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In this work, we report the results on the application of Laser-Induced Breakdown Spectroscopy (LIBS) to the analysis of coal, for the determination of its Ash Content. This parameter is critical for an efficient use of coal to produce electrical energy in power plants. The results obtained are compared with the ones obtained through conventional chemical laboratory analysis. The study here presented, although performed in the Laboratory, was set up trying to reproduce the real industrial conditions, with the purpose of easing the transfer of the results obtained to the on-line monitoring of coal quality at the power plant.

In this work, a fast method for obtaining a quantitative elemental mapping of highly inhomogeneous samples by μ-LIBS maps is proposed. The method, transportable and cheap, allows the analysis of large maps through the use of a Self-Organizing Map clustering method coupled to Calibration-Free LIBS for quantification of cluster prototypes. The method proposed has been verified on heterogeneous materials such historical lime mortars but it can be easily applied to a larger class of inhomogeneous materials for very different applications (modern building materials, biological samples, industrial materials, etc.).

The present work aims to evaluate the alteration conditions of historical limestone rocks exposed to urban environment using the Laser-Induced Breakdown Spectroscopy (LIBS) technique. The approach proposed is based on the microscale three dimensional (3D) compositional imaging of the sample through double-pulse micro-Laser-Induced Breakdown Spectroscopy (DP-μLIBS) in conjunction with optical microscopy. DP-μLIBS allows to perform a quick and detailed in-depth analysis of the composition of the weathered artifact by creating a ‘virtual thin section’ (VTS) of the sample which can estimate the extent of the alteration processes occurred at the limestone surface. The DP-μLIBS analysis of these thin sections showed a reduction with depth of the elements (mainly Fe, Si and Na) originating from atmospheric dust, particulate deposition and the surrounding environment (due to the proximity of the sea), whereas, the LIBS signal of Ca increased in intensity from the black crust to the limestone underneath.

When compared to other analytical techniques, LIBS shows relatively low precision and, generally, high Limits of Detection (LODs). Until recently, the attempts in improving the LIBS performances have been based on the use of more stable/powerful lasers, high sensitivity detectors or controlled environmental parameters. This can hinder the competitiveness of LIBS by increasing the instrumental setup cost and the difficulty of operation. Sample treatment has proved to be a viable and simple way to increase the LIBS signal; in particular, the Nanoparticle-Enhanced Laser Induced Breakdown Spectroscopy (NELIBS) methodology uses a deposition of metal nanoparticles on the sample to greatly increase the emission of the LIBS plasma. In this work, we used a simple, fast, “green” and low-cost method to synthetize silver nanoparticles by using coffee extract as reducing agents for a silver nitrate solution. This allowed us to obtain nanoparticles of about 25 nm in diameter. We then explored the application of such nanoparticles to the NELIBS analysis of metallic samples with a mobile LIBS instrument. By adjusting the laser parameters and optimizing the sample preparation procedure, we obtained a NELIBS signal that is 4 times the LIBS one. This showed the potential of green-synthetized nanoparticle for NELIBS applications and suggests the possibility of an in-situ application of the technique.

In this paper we present the application of Independent Component Analysis to a set of time-resolved LIBS spectra, acquired on a brass sample at different delay times. The decomposition of the LIBS spectra in few Independent Components with a given temporal evolution is then exploited for obtaining the temporal evolution of the plasma electron temperature, through the application of the three-dimensional Boltzmann plot method recently proposed by the authors. This method allows the determination of the electron temperature temporal evolution without any knowledge of the spectral parameters (transition probability, degeneracy of the levels, etc.…) of the emitting lines. Only the knowledge of the energy of the upper level of the transition is required. The reduction of the LIBS spectral dataset to few Independent Components and associated proportions, further simplifies the determination of the plasma electron temperature temporal evolution, since the intensity of the emission lines does not need to be calculated. The results obtained are compared with the ones obtained using classical two-dimensional Boltzmann plot approach.

Every year throughout the world > 50 million vehicles reach the end of their life, producing millions of tons of automotive waste. The current strategies for the separation of the non-ferrous waste fraction, contain mainly aluminum, magnesium, zinc and copper alloys, involve high investment and operational costs, and pose environmental concerns. The European project SHREDDERSORT, in which our research group was actively involved, aimed to overcome this issue by developing a new dry sorting technology for the shredding of non-ferrous automotive wastes. This work represents one step of the complex SHREDDERSORT project, dedicated to the development of a strategy based on Laser Induced Breakdown Spectroscopy (LIBS) for the sorting of light alloys. LIBS was here applied in laboratory for the analysis of stationary aluminum shredder samples. To process the LIBS spectra a methodological approach based on artificial neural networks was used. Although separation could in principle be based on simple emission line ratios, the neural networks approach enables more reproducible results, which can accommodate the unavoidable signal variations due to the low intrinsic reproducibility of the LIBS systems. The neural network separated samples into different clusters and estimates their elemental concentrations.

In this communication, we present the development of the first double-pulse micro-LIBS (DP-μLIBS) instrument for three-dimensional compositional mapping of materials. The system allows for high-resolution three-dimensional scanning of materials; its advantages with respect to conventional single-pulse micro-LIBS systems are described and discussed. As a test example, we analyzed three Euro coins to show the performances of the system on homogeneous samples (20 Eurocents), heterogeneous samples (1 Euro) and layered samples (5 Eurocents). DP-μLIBS cannot provide isotopic information and has, typically, limits of detection for the elements of interest much higher with respect to Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS). However, when the isotopic information and the extreme sensitivity to trace elements is not be necessary, the results obtained show that DP-μLIBS can be a viable alternative to LA-ICP-MS for the acquisition of high resolution three-dimensional compositional maps.

In this paper, we present a discussion on three different methods recently proposed for the analysis of Laser-Induced Breakdown Spectroscopy (LIBS) spectra, namely the One-Point-Calibration, the Calibration-Free Inverse method, and the Csigma graphs approach. The common characteristics of these methods is the fact of being based on the main equations governing the emission of near-LTE plasmas, i.e. the same theoretical framework that is at the basis of the Calibration-Free LIBS analysis. However, with the purpose to guarantee a higher trueness in the analysis, these methods rely on the information obtained by the analysis of a single reference material, of known composition, for calculating the values of some experimental parameters that would be otherwise difficult to determine. In analogy with a similar approach used in the analysis of X-ray Fluorescence spectra, we place these techniques in the general framework of the so-called Fundamental Parameters Analysis. The three methods were tested and compared on LIBS spectra acquired on modern bronze samples. The advantages and drawbacks of the different approaches are discussed in detail.

In this paper we present a new approach for unsupervised classification of materials from the spectra obtained using the Laser-Induced Breakdown Spectroscopy technique. The method is based on the calculation of the correlation matrix between the LIBS spectra, which is interpreted as an Adjacency matrix in the framework of Graph theory. A threshold is applied on the edge values, which is determined through maximization of the Modularity of the Graph. The classification of the spectra is done automatically after the calculation of the Modularity parameter. An example of the application of the proposed method is given, based on the study of six bronze standards of known composition. The advantages of the proposed approach with respect to Principal Component Analysis are also discussed.

The composition of atmospheric aerosols (particulate matter - PM) has been studied through the quantification of major elements in filter samples by laser induced breakdown spectroscopy (LIBS). After the collection of PM on over quartz filters, the samples were directly analyzed without the need of wet chemistry. The spectral analysis was performed by calibration free method (CF-LIBS), which also avoids using calibration standards. The major mineral elements found in PM10 and PM2.5 samples were Al, Ca, Fe, Mg, K, Na and Si. Furthermore, elements such as C, O and H, which form part of the organic matter and/or water, were also identified and quantified. In addition to CF-LIBS fastness and feasibility, it has been demonstrated that CF-LIBS is a complementary technique for the characterization of PM.

Several strategies have been effectively tested in the past to improve the Laser Induced Breakdown Spectroscopy (LIBS) signal, involving peculiar experimental configurations, such as Double Pulse LIBS (DP-LIBS). Recently, sample treatment has proven to be a viable and simple way to enhance the LIBS performances; in particular, the Nanoparticle-Enhanced LIBS (NELIBS) methodology uses a deposition of metal nanoparticles (NPs) on the sample to greatly increase the emission of the LIBS plasma. In this work, we investigate the possibility of combining, for the first time, DP-LIBS with NELIBS. The chosen set-up uses two parallel non-collinear laser pulses to ensure the activation of NPs during both pulses, while maintaining the low-pressure environment typical of DP-LIBS. The two laser beams are spatially separated so that the second beam is focused within the shock wave generated by the first one. This configuration allows the exploitation of the combined enhancement of both DP-LIBS and NELIBS. Several tests on copper targets have been performed to determine the best set-up for the maximum magnitude of the signal enhancement by adjusting different parameters such as pulse energies and offset distances. Commercially available silver NPs and NPs prepared by pulsed laser ablation in liquid have been tested and their performances compared with standard DP-LIBS.

Laser induced breakdown spectroscopy (LIBS) is a well-established technique for on-line industrial applications where robustness, stability, reliability, speed of analysis and operational availability are important topics to be taken into account. In this work, we propose an advanced methodology for in situ analysis of transition as-cast products during continuous casting with the purpose of improving the productivity and cost-efficiency in the fabrication of stainless steel and special steels. We have applied LIBS combined with Artificial Neural Network (ANN) for elemental analysis of casting steels. We have found that the quantitative determination of the material's composition across the slab/billet length is possible and that the intermediate chemical composition between sequential heats can be readily identified.

In this paper we face the problem of assessing similarities in the composition of different metallic alloys, using the laser-induced breakdown spectroscopy technique. The possibility of determining the degree of similarity through the use of artificial neural networks and self-organizing maps is discussed. As an example, we present a case study involving the comparison of two historical brass samples, very similar in their composition. The results of the paper can be extended to many other situations, not necessarily associated with cultural heritage and archeological studies, where objects with similar composition have to be compared.

Laser Induced Breakdown Spectroscopy can provide a useful contribution in mineralogical field in which the quantitative chemical analyses (including the evaluation of light elements) can play a key role in the studies on the origin of the emeralds. In particular, the chemical analyses permit to determine those trace elements, known as fingerprints, that can be useful to study their provenance. This technique, not requiring sample preparation results particularly suitable for gemstones, that obviously must be studied in a non-destructive way. In this paper, the LIBS technique was applied to distinguish synthetic emeralds grown by Biron hydrothermal method from those grown by Chatham flux method. The analyses performed by collinear double-pulse LIBS give a signal enhancement useful for the quantitative chemical analyses while guaranteeing a minimal sample damage. In this way it was obtained a considerable improvement on the detection limit of the trace elements, whose determination is essential for determining the origin of emerald gemstone. The trace elements V, Cr, and Fe and their relative amounts allowed the correct attribution of the manufacturer. Two different methods for quantitative analyses were used for this study: the standard Calibration-Free LIBS (CF-LIBS) method and its recent evolution, the One Point Calibration LIBS (OPC-LIBS). This is the first approach to the evaluation of the emerald origin by means of the LIBS technique.

The usual approach to laser-induced breakdown spectroscopy (LIBS) quantitative analysis is based on the use of calibration curves, suitably built using appropriate reference standards. More recently, statistical methods relying on the principles of artificial neural networks (ANN) are increasingly used. However, ANN analysis is often used as a ‘black box’ system and the peculiarities of the LIBS spectra are not exploited fully. An a priori exploration of the raw data contained in the LIBS spectra, carried out by a neural network to learn what are the significant areas of the spectrum to be used for a subsequent neural network delegated to the calibration, is able to throw light upon important information initially unknown, although already contained within the spectrum. This communication will demonstrate that an approach based on neural networks specially taylored for dealing with LIBS spectra would provide a viable, fast and robust method for LIBS quantitative analysis. This would allow the use of a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and provide a fully automatizable approach for the analysis of a large number of samples.