically supported spectral standardization method, which contributes to the further promotion and application of LIBS.