Nanoparticle-enhanced laser-induced breakdown spectroscopy combined with support vector regression on the quantification of sr element

To improve the precision of laser-induced breakdown spectroscopy (LIBS) for detecting Sr in solutions, the study employs nanoparticle-enhanced laser-induced breakdown spectroscopy (NELIBS) technique. Nanogold particles (Au NPs) are introduced into the test solution, and their impact on the LIBS is analyzed. The study investigates the influence of Au NPs on various parameters such as spectral intensity, plasma electron temperature, electron density, etc. The results indicated that by incorporating gold nanoparticles, the enhancement factor reached 10 at low concentrations and 2.5 at higher concentrations, thereby improving the spectral signal intensity of Sr elements. The coefficient of determination (R²) of the calibration curve improved from 0.925 to 0.963. The spectral intensity's RSD underwent a notable reduction, shifting from 40.808% to 17.394%, and the root mean square error (RMSEP) of the predicted concentration improved, falling from 7.859 to 4.082. Therefore, NELIBS improves the accuracy of R² of the calibration curve and the quantitative analysis results. Support vector regression (SVR) is employed for the quantitative analysis of Sr element content in NELIBS samples, yielding an R² of 0.984 and RMSEP of 3.949. Compared to the external standard method, the relative error between the predicted concentration and the true concentration of the samples is reduced under SVR.