Mechanical stirring: Novel engineering approach for in situ spectroscopic analysis of melt at high temperature.

This paper proposes a novel engineering approach to control molten metals at high temperatures considering the industrial environment of such materials. To reduce analysis time and cost, in-line analysis techniques are more advantageous as they provide real-time information about melt composition. For this reason, recent research works focus on the development of new devices based on LIBS (Laser Induced Breakdown Spectroscopy). These devices allowed for analyzing impurities inside molten metals with great performance. However, improvements related to the immersion probe conception are still required. Indeed, the previous design used bubbling inside the melt, leading to spatial instabilities of the surface analyzed by LIBS. The solution presented here is mechanical stirring by innovative rotary blades which will be a part of an immersion LIBS probe. Their rotation will generate a representative, renewed, and stable surface that will be targeted by spectroscopic techniques in general and particularly by LIBS laser for molten metal monitoring at high temperatures. This solution was validated using experimental tests based on particle imaging velocimetry (PIV) in water at room temperature and then applied to silicon melt at high temperatures. To do so, it was necessary to design a system that allows the introduction of the blade in the melt and controls its rotation.

In-situ characterization of nanoparticle beams focused with an aerodynamic lens by Laser-Induced Breakdown Detection.

The Laser-Induced Breakdown Detection technique (LIBD) was adapted to achieve fast in-situ characterization of nanoparticle beams focused under vacuum by an aerodynamic lens. The method employs a tightly focused, 21 µm, scanning laser microprobe which generates a local plasma induced by the laser interaction with a single particle. A counting mode optical detection allows the achievement of 2D mappings of the nanoparticle beams with a reduced analysis time thanks to the use of a high repetition rate infrared pulsed laser. As an example, the results obtained with Tryptophan nanoparticles are presented and the advantages of this method over existing ones are discussed.

Monitoring of heavy metal particle emission in the exhaust duct of a foundry using LIBS.

Heavy metals have long been known to be detrimental to human health and the environment. Their emission is mainly considered to occur via the atmospheric route. Most of airborne heavy metals are of anthropogenic origin and produced through combustion processes at industrial sites such as incinerators and foundries. Current regulations impose threshold limits on heavy metal emissions. The reference method currently implemented for quantitative measurements at exhaust stacks consists of on-site sampling of heavy metals on filters for the particulate phase (the most prominent and only fraction considered in this study) prior to subsequent laboratory analysis. Results are therefore known only a few days after sampling. Stiffer regulations require the development of adapted tools allowing automatic, on-site or even in-situ measurements with temporal resolutions. The Laser-Induced Breakdown Spectroscopy (LIBS) technique was deemed as a potential candidate to meet these requirements. On site experiments were run by melting copper bars and monitoring emission of this element in an exhaust duct at a pilot-scale furnace in a French research center dedicated to metal casting. Two approaches designated as indirect and direct analysis were broached in these experiments. The former corresponds to filter enrichment prior to subsequent LIBS interrogation whereas the latter entails laser focusing right through the aerosol for detection. On-site calibration curves were built and compared with those obtained at laboratory scale in order to investigate possible matrix and analyte effects. Eventually, the obtained results in terms of detection limits and quantitative temporal monitoring of copper emission clearly emphasize the potentialities of the direct LIBS measurements.

Qualitative and quantitative investigation of chromium-polluted soils by laser-induced breakdown spectroscopy combined with neural networks analysis.

Laser-induced breakdown spectroscopy (LIBS) has been applied to the analysis of three chromium-doped soils. Two chemometric techniques, principal components analysis (PCA) and neural networks analysis (NNA), were used to discriminate the soils on the basis of their LIBS spectra. An excellent rate of correct classification was achieved and a better ability of neural networks to cope with real-world, noisy spectra was demonstrated. Neural networks were then used for measuring chromium concentration in one of the soils. We performed a detailed optimization of the inputs of the network so as to improve its predictive performances and we studied the effect of the presence of matrix-specific information in the inputs examined. Finally the inputs of the network--the spectral intensities--were replaced by the line areas. This provided the best results with a prediction accuracy and precision of about 5% in the determination of chromium concentration and a significant reduction of the data, too.

Laser-induced breakdown spectroscopy of composite samples: comparison of advanced chemometrics methods.

Laser-induced breakdown spectroscopy is used to measure chromium concentration in soil samples. A comparison is carried out between the calibration curve method and two chemometrics techniques: partial least-squares regression and neural networks. The three quantitative techniques are evaluated in terms of prediction accuracy, prediction precision, and limit of detection. The influence of several parameters specific to each method is studied in detail, as well as the effect of different pretreatments of the spectra. Neural networks are shown to correctly model nonlinear effects due to self-absorption in the plasma and to provide the best results. Subsequently, principal components analysis is used for classifying spectra from two different soils. Then simultaneous prediction of chromium concentration in the two matrixes is successfully performed through partial least-squares regression and neural networks.