Laser-Induced Breakdown Spectroscopy (LIBS) has proven to be a versatile analytical technique for the detection of trace elements in various sample matrices, including hydrocarbon materials. This study focuses on optimizing LIBS parameters to enhance the accuracy and sensitivity of vanadium (V) detection in coal and hydrocarbon-based samples. A probabilistic-deterministic design of experiments (PDDoE) was employed to fine-tune key factors such as lamp energy, delay, and the number of laser impulses. Optimal settings (lamp energy: 20 J, Q-SW1: 111 µs, ΔQ-SW: 3 µs, delay: 1 µs, and 100 impulses) significantly improved the intensity of vanadium spectral lines, particularly in samples with low concentrations, achieving a limit of detection (LoD) of 0.03 % for vanadium. Calibration models were developed using Partial Least Squares Regression (PLS-R) and Multiple Linear Regression (MLR) methods. These models achieved an accuracy of 95–98 % for vanadium quantification in coal ash and hydrocarbon samples. Validation of the developed method was performed using Atomic Absorption Spectroscopy (AAS), demonstrating good correlation between predicted and experimental values. Although the method requires sample preparation and shows lower accuracy than traditional AAS, it offers a practical solution for cases where sample dissolution is difficult, such as the analysis of glasses, enamels, and geological materials. Furthermore, the method demonstrated satisfactory results in field applications, where rapid on-site analysis with portable LIBS devices was required.

Vein quartz often becomes the subject of chemical analysis. Laser-induced breakdown spectroscopy (LIBS) is faster, cheaper, and more environmentally friendly compared to traditional silicate analysis. This study demonstrates the feasibility of using LIBS in combination with design of experiment and chemometric methods for the classification analysis of amorphous quartz and the quantitative determination of several constant impurities. The combination of objects and methods used in this work has not been previously published. Vein quartz samples were collected from various geographically distant locations. The sample preparation for LIBS involved cutting and polishing a flat surface of the quartz specimen. The LIBS instrument settings were optimized using design of experiment method. The concentrations of several trace elements in the training and control sets of samples were determined using atomic absorption spectroscopy (AAS). The spectra and analytical data of the training set were used for multivariate calibration using Partial Least Squares Regression (PLS-R). Sample classification was performed using Principal Component Analysis (PCA). It was shown that in the score plot in the space of the first three principal components, the samples confidently group according to their sources of origin. The results were validated on a test set of samples. The accuracy of quantitative determination of Mg, Ca, Na, K, Al, Ti using LIBS exceeded 90%. A method for the quantitative determination of several elements in quartz and an approach to identifying the place of origin of a sample are proposed.

A probabilistic–deterministic design of experiments (PDDoE) approach was employed to optimize laser-induced breakdown spectroscopy (LIBS) parameters for the quantitative determination of minor components in lead-based alloys. The PDDoE optimization identified 18 J laser pump lamp energy, 1 µs delay, and 1 µs exposure as optimal conditions, minimizing spectral dispersion (5–8%) and ensuring stable plasma formation. The acquired spectra were subsequently processed in an R-based automated workflow, where Linear, Lasso, and Ridge regression models were used to establish quantitative relationships between normalized line intensities and atomic absorption spectroscopy (AAS) reference data. The resulting models demonstrated high accuracy (R² = 0.97 for Sn, 0.985 for Sb, 0.982 for Bi, 0.919 for As, and 0.905 for Ag), with prediction errors (RMSE) below 10% and limits of quantification (LOQ) under 0.05 wt.%. Principal component analysis (PCA) applied to 43 historical (19th–20th century) and technogenic samples (19th–20th century) allowed us to isolate clusters of Pb–Sb alloys corresponding to secondary accumulator materials, alongside a diffuse group of nearly pure Pb specimens containing variable minor impurities. The combined PDDoE–LIBS–R analytical framework provides a reproducible, non-destructive, and chemometrically validated methodology for the quantitative characterization and classification of archeological and industrial lead alloys.

This work involved conducting X-ray phase analysis and energy-dispersive spectroscopy (EDS), for the first time, as well as examining the kinetics of metal extraction processes from the tailings of the Zhezkazgan Concentration Plant (ZhCP) before and after their treatment. Based on the results of X-ray diffraction results, the main diffraction peaks have been found to corresponded to silicon-containing or silicate minerals, as well as phases characteristic of Cu2S, CuFe2S3, and ZnS. Following electrohydropulse discharge leaching, the silicates (SiO2) were partially destroyed, releasing metal oxides. The elemental composition of the tailings was analyzed using electron probe X-ray microanalysis. The EDS analysis revealed the presence of 13 elements, the most intense lines of which were silicon, oxygen, and aluminum, while the remaining elements were found in minor quantities. The activation energy was determined graphically using the Arrhenius equation based on the rate constants (lnK) at various temperatures and exposure times to electrohydropulse discharge. To determine the kinetics of the leaching process, a high-temperature thermometer was installed on the reactor to record the temperature of the slurry, including the solution reaction mixture. During the hydropulse discharge process, the temperature of the reaction medium rose to 60 °C over 30 minutes.