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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.
The authenticity of objects and artifacts is often
the focus of forensic analytic chemistry. In document fraud
cases, the most important objective is to determine the
origin of a particular ink. Here, we introduce a new
approach which utilizes the combination of two analytical
methods, namely Raman spectroscopy and laser-induced
breakdown spectroscopy (LIBS). The methods provide
complementary information on both molecular and elemental
composition of samples. The potential of this hyphenation
of spectroscopic methods is demonstrated for ten blue
and black ink samples on white paper. LIBS and Raman
spectra from different inks were fused into a single data
matrix, and the number of different groups of inks was
determined through multivariate analysis, i.e., principal
component analysis, soft independent modelling of class
analogy, partial least-squares discriminant analysis, and
support vector machine. In all cases, the results obtained
with the combined LIBS and Raman spectra were found to
be superior to those obtained with the individual Raman or
LIBS data sets.
The purpose of this work is to improve the performance of a linear correlation method used for material identification in laser induced breakdown spectroscopy. The improved correlation procedure is proposed based on the selection and use of only essential spectral information and ignoring empty spectral fragments. The method is tested on glass samples of forensic interest. The 100% identification capability of the new method is demonstrated in contrast to the traditional approach where the identification rate falls below 100% for many samples.
The Radon transform is tested as a method for reconstruction of the emissivity distribution of asymmetric laser induced plasmas. Two types of experiments were carried out. First, the plasma asymmetry is introduced via focusing the laser by a cylindrical lens to create plasma plumes elongated along the symmetry axis of the lens. Second, an asymmetric power distribution across the laser beam is created by reflecting the latter from a damaged mirror. Various effects on the quality of the plasma emissivity reconstructed by the Radon tomography method are investigated. The understanding of these effects appears to be essential to design a proper experimental setup to study LIBS plasmas by the Radon tomography method. It is demonstrated that the Radon tomography can successfully be used for experimental studies of asymmetric LIBS plasmas.
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.
A spatial heterodyne spectrometer (SHS) is tested for the first time in combination with laser-induced breakdown spectroscopy (LIBS). The spectrometer is a modified version of the Michelson interferometer in which mirrors are replaced by diffraction gratings. The SHS contains no moving parts and the gratings are fixed at equal distances from the beam splitter. The main advantage is high throughput, about 200 times higher than that of dispersive spectrometers used in LIBS. This makes LIBS-SHS a promising technique for low-light standoff applications. The output signal of the SHS is an interferogram that is Fourier-transformed to retrieve the original plasma spectrum. In this proof-of-principle study, we investigate the potential of LIBS-SHS for material classification and quantitative analysis. Brass standards with broadly varying concentrations of Cu and Zn were tested. Classification via principal component analysis (PCA) shows distinct groupings of materials according to their origin. The quantification via partial least squares regression (PLS) shows good precision (relative standard deviation , 10%) and accuracy (within 6 5% of nominal concentrations). It is possible that LIBS-SHS can be developed into a portable, inexpensive, rugged instrument for field applications.
High repetition rate laser-induced breakdown spectroscopy using acousto-optically gated detection
(2014)
This contribution introduces a new type of setup for fast sample analysis using laser-induced breakdown spectroscopy (LIBS). The novel design combines a high repetition rate laser (up to 50 kHz) as excitation source and an acousto-optical modulator (AOM) as a fast switch for temporally gating the detection of the emitted light. The plasma radiation is led through the active medium of the AOM where it is diffracted on the transient ultrasonic Bragg grid. The diffracted radiation is detected by a compact Czerny-Turner spectrometer equipped with a CCD line detector. Utilizing the new combination of high repetition rate lasers and AOM gated detection, rapid measurements with total integration times of only 10 ms resulted in a limit of detection (LOD) of 0.13 wt.% for magnesium in aluminum alloys. This short integration time corresponds to 100 analyses/s. Temporal gating of LIP radiation results in improved LODs and consecutively higher sensitivity of the LIBS setup. Therefore, an AOM could be beneficially utilized to temporally detect plasmas induced by high repetition rate lasers. The AOM in combination with miniaturized Czerny-Turner spectrometers equipped with CCD line detectors and small footprint diode pumped solid state lasers results in temporally gateable compact LIBS setups.
A mobile double-pulse laser-induced breakdown spectroscopy system for industrial environments is presented.
Its capabilities as a process analytical technique for the recovery of metals from molten inorganic wastes are investigated. Using low-melting glass doped with different amounts of additives as a model system for recycling slags, the optimum number of shots, laser inter-pulse and acquisition delay times are optimized for solid and liquid (1200 °C) glass. Limits of detection from 7 ppm (Mn) to 194 ppm (Zn) are achieved working at a distance of 75 cm from the sample. To simplify the quantification of molten samples in an industrial furnace, the possibility is examined of using solid standards for analysis of molten material.
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.
LIBS matures to a quantitative method for elemental analysis rather than a qualitative diagnostic tool. Numerous real world applications for bulk and microanalysis profit from instrumental and methodical advances in the last decade. Today, recording of numerous spectra from samples can be done with low experimental efforts and low cost per spectrum. Not surprisingly, LIBS data, with a high spectral resolution and a broad spectral range, become “big data” and can be utilized in different ways beyond elemental analysis. But, what is the generic or best approach for quantitative LIBS analysis? What techniques can be employed to gain new insights into data? Emergent information can arise through data fusion of LIBS data with other data, i.e. orthogonal spectroscopic or other information related to the sample. But are fused data better than data from a single method?
This talk will provide an in-depth overview what chemometric tools can do for LIBS. For quantitative analysis, pre-processing tools are essential to improve precision, accuracy, and reproducibility but at the same time their application to data is still based on phenomenological criteria. Multivariate analysis seems to dominate LIBS, but are there drawbacks on using all information from spectra. For different data sets from real applications, the use of multivariate calibration and (un)supervised pattern recognition will be discussed in comparison with reference analytical methods and possible improvements through plasma diagnostics and modelling.