Laser-induced breakdown spectroscopy (LIBS) is a technology of content analysis and composition analysis based on the atomic excitation and emission spectrum of materials. It has been intense activity in the field because of its advantages such as fast detection speed, no environmental limitation and no sample pretreatment. The low accuracy of LIBS is a primary problem in current applications, and the better data analysis methods is the key to solve this problem. Recently, machine learning algorithms significantly improve the accuracy of LIBS compared with traditional analysis methods. Therefore, the researchers gradually begin to pay attention to the application of machine learning algorithms in the LIBS data analysis. It is a programming method to study how computers simulate the learning process of human beings to acquire new knowledge and skills and continuously improve their performance. It is widely used in data analysis, pattern recognition, artificial intelligence and other fields. Here, we introduce the basic principle of LIBS and machine learning algorithms, review the research situation and progress of the application of machine learning algorithms to LIBS, and put forward the problems and challenges of its application.
MgB2 bulks doped with Fe and Fe2O3 nanowires are prepared by hybridized diffusion method. The doping effect on superconductivity transition temperature, Tc, critical current density Jc, and flux pinning behavior have been studied. It is found that both Tc and Jc of MgB2 show quite different features for these two kinds of nanowires. Fe2O3 nanowires significantly suppress both Tc and Jc of MgB2, whereas Fe nanowires do improve the flux pining behavior of MgB2 although the Tc is slightly suppressed.
In the field of quantitative analysis utilizing laser induced breakdown spectroscopy (LIBS), ensuring the stability and precision of the technique is a crucial consideration. This is particularly pertinent for certain engineering applications, where utilizing electric translation stages for sample scanning is not feasible. In this study, a compact optical scanning LIBS analysis system was developed using a deflection mirror design to replace the translation platform. The system achieved rapid scanning and precise analysis, with a total weight of less than 5 kg. Moreover, to quantitatively analyze Mn, Ni, Cr, Cu, and other elements in various alloy steels, a general method for selecting spectral analysis line combination was proposed. The calibration model was established utilizing 16 alloy steel samples, and its validity was assessed through verification set of 7 samples (with element content ranging from 0.021 to 2.0wt%). The results indicate a high degree of accuracy. The calibration model determination coefficients (R2) for Mn, Ni, Cr, and Cu elements exceeded 0.99. The root mean square error of validation (RMSEV) was less than 0.05, and the relative standard deviation (RSD) was not more than 4%. Additionally, the relative prediction errors for Mn range from 2.98% to 67.44%, for Ni range from 0.57% to 15.93%, for Cr range from 3.91% to 55.56%, and for Cu range from 0.95% to 38.1%.
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