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The improved Monte-Carlo (MC) method for standard-less analysis in laser induced breakdown spectroscopy (LIBS) is presented. Concentrations in MC LIBS are found by fitting model-generated synthetic spectra to experimental spectra. The current version of MC LIBS is based on the graphic processing unit (GPU) computation and reduces the analysis time down to several seconds per spectrum/sample. The previous version of MC LIBS which was based on the central processing unit (CPU) computation requested unacceptably long analysis times of 10's minutes per spectrum/sample. The reduction of the computational time is achieved through the massively parallel computing on the GPU which embeds thousands of co-processors. It is shown that the number of iterations on the GPU exceeds that on the CPU by a factor > 1000 for the 5-dimentional parameter space and yet requires > 10-fold shorter computational time. The improved GPU-MC LIBS outperforms the CPU-MS LIBS in terms of accuracy, precision, and analysis time. The performance is tested on LIBS-spectra obtained from pelletized powders of metal oxides consisting of CaO, Fe2O3, MgO, and TiO2 that simulated by-products of steel industry, steel slags. It is demonstrated that GPU-based MC LIBS is capable of rapid multi-element analysis with relative error between 1 and 10's percent that is sufficient for industrial applications (e.g. steel slag analysis). The results of the improved GPU-based MC LIBS are positively compared to that of the CPU-based MC LIBS as well as to the results of the standard calibration-free (CF) LIBS based on the Boltzmann plot method.
A method based on matching synthetic and experimental emissivity spectra was applied to spatially resolved measurements of a laser-induced plasma ignited in argon at atmospheric pressure. The experimental emissivity spectra were obtained by Abel inversion of intensity spectra measured from a thin plasma slice perpendicular to the plasma axis. The synthetic spectra were iteratively calculated from an equilibrium model of plasma radiation that included free free, free–bound, and bound–bound transitions. From both the experimental and synthetic emissivity spectra, spatial and temporal distributions of plasma temperature and number densities of plasma species (atoms, ions, and electrons) were obtained and compared. For the best-fit synthetic spectra, the temperature and number densities were read directly from the model; for experimental spectra, these parameters were obtained by traditional Boltzmann plot and Stark broadening methods. In both cases, the same spectroscopic data were used. Two approaches revealed a close agreement in electron number densities, but differences in plasma excitation temperatures and atom number densities. The trueness of the two methods was tested by the direct Abel transform that reconstructed the original intensity spectra for comparing them to the measured spectra. The comparison yielded a 9 and 13% difference between the reconstructed and experimental spectra for the numerical and traditional methods, respectively. It was thus demonstrated that the spectral fit method is capable of providing more accurate plasma diagnostics than the Boltzmann plot and Stark broadening methods.