Spectrochimica Acta Part B: Atomic Spectroscopy最新文献

This work investigates the application of femtosecond laser-ablation spark-induced breakdown spectroscopy (fs-LA-SIBS) combined with machine learning algorithms for the rapid and accurate identification of steel alloys. Three algorithms, namely random forest (RF), support vector machine (SVM), and partial least squares identification analysis (PLS-DA), were compared and evaluated. The results indicate that, in 100 independent classifications, the RF model demonstrated an average accuracy of 0.9337, significantly surpassing the accuracies of the SVM model at 0.8281 and the PLS-DA model at 0.8646. In addition, in the evaluation of 5-fold cross-validation and the prediction set, the RF model achieved a near-perfect micro-average area under curve (AUC) of 0.9996, surpassing the AUCs of the SVM model at 0.9761 and the PLS-DA model at 0.9847. The PCA results provided valuable insights into the spectral features that most significantly contributed to the classification accuracy, further confirming the RF model's robustness and effectiveness. This integrated approach offers a powerful tool for the rapid classification and accurate identification of steel alloys in industrial applications.

A method for estimating the composition of the metal core in historical and archaeological artifacts using surface X-ray fluorescence (XRF) data is described. The method is based on the combination of fluorescence data recorded at points of the object with different degrees of corrosion exploiting the general phenomenon of demetallation of the less noble components of the metal alloy. A theoretical approach is provided using the Johnson–Mehl–Avrami–Erofeev-Kolmogorov (JMAEK) formalism to describe demetallation. Experimental data for binary ZnCu brass and Pb- and Sn-containing brass from Hofkirche in Innsbruck agree satisfactorily with the model.

Plasma grating-induced breakdown spectroscopy (GIBS) has gained notable attention for its capacity in trace metal element detection. This study examined the utilization of plasma grating to create micropores and nanoparticles on a Si sample surface and explored their impact on GIBS. We found that the presence of these features resulted in a significant 2.4-fold enhancement in the spectral intensity of the Si plasma at a laser energy of 2.7 mJ, with micropores structures and nanoparticles promoting the plasma excitation. Furthermore, the effect of micropores structures and nanoparticles on the spectral intensities of Cr and Cd elements in water was investigated. Significantly, the spectral line intensity of heavy metal Cr and Cd in the etched area was about 4.5 and 2.6 times that of the unetched area, and the detection limit for trace levels of Cr and Cd in water was determined to be 6.40 mg/L and 75.0 mg/L, respectively. These findings highlight the promising potential of the enhanced GIBS as a more sensitive method for detecting trace metal elements in water.

Continuous emission monitoring (CEM) of strontium in aerosols is of great significance for air pollution prevention and industrial facilities emission monitoring. Laser-induced plasma spectroscopy (LIPS, also LIBS) is a promising technology for direct and online monitoring aerosols because of the advantages of no sample preparation, rapid analysis, and online detection. An enhanced LIPS setup with high detection sensitivity and short detection cycles is integrated to meet the demand for continuous monitoring of trace element aerosols. The continuous monitoring of strontium in aerosols is introduced with an improved LIPS setup. The setup is calibrated and tested with different concentrations of strontium aerosols generated by aerosol generators. The enhanced LIPS setup can quantify 22 ng/m³ strontium in aerosol within 10 min. Based on experiments, a calibration curve for the strontium aerosol is established and the limit of detection (LOD) of the setup reaches 1.8 ng/m³, which meets the need for continuous monitoring of trace elements.

The importance of total atmospheric carbon (TAC) has been increasingly recognized in light of the growing significance of global climate change. The concept of TAC has expanded beyond its previous focus solely on CO₂ to encompass additional novel components. Here, a promising detection-analysis system for TAC quantitative detection is self-developed using LIBS and a multivariate physicochemical model based on transition and collision mechanism (MP-TC model). Spectral signals under different compositions were analyzed based on static detection. Then, a MP-TC model was developed by incorporating particle collisions and transition mechanisms. Subsequently, three dynamic monitoring were conducted analyzing the dynamic spectra obtained when CO₂, CO, and CH₄ were the different primary components of TAC. Interestingly, an anomalous CN transition was observed in Fuel combustion and the inhibited low vibrational transitions in symmetric molecules can be explored in CH₄ gradient concentration. Additionally, each dynamic process was fitted using the MP-TC model, confirming its reliability in TAC detection and its better alignment with the observed trends.

In order to ensure that byproducts are environmentally friendly and fit for purpose, their chemical composition must be determined, with special attention to elements at trace level. The large variability in the type of matrices demands a versatile analytical technique, compatible with sample preparation methods allowing both fast composition screenings and accurate quantitative analysis.

In this work, byproducts derived from carbon-rich matrices, including coal fly ash and activated carbon from coconut and almond shells, were investigated by means of total reflection x-ray spectrometry and different sample preparations. Suspension, acid digestion, solid-liquid extraction and ashing were performed and assessed as to their complexity and detection capabilities, ranging from 0.01 mg/kg to 16 mg/kg, depending on the element and sample preparation. The internal standard quantification with gallium, aided by a variation of the standard additions method to take into account its possible presence in the samples, allowed to determine elements of environmental and technological interest, including K, Ca, Ti, V, Mn, Cr, Ni, Cu, Zn, Ga, As, Sr, Pb. Assets, limits and possible developments are presented and discussed.

Laser-induced breakdown spectroscopy (LIBS) has been widely applied in various fields such as environmental monitoring, materials science, and archaeological research. The current research focuses on improving the spatial resolution of elemental spatial imaging by using LIBS. The nanosecond laser is commonly used in LIBS. During the nanosecond laser ablation process, there is a thermal effect on the target material, making it difficult to further improve the spatial resolution. This study used a picosecond laser to investigate the effects of the diameter of laser focusing spot, laser energy, and laser irradiation interval on the spatial resolution of LIBS. The spatial mapping of metallic coatings by using LIBS with a spatial resolution of 1 μm was achieved by using laser energy of 0.4 μJ/pulse and irradiation interval of 0.8 μm. The LIBS measurement results are in good agreement with the scanning electron microscopy energy dispersive X-ray spectroscopy (SEM-EDS) results. This research shows that by changing the laser ablation conditions, the spatial resolution of the spatial mapping of metallic coatings by using LIBS based on picosecond laser can be reduced to 1 μm or lower.

A Helium- and Argon-flexible μ-tube plasma (FμTP) driven by a square wave high voltage were investigated by means of temporally and spatially resolved spectroscopy to characterize the discharge mechanisms. These two discharge gases show different discharge behavior in the same FμTP-configuration. The mixtures of different concentration of propane in Ar were used as discharge gases to confirm the role of Ar⁺ in an Ar-FμTP by tuning the discharge behavior. It was demonstrated that, N₂⁺ are mainly responsible for the excitation and ionization and only small amount of He⁺ are produced in a He-FμTP, whereas Ar-FμTP lives from Ar⁺. These ions as well as excited species do not propagate beyond the capillary during the negative half cycle, resulting in a negligible contribution for protonation in this half cycle. The present study proposes a new method to data analysis, Plasma Optical Emission Phoresis Spectroscopy, which allows to distinguish noble gas ions from excited noble gas species.

This study introduces an improved spectrometric method with enhanced precision to determine isotope ratios in geological samples without chromatographic separation. Firstly, the improvement is achieved by increasing the spectral resolution of the spectrometer applied in well-known high-resolution continuum source atomic absorption spectrometry (HR-CS-AAS). The resulting resolving power and linear dispersion of the upgraded setup, which is denoted in the following as HR+CS-AAS, is well adapted to the line widths of the Li isotope components we investigated. Secondly, our proposed method combines optical absorption spectrometry with machine learning data analysis using an extreme gradient boosting algorithm (XGBoost). This method was applied to analyze certified geological reference materials with δ_LSVEC(⁷Li/⁶Li) (hereafter δ⁷Li) values ranging from −0.5 ‰ to 4.5 ‰. With a pixel related optical resolving power of λ/∆λ ≈ 780 000, we obtain precisions in δ⁷Li measurements from 1.0 ‰ to 2.5 ‰. The method is validated by comparing the results with multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS), confirming its metrological compatibility. This work presents a fast, robust, and reliable method for δ⁷Li measurement in geological samples.