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A joint project of partners from industry and research institutions for the research and construction of an analysis system for an automated, sensor-supported sorting of construction and demolition waste will be presented. This is intended to supplement or replace the previously practiced manual sorting, which harbors many risks and dangers for the staff and only enables obvious, visually detectable differences for separation. The method of laser-induced breakdown spectroscopy is to be used in combination with hyperspectral sensors. Due to the jointly processed information (data fusion), this should lead to a significant improvement in the separation of types. In addition to the sorting of different materials (concrete, main masonry building materials, organic components, glass, etc.), impurities such as SO3-containing building materials (gypsum, aerated concrete, etc.) could also be detected and separated.
The subsequent recycling and sales opportunities are examined, such as the use of recycled aggregates in concrete, the recycling of building materials containing sulphate as a gypsum substitute for the cement industry or the agglomeration of synthetic lightweight aggregates for lightweight concrete or as a substrate for green roofs. At the same time, it is investigated whether soluble components (sulfates, heavy metals, etc.) can be detected by LIBS without a wet chemical analysis and what impact the recycling materials have on the environment.
The entire value chain is examined using the example of the Berlin location in order to minimize economic / technological barriers and obstacles on a cluster level and to sustainably increase the recovery and recycling rates.
The performance of the Monte Carlo (MC) algorithm for calibration-free LIBS was studied on the example of a simulated spectrum that mimics a metallurgical slag sample. The underlying model is that of a uniform, isothermal, and stationary plasma in local thermodynamical equilibrium.
Based on the model, the algorithm generates from hundreds of thousands to several millions of simultaneous configurations of plasma parameters and the corresponding number of spectra. The parameters are temperature, plasma size, and concentrations of species. They are iterated until a cost function, which indicates a difference between synthetic and simulated slag spectra, reaches its minimum. After finding the minimum, the concentrations of species are read from the model and compared to the certified values. The algorithm is parallelized on a graphical processing unit (GPU) to reduce computational time. The minimization of the cost function takes several minutes on the GPU NVIDIA Tesla K40 card and depends on the number of elements to be iterated. The intrinsic accuracy of the MC calibration-free method is found to be around 1% for the eight elements tested. For a real experimental spectrum, however, the efficiency may turn out to be worse due to the idealistic nature of the model, as well as incorrectly chosen experimental conditions. Factors influencing the performance of the method are discussed.
Improved Data Processing for Accurate Plasma Diagnostics with Implications for Calibration-Free LIBS
(2023)
Many LIBS papers report the determination of plasma temperature using the Boltzmann plot method or the determination of electron density using the Stark line broadening relation. This requires measuring the integrated intensities of the spectral lines and the linewidth under the assumption of optical thinness. It is taken for granted that this can be easily done either by working with the raw spectra or by fitting the appropriate function to the observed spectral lines. However, extracting the necessary information from raw spectral data is not as easy as it might seem. The quality of such extraction will depend to a decisive extent on the type of spectral instrument used. The spectrum emitted by the plasma is distorted by the device; an example is shown in Fig. 1. The elimination of this distortion belongs to the class of inverse problems, the successful solution of which fundamentally depends on the quality of the available information. When it comes to spectroscopy, the quality of information primarily means high spectral resolution and low noise. Not all spectrometers used in LIBS can provide the quality needed to solve the inverse problem; this casts doubt on many published plasma measurements. This communication will be devoted to the general shortcomings of spectral data processing and the inaccuracies in determining the plasma parameters resulting from these shortcomings. The analysis is based on the use of synthetic spectra generated by plasma with known temperature, particle density and electron density. The estimation of errors caused by inadequate processing of spectral data is made by comparing the initial and measured plasma parameters from the spectra. Recommendations will be made for which analytic function best approximates the observed spectral lines, and how data processing errors affect the accuracy of calibration-free LIBS will be discussed. These issues were only partially covered in previously published works, for example [1, 2].
In the LIBS literature, almost every second article reports the determination of the plasma temperature using the Boltzmann plot method or the determination of the electron density using the Stark line broadening relation. The first requires the measurement of the integrated intensities of the spectral lines, and the second requires the measurement of the linewidth, under the same assumption of optical thinness. It is taken for granted that this can be easily done either by working with the raw spectra or by fitting an appropriate function to the observed spectral lines. As a rule, reported data are not verified either by an alternative method (e.g., Thomson scattering) or by computer simulations using synthetic spectra.
However, the question of how to extract the necessary information from the raw spectral data is not as simple as it might seem. The quality of such an extraction will depend critically on the type of spectral instrument used, its resolution, and the noise superimposed on the data. The problem is that we do not see the spectrum emitted by the plasma, but the spectrum distorted by the measurement; an exaggerated example of such a distortion is shown in Fig. 1. The elimination of this distortion belongs to the class of inverse problems, the so-called ill-posed problems, whose successful solution crucially depends on the quality of the information available. When it comes to spectroscopy, quality of information primarily means high spectral resolution and low noise. Not all spectrometers used in LIBS can provide the quality needed to solve the inverse problem; this casts doubt on many published plasma measurements.
The current presentation will be devoted to general shortcomings in the processing of spectral data and inaccuracies in the determination of plasma parameters resulting from these shortcomings. The analysis is based on the use of synthetic spectra produced by plasma with known characteristics, i.e., temperature, species densities, and electron density. The estimation of errors caused by inadequate processing of spectral data is made by comparing the initial and reconstructed plasma parameters. Recipes will be given for which the analytic function best approximates the observed spectral lines, and how data processing errors affect accuracy of calibration-free LIBS will be discussed. These issues were only partially covered in previously published works, for example [1, 2, 3].
Laser breakdown spectroscopy (LIBS) is a common tool for applications in various fields of science and technology. Originally an atomic analysis technique, LIBS was later extended to molecular analysis due to the transient nature of the laser-induced plasma, which develops from a hot dissociation stage on a nanosecond to several microsecond scale to a relatively cold recombination stage on a scale of 10 to 100 microseconds after breakdown. Molecules formed during the recombination stage or incompletely dissociated after ablation can be efficiently detected, allowing the analysis of "difficult" elements or even molecular isotopes. However, with a small amount of ablated material and a short lifetime of the luminous plasma, analytical signals, especially molecular ones, can be very weak.
Several methods have been proposed for reheating the plasma and increasing its lifetime, for example, a two-pulse LIBS or a LIBS combined with microwave radiation or with an electric spark discharge. Here we propose another one, LIBS combined with a capacitively coupled RF discharge at 13.6 MHz. The advantages of this combination are an increase in the lifetime of atomic and molecular emission and operation in a low-pressure atmosphere, which significantly reduces pressure line broadening and allows high-resolution spectroscopy. Another major advantage is operating in a chemically controlled atmosphere that can predictably drive desired chemical reactions. In this presentation, we will show the first results obtained with RF-LIBS combination. These will include separate and joint characterization of LIBS and RF plasmas and evaluation of its potential for elemental and molecular analysis and for plasma enhanced chemical vapor deposition.