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The Radon transform tomography is used for reconstruction of the emissivity distribution in single- (SP) and double-pulse (DP) laser induced plasmas in orthogonal geometry. The orthogonal DP plasma is intrinsically asymmetric and thus suitable for the Radon reconstruction. The DP plasma consists of two plasmas separated by a short time interval of ~ 1 µs. The first plasma is created in air near the surface of a Si wafer and is followed (pre-ablation mode) or preceded (post-ablation mode) by the second plasma induced on this surface. A spectrometer moves in a semi-circular path around the plasma keeping the plasma in the rotation center. The optical detection is arranged so that a thin plasma layer parallel to the target surface is monitored. The axial symmetry of the SP plasma is investigated by comparing data from the Abel inversion taken at different angles and Radon reconstruction. The multi-angle measurements are used to estimate errors of the Abel reconstruction due to asymmetries of the plasma. Time-resolved Radon reconstruction in white light is performed for the DP plasma in both pre- and post-ablation modes. In the former case, the effect of ablated aerosol on the formation of the air plasma is monitored. In the latter case, a formation of an asymmetric compression shock created by the target plasma inside the air plasma is visualized. This observation is supported by computer simulations. An interaction of the two plasmas is studied by spectrally resolved Radon reconstruction revealing a complex distribution of target and ambient species inside the plasma at all studied delay times. Overall, it is demonstrated that Radon-based tomography is an informative tool to study transient asymmetric laser induced plasmas.

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.

In this work, the potential of laser-induced breakdown spectroscopy (LIBS) for discrimination and analysis of geological materials was examined. The research was focused on classification of mineral ores using their LIBS spectra prior to quantitative determination of copper. Quantitative analysis is not a trivial task in LIBS measurement because intensities of emission lines in laser-induced plasmas (LIP) are strongly affected by the sample matrix (matrix effect). To circumvent this effect, typically matrix-matched standards are used to obtain matrix-dependent calibration curves. If the sample set consists of a mixture of different matrices, even in this approach, the corresponding matrix has to be known prior to the downstream data analysis. For this categorization, the multielemental character of LIBS spectra can be of help. In this contribution, a principal component analysis (PCA) was employed on the measured data set to discriminate individual rocks as individual matrices against each other according to their overall elemental composition. Twenty-seven igneous rock samples were analyzed in the form of fine dust, classified and subsequently quantitatively analyzed. Two different LIBS setups in two laboratories were used to prove the reproducibility of classification and quantification. A superposition of partial calibration plots constructed from the individual clustered data displayed a large improvement in precision and accuracy compared to the calibration plot constructed from all ore samples. The classification of mineral samples with complex matrices can thus be recommended prior to LIBS system calibration and quantitative analysis.

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.