In this paper, we present the analysis of functional alloy samples containing metals aluminum (Al), copper (Cu), lead (Pb), silicon (Si), tin (Sn), and zinc (Zn) using a Q-switched Nd laser operating at a wavelength of 532 nm with a pulse duration of 5 ns. Nine pelletized alloy samples were prepared, each containing varying chemical concentrations (wt.%) of Al, Cu, Pb, Si, Sn, and Zn—elements commonly used in electrotechnical and thermal functional materials. The laser beam is focused on the target surface, and the resulting emission spectrum is captured within the temperature interval of $9.0\times {10}^3$ to $1.1\times {10}^4$ K using a set of compact Avantes spectrometers. Each spectrometer is equipped with a linear charged-coupled device (CCD) array set at a 2 μs gate delay for spectrum recording. The quantitative analysis was performed using calibration-free laser-induced breakdown spectroscopy (CF-LIBS) under the assumptions of optically thin plasma and self-absorption-free conditions, as well as local thermodynamic equilibrium (LTE). The net normalized integrated intensities of the selected emission lines were utilized for the analysis. The intensities were normalized by dividing the net integrated intensity of each line by that of the aluminum emission line (Al II) at 281.62 nm. The results obtained using CF-LIBS were compared with those from the laser ablation time-of-flight mass spectrometer (LA-TOF-MS), showing good agreement between the two techniques. Furthermore, a random forest technique (RFT) was employed using LIBS spectral data for sample classification. The RFT technique achieves the highest accuracy of ~98.89% using out-of-bag (OOB) estimation for grouping, while a 10-fold cross-validation technique, implemented for comparison, yields a mean accuracy of ~99.12%. The integrated use of LIBS, LA-TOF-MS, and machine learning (e.g., RFT) enables fast, preparation-free analysis and classification of functional metallic materials, highlighting the synergy between quantitative techniques and data-driven methods.

Rare earth element (REE) detection sensitivity with minimal sample damage is exciting. Laser-induced breakdown spectroscopy (LIBS) with a typical methodology is a useful diagnostic tool, but often shows poor REE sensitivity. This study presents the qualitative, quantitative, and classification analysis of REE-bearing ore samples that contain multiple elements from the lanthanoid (Ln) group (e.g., La, Ce, Nd, Sm, and Gd) using the LIBS technique, and the results are compared with those obtained using a magnetic-field-assisted LIBS (MFA-LIBS) system. The LIBS spectrum was recorded using a Nd:YAG Laser with a 532 nm emission wavelength, a 5 ns pulse duration, and a 10 Hz repetition rate. Optical regions exhibiting the strongest emission lines of REEs were identified, followed by MFA-LIBS to improve the qualitative signatures of the elements of interest. MFA-LIBS also assists in confirming signal enhancement for Sm and Gd, which were unidentified with a conventional LIBS setup. Quantitative analysis was performed using a calibration-free and magnetic-field-assisted LIBS (CF-MF-LIBS) method. La, Ce, and Nd concentrations were estimated to be from 1 to 3 wt.%, whereas Sm and Gd were detected within 0.5 wt.%. The results obtained using CF-MF-LIBS were compared with those obtained using the X-ray fluorescence spectroscopy (XRF) technique, showing good agreement between the LIBS/XRF techniques. Further, the limit of detection (LOD) of the REEs using in-house prepared samples was estimated, and the results were compared with those previously reported in the literature. Furthermore, classification analysis of REE ores based on compositional variations was achieved using principal component analysis (PCA). The first two principal components (PCs) with maximum spectral variance, such as PC1~74.5% and PC2~14.5%, were considered for the clustering, and ellipses with 95% confidence using major (x) and minor (y) axes were created to explore outliers. Therefore, the CF-MF-LIBS method in combination with PCA demonstrates a rapid, robust, and effective methodology for the detection, quantification, and classification investigation of REE-bearing ores.