Journal of Analytical Atomic Spectrometry最新文献

With the rapid development of laser-induced breakdown spectroscopy (LIBS) imaging technology, it has attracted widespread attention. A LIBS imaging system based on laser ablation generates two- and three-dimensional (2D/3D) images through the point scanning method. This paper reviews the recent developments based on LIBS chemical composition imaging technology, including its background, fundamental principles, operation types, applications, and technical upgrade schemes. Here, the types of LIBS imaging operations and proposals for technological upgrades are the focus of this review. More importantly, we point out the existing problems in LIBS imaging and provide our own insights into its future development directions, including LIBS signal optimization, LIBS imaging resolution improvement and multiple technology integration. As a newly recent development direction, the technology fusion method is also a very promising research field in chemical composition imaging based on LIBS. This review is of great significance for the research and application of LIBS chemical composition imaging operations, which will provide basic knowledge guidance for LIBS researchers.

The accurate evaluation of heat-resistant steel deterioration using laser-induced breakdown spectroscopy (LIBS) is of great importance for the safe operation of high-temperature pressure equipment. Understanding how plasma expresses matrix properties and utilizing plasma information effectively can lead to achieving more effective detection methods. In this study, the plasma evolution and pulse fluctuations of typical heat-resistant steel T91 are studied based on plasma images to understand the different evolution stages and characteristics of plasma. T91 specimens with different aging grades are employed to investigate the expression form, evolution and identification of matrix information on plasma. Subsequently, the plasma images and the RSD images based on pulse–pulse relative standard deviation (RSD) were employed to build an aging grade evaluation model, the best model accuracies were 96.6% and 96.0%, respectively. A model combining these two image features achieved the highest accuracy at 99.8%. Finally, the effects of the delay time, region selection, and data coupling strategy on model performance were explored. The results indicate that the temporal–spatial characteristics, identification, and stability of plasma information have a significant effect on the performance of the model. This study deepens the understanding of the plasma evolution and matrix effect of heat-resistant steel and expands the application of plasma image information for property detection.

A new compact, extendable and lab-based micro-XRF (μXRF) spectrometer with high photon flux and spatial resolution is developed and reported in this paper. With a liquid Ga–In alloy jet as the X-ray source and using a multi-layer focusing mirror, the spectrometer provides an energy of 9.25 keV, with a focused spot size of 70 μm (H) × 63 μm (V) and a photon flux density of about 4.15 × 10¹¹ photons per s per mm². A National Institute of Standards and Technology (NIST) standard reference material (SRM) 611 was used to determine the detection limits (DLs). The spectrometer provides the DLs of 5.7–86.5 ppm in the atomic number range of 20–29. Finally, an ancient turquoise glazed ceramic was investigated by this method. The μXRF mapping results can help to reveal elemental compositions and could be used to facilitate an in-depth investigation of the production of ceramics when combined with graphical displays. This in situ μXRF analysis in the laboratory could serve as an effective means for understanding the microstructure of samples.

Recent findings reported by Van Helden et al. (Anal. Chim. Acta, 2024, 1287, 342089) have revealed that a whole suite of elements (S, Zn, As, Se, Cd, I, Te and Hg) undergoes two-phase sample transport during mapping of carbon-based materials using nanosecond laser ablation (LA) combined with an ICP-mass spectrometer equipped with a quadrupole or time-of-flight analyzer (ICP-QMS or ICP-TOFMS). Examining single pulse response (SPR) profiles, it became evident that these elements are transported in both gaseous and particulate forms. This phenomenon leads to notable widening of the SPR profiles, exhibiting two peaks in which the distribution of the ablated sample material across the peaks depends on the laser fluence. Consequently, the image quality may degrade, especially at higher pixel acquisition rates typically used with low-dispersion ablation cells. This is experimentally demonstrated by mapping of kidney tissue at low and high pixel acquisition rates and including elements which show one-phase (Mg, Ca, Fe and Cu) and two-phase (S, Zn, I and Hg) sample transport. To predict the impact of sample transport phenomena on the image quality through modeling, well-established computational models were utilized for virtual LA-ICP-MS mapping of a phantom and incorporate the experimentally obtained element-specific SPR profiles referenced in the aforementioned work. Downloadable interactive Python-based software for MS Windows was developed to study the effect of mapping parameters on the image quality, which was quantified by the structural similarity index (SSIM).

Egyptian mummies are often covered with black embalming matter, which is made of complex mixtures of natural organic substances such as vegetable resins, beeswax, animal fats, gums, and vegetable oils, as well as bitumen. In this work, we used proton-induced X-ray emission (PIXE) and electron paramagnetic resonance (EPR) to investigate the potential of certain transition metals, in particular V and Ni, as probes for detecting the presence of bitumen and tracing its origin and alteration in this black embalming matter. PIXE analysis showed that all the mummies studied in this work (bird, ram, crocodile, human), which span a period of about 1000 years and come from different sites in Egypt, have a nearly constant Ni/V ratio close to that of bitumen from the Dead Sea, suggesting a well-defined source of bitumen supply. The same conclusion was reached by EPR analysis of vanadyl porphyrins and carbonaceous radicals. The presence of an excess of radicals in the black matter from several mummies indicates that they probably contain some carbonized organic matter in addition to bitumen. This combined PIXE-EPR methodology is quantitative and sensitive since a few % of bitumen can be non-destructively detected in a mummy sample weighing only a few mg. The combination of these two techniques can provide new information on the thermal history (preparation recipes) and redox history (natural degradation) of this black matter.

Breast cancer (BC) continues to be a significant cause of morbidity and mortality among women globally, underscoring the critical need for efficient and accurate screening methods. In this study, we introduce a Parallel Spectral Convolutional Neural Network (PSCNN), an end-to-end model, to simultaneously perform laser-induced breakdown spectroscopy (LIBS) spectral preprocessing and BC identification. PSCNN demonstrated superior performance compared to traditional single-task models. In the spectral preprocessing task, the signal-to-background ratio and signal-to-noise ratio of the preprocessed spectra improved by 8.6 and 1.6 times, respectively, compared to the raw spectra. For the classification task, the PSCNN achieved a classification accuracy of 90% on 52 test blood plasma samples, surpassing the 78% accuracy of the principal component analysis with linear discriminant analysis (PCA-LDA) model and the 82% accuracy of a single-task deep CNN. Furthermore, the PSCNN classification results were corrected according to the source of the donor individual, where the accuracy, specificity, and sensitivity achieved 92%, 96%, and 89%, respectively, for distinguishing between BC and healthy control (HC) donors. Ablation experiments revealed that removing the preprocessing module of the PSCNN led to decreased overall model performance and overfitting, indicating that information sharing occurred between the two modules. The spectral preprocessing module introduced regularization constraints for the classification module, enabling the model to learn more effective features. Overall, the PSCNN enhanced the discrimination performance in BC spectral analysis through multi-task modeling.

Nitrogen (N) content is a significant indicator to evaluate eutrophication. The interference of nitrogen in air is the biggest hurdle for the direct detection of N in aerosol solution. In this work, the liquid was changed to liquid aerosol by plasma amplification LIBS with the aid of inert gas argon to reduce the interference of air. To suppress the interference of air, the effect of laser energy on the qualitative and quantitative analysis of N in aerosol solution by plasma amplification LIBS was studied. The laser energy threshold (E_th) of N I 746.831 nm reduced with the concentration of N in aerosol solution. Moreover, for the blank sample, the E_th of N I 746.831 nm was 81.77 mJ. The optimal laser energy was 88.00 mJ, and the highest relative blank deviation was observed at this value. The results show that by reducing the laser energy closer to this E_th, the limit of detection (LoD) and background equivalent concentration (BEC) were reduced from 2.79 and 337.60 ppm to 0.99 and 9.06 ppm, respectively. Furthermore, the R², the average relative error (RE_AV), and the root mean square error of cross validation (RMSECV) were improved from 0.9539 to 0.9844, 10.59 to 6.65%, and 11.45 to 7.48 ppm, respectively. The recoveries of the N element in real samples were analyzed by the standard addition method and the recoveries were in the range of 95.85–104.38%. The results indicate that the reduction of the laser energy can mitigate the interference of air in the detection of N in aerosol solution by plasma amplification LIBS.

The screening of interfering spectral lines is of great significance for improving quantitative accuracy during the LIBS quantitative analysis process. This paper proposes a novel method that utilizes optical computation for spectral line screening. The method calculated the spectral line intensity using optical formulae and obtained a reference intensity. First, during the initial screening process, the plasma temperature was determined using the Boltzmann double-line method. This step ensured that the temperature remained unaffected by the actual spectral intensities. Subsequently, the reference intensity was determined based on the formula for spectral line intensity emitted from energy levels. Finally, comparing the reference intensity with the actual intensity yields a ratio. The characteristic spectral lines were then screened based on the results of this ratio. The selected spectral line data were utilized in artificial neural network (ANN) analysis. The results demonstrate a significant improvement in the determination coefficient (R²), increasing from 0.6378 to 0.9992. Simultaneously, the root mean square error (RMSE) was reduced from 2.9098 to 0.1135. This study provides a feasible approach for addressing spectral interference issues in LIBS by integrating theoretical calculations and machine learning models.

Nanoparticles are used in various fields, such as material manufacturing, catalysis and medicine, due to their unique physical and chemical properties. Accurate characterization of nanoparticles is essential for manufacturing purposes as well as for assessing their impact on the environment and human health. To achieve this, single particle inductively coupled plasma mass spectrometry (sp-ICP-MS) has become an essential analytical technique for nanoparticle analysis. It can also be used with laser ablation as a sampling method to overcome challenges related to introducing nanoparticles in liquid suspension. Similar to conventional sp-ICP-MS, laser ablation sp-ICP-MS requires standards for signal calibration, which is challenging as the availability of standard reference materials is limited for all different kinds of nanoparticles. In this work, nanoparticles embedded in polymer thin films are analyzed using laser ablation sp-ICP-MS, whereby the laser is used to sample and transport the intact particles to the plasma. For creating a calibration for mass and size investigations, defined amounts of the element of interest were introduced into the ICP-MS by quantitatively ablating polymer thin film spiked with a defined amount of liquid element standard with different laser spot sizes. The method was developed and optimized using gold nanoparticles with certified sizes that were analyzed using a quadrupole-ICP-MS in single-element mode. The nanoparticles were sized using the proposed calibration approach with a deviation of ≤2.5% from the certified diameter value. Using the calibration approach, a limit of detection for gold of 3 × 10⁻⁷ ng was calculated, which translates to a particle size of approximately 15.5 nm, comparable to values in the literature for liquid-suspension-based approaches. Multi-element nanoparticles in the form of gadolinium-doped cerium oxide (GDC) nanoparticles with two elements of interest were analyzed using an ICP-TOFMS utilizing the thin-film-based calibration approach. Comparative measurements of the material confirmed the investigated sizes and composition of the particles. This developed alternative approach circumvents the need for certified particulate standard materials by using in-house-produced spiked polymer thin films as storage-stable calibration standards. Moreover, changing the laser spot size makes it straightforward to alter the number of particles introduced into the ICP-MS.

Rotating disc electrode-optical emission spectrometry (RDE-OES) is a leading technology for detecting oil elements. However, electrode wear impedes sample combustion and spectral stability, causing errors in acquisition and analysis. In this work, we propose and develop a novel arc excitation method with a double disc electrode structure, replacing the conventional upper rod electrode with a disc electrode of the same type as the lower electrode, and the double electrodes rotate simultaneously. This method minimizes wear and maintains a constant gap, enabling a long-lasting, energy-focused arc and high-intensity, stable spectrum. Our method is validated by analyzing the spectral lines of Al, Mg, Cr and Ca. Compared with the conventional method, the results revealed that our method can improve calibration robustness, with a 2–3 times higher spectral intensity and 1–3 times higher signal-to-noise ratio (SNR) with an 8–10% reduction in the relative standard deviation (RSD) values. R² for Al, Ca, Cr and Mg increased from 0.9872, 0.9772, 0.9824, and 0.9832 to 0.9988, 0.9994, 0.9998 and 0.9997, respectively, and the limits of detection (LODs) decreased from 2.0, 1.8, 1.7, and 1.9 ppm to 1.9, 1.6, 1.6 and 1.7 ppm. Our method achieved consistently high and stable spectral intensity and significantly reduced electrode wear during excitation, with the outer diameter change being 44.4% of the rod electrode length change after 50 excitations. The results demonstrate the method's ability for signal stability and accurate quantitative analysis, highlighting RDE-OES's importance in oil analysis.