Laser-induced Breakdown Spectroscopy Instrument Research Articles

Application of handheld/portable spectroscopic tools to the identification, inner stratigraphy and mapping of archaeological metal artefacts

Field handheld/portable instrumentations, such as in-situ geochemical analyzers, have the potential to assist efficiently targeted geochemical archaeometry campaigns in detecting and quantifying specific elements. Non-destructive portable energy dispersive x-ray fluorescence and micro-destructive handheld laser-induced breakdown spectroscopy (LIBS) instrumentation were utilized to investigate the elemental composition, internal stratigraphy by depth profiling and microscale compositional mapping of five copper and two iron alloy artefacts collected from various ancient graves in the Minervino Murge area, Apulia, Italy. The primary elements identified by both techniques included Cu, Sn and Pb in copper alloys, and Fe with minor amounts of Cu and Pb in iron alloys. Furthermore, the elements Al, Ca, Si, Mg, Na and K, mostly originated from soil contamination, and the trace elements Sb, Ni and Zn were detected. The satisfactory performance of both techniques was assessed by their capacity to provide reproducible elemental composition data. Finally, the depth profile and mapping achieved by LIBS contributed to understanding the metal processing and history of the objects studied, so confirming both techniques to be robust analytical tools in outdoor archaeology and archaeometry campaigns.

Quality index for Martian in-situ laser-induced breakdown spectroscopy data

Laser-induced breakdown spectroscopy (LIBS) has been widely implemented in Mars explorations as a versatile technique for in-situ chemical analysis. Determination of elemental composition from Martian LIBS typically relies on uni/multi-variate models trained on specific laboratory datasets, which in turn requires similarities between this training dataset and Martian data. However, plasma excitation can affect this similarity. The inability to identify insufficiently excited data may ignore poor focusing for some observations or lead to unawareness of compositional uncertainty. This study establishes the LIBS Quality Index (LQI) for Martian LIBS data quality by evaluating the carbon ionic doublet emission around 658 nm for the Martian atmosphere, assessing its signal-to-noise ratio as well as its deviation from the line profile standard of the instrument-specific laboratory dataset. This method allows the LQI to be used as a confidence level for assessing LIBS excitation quality and measurement for similarity with instrument-specific laboratory datasets. The LQI evaluation routines were established for ChemCam, MarSCoDe, and SuperCam. Validation of LQI was carried out on the laboratory datasets and Mars data with known quality assessments of the three instruments. The index was verified to be sensitive to input irradiance, applicable with three current Mars LIBS instruments, robust against various material types (except carbonate), and moderate interference from H, Fe, and Ti. Applying the LQI, the study was able to identify the remote micro-imager (RMI) autofocus as an optimal mechanism, to discover the influence of environmental conditions on data quality, to discuss the impact of target material consolidation and morphology on data quality, and to investigate the contribution of data quality to quantification uncertainty. These application examples demonstrated the feasibility of LQI as a useful and sensitive tool to assess data quality and its potential to aid current and future Martian LIBS explorations.

A Laser-Induced Breakdown Spectroscopy (LIBS) Instrument for In-Situ Exploration with the DLR Lightweight Rover Unit (LRU)

In the framework of the Helmholtz ARCHES project, a multitude of robots, including rovers and drones, were prepared for the autonomous exploration of a test site at the foothills of Mt. Etna, Sicily—a terrain resembling extraterrestrial locations such as the Moon. To expand the suite of tools and sensors available for the exploration and investigation of the test site, we developed a laser-induced breakdown spectroscopy (LIBS) instrument for the geochemical analysis of local geological samples. In alignment with the mission scenario, this instrument is housed in a modular payload box that can be attached to the robotic arm of the Lightweight Rover Unit 2 (LRU2), allowing the rover to use the instrument autonomously in the field. A compact Nd:YAG laser is utilized for material ablation, generating a micro-plasma that is subsequently analyzed with a small fiber-coupled spectrometer. A single-board computer controls the LIBS hardware components for data acquisition. In this study, we provide details of the ARCHES LIBS instrument implementation, report on preceding laboratory tests where the LRU2 operated the LIBS module for the first time, and showcase the results obtained during the successful ARCHES space analogue demonstration mission campaign in summer 2022 in Sicily.

Multielement simultaneous quantitative analysis of trace elements in stainless steel via full spectrum laser-induced breakdown spectroscopy

Laser-Induced Breakdown Spectroscopy (LIBS) instruments are increasingly recognized as valuable tools for detecting trace metal elements due to their simplicity, rapid detection, and ability to perform simultaneous multi-element analysis. Traditional LIBS modeling often relies on empirical or machine learning-based feature band selection to establish quantitative models. In this study, we introduce a novel approach—simultaneous multi-element quantitative analysis based on the entire spectrum, which enhances model establishment efficiency and leverages the advantages of LIBS. By logarithmically processing the spectra and quantifying the cognitive uncertainty of the model, we achieved remarkable predictive performance (R2) for trace elements Mn, Mo, Cr, and Cu (0.9876, 0.9879, 0.9891, and 0.9841, respectively) in stainless steel. Our multi-element model shares features and parameters during the learning process, effectively mitigating the impact of matrix effects and self-absorption. Additionally, we introduce a cognitive error term to quantify the cognitive uncertainty of the model. The results suggest that our approach has significant potential in the quantitative analysis of trace elements, providing a reliable data processing method for efficient and accurate multi-task analysis in LIBS. This methodology holds promising applications in the field of LIBS quantitative analysis.

Laser-Induced Breakdown Spectroscopy Applied to Elemental Analysis of Aqueous Solutions—A Comprehensive Review

Laser-induced breakdown spectroscopy (LIBS) has evolved considerably in recent years, particularly the application of portable devices for the elemental analysis of solids in the field. However, aqueous analysis using LIBS instruments, either in the laboratory or in the field, is rather rare, despite extensive research on the topic since 1984. Thus, our comprehensive review aims to provide a clear overview of this research to offer guidance to new users. To achieve this, we examined the literature published between 1984 and 2023, comparing various settings and parameters in a database. There are four different categories of LIBS instruments: laboratory-based, online, portable, and telescopic. Additionally, there are four main categories of sample preparation techniques: liquid bulk, liquid-to-solid conversion, liquid-to-aerosol conversion, and hydride generation. Various experimental setups are also in use, such as double-pulse. Moreover, different acquisition settings significantly influence the sensitivity and therefore the detection limits. Documentation of the different methods of sample preparation and experimental settings, along with their main advantages and disadvantages, can help new users make an informed choice for a particular desired application. In addition, the presentation of median detection limits per element in a periodic table of elements highlights possible research gaps and future research opportunities by showing which elements are rarely or not analysed and for which new approaches in sample preparation are required to lower the detection limits.

Sharing calibration information among laser-induced breakdown spectroscopy instruments using spectral line binning and calibration transfer

The ability of a publicly accessible preexisting laser-induced breakdown spectroscopy (LIBS) database to train multivariate models to predict rock and mineral compositions from other laboratories or from remotely collected spectra is evaluated using LIBS spectra collected on >2500 unique geological targets using three different instruments and a range of collection protocols. Datasets collected under increasingly disparate conditions are utilized, including a single instrument with different resolution settings; two different instruments with very similar ablation and collection optics; and a benchtop instrument and portable instrument with different collection protocols, resolutions, and plasma conditions. Cross-calibration among datasets is performed for a range of scenarios designed to test the efficacy of post-processing techniques.Major element predictions are most accurate when instrument parameters match among training and test spectra. Use of a piecewise direct standardization-partial least squares (PDS-PLS) calibration transfer algorithm reduces major element prediction uncertainties when the resolution of training and test spectra do not match. Even when training and test spectra are collected on different instruments, reasonable predictions can be derived by binning peak areas prior to training calibration models. Finally, incorporating a large, preexisting database into a smaller dataset collected under test conditions has the potential to greatly improve the reliability of predicted compositions. With careful consideration to match plasma conditions and utilize post-processing, existing large-scale LIBS calibration databases like the one used in this study can be used to boost LIBS accuracy and expand the potential of LIBS as a widely-applicable quantitative geochemical tool.

A Simple Laser-Induced Breakdown Spectroscopy Method for Quantification and Classification of Edible Sea Salts Assisted by Surface-Hydrophilicity-Enhanced Silicon Wafer Substrates.

Salt, one of the most commonly consumed food additives worldwide, is produced in many countries. The chemical composition of edible salts is essential information for quality assessment and origin distinction. In this work, a simple laser-induced breakdown spectroscopy instrument was assembled with a diode-pumped solid-state laser and a miniature spectrometer. Its performances in analyzing Mg and Ca in six popular edible sea salts consumed in South Korea and classification of the products were investigated. Each salt was dissolved in water and a tiny amount of the solution was dropped and dried on the hydrophilicity-enhanced silicon wafer substrate, providing homogeneous distribution of salt crystals. Strong Mg II and Ca II emissions were chosen for both quantification and classification. Calibration curves could be constructed with limits-of-detection of 87 mg/kg for Mg and 45 mg/kg for Ca. Also, the Mg II and Ca II emission peak intensities were used in a k-nearest neighbors model providing 98.6% classification accuracy. In both quantification and classification, intensity normalization using a Na I emission line as a reference signal was effective. A concept of interclass distance was introduced, and the increase in the classification accuracy due to the intensity normalization was rationalized based on it. Our methodology will be useful for analyzing major mineral nutrients in various food materials in liquid phase or soluble in water, including salts.

Detection and characterization of mono- and bimetallic nanoparticles produced by electrical discharge plasma generators using laser-induced breakdown spectroscopy

This work investigates the performance of laser induced breakdown spectroscopy (LIBS) for the detection and characterization of nanoparticles generated by spark discharge generators (SDG) and arc discharge generators (ADG). LIBS nanoaerosol analysis was carried out according to both on-line (in a gas stream) and off-line (after collection on a filter) methodologies for monometallic (Cu and Au) and bimetallic (AgCo, SnCu, AgAu) nanoparticles generated by ADG and SDG. It was shown that LIBS is not only capable of detecting the presence of nanoparticles in real time, but LIBS spectra can also be used to provide qualitative and quantitative compositional information, as well as estimates for the particle number concentration. This analytical capability, combined with the compact and robust character of LIBS instrumentation, can be put to use for the real-time monitoring of industrial gas-phase nanoparticle synthesis or environmental or workplace exposure to nanoparticles.

Identification possible preserved 2004 Indian Ocean tsunami deposit collected from Pulot Village in Aceh Besar Regency, Indonesia

Identification of the tsunami candidate layers deposited by the Indian Ocean tsunami in 2004 has been successfully performed in Pulot Village, located in Leupung district, Aceh Besar Regency, Aceh Province. Based on a study of tectonic activity before and after the earthquake, Pulot Village was reported as one of the potential locations for finding tsunami deposits. Tsunami sediment is collected by inserting a hand drill into the ground more than 1 m deep. The presence of the 2004 tsunami candidate layers was identified according to the Troels-Smith characteristics. Based on our visual analysis, the prospective tsunami layer is found at a depth of 4 cm with a thickness of 8-38 cm, which is characterized by fine-medium sized dark brown sand deposits on weathered paleo soil layers. The sharp boundaries with the layers above and below it and the presence of organic material (plant roots, stems and anthropogenic material) can also be clearly observed in the tsunami deposit layers. The candidate layer for the 2004 tsunami deposit found will be further studied geochemically using a laser-induced breakdown spectroscopy (LIBS) instrument.

Mobile Laser-Induced Breakdown Spectroscopy for Future Application in Precision Agriculture-A Case Study.

In precision agriculture, the estimation of soil parameters via sensors and the creation of nutrient maps are a prerequisite for farmers to take targeted measures such as spatially resolved fertilization. In this work, 68 soil samples uniformly distributed over a field near Bonn are investigated using laser-induced breakdown spectroscopy (LIBS). These investigations include the determination of the total contents of macro- and micronutrients as well as further soil parameters such as soil pH, soil organic matter (SOM) content, and soil texture. The applied LIBS instruments are a handheld and a platform spectrometer, which potentially allows for the single-point measurement and scanning of whole fields, respectively. Their results are compared with a high-resolution lab spectrometer. The prediction of soil parameters was based on multivariate methods. Different feature selection methods and regression methods like PLS, PCR, SVM, Lasso, and Gaussian processes were tested and compared. While good predictions were obtained for Ca, Mg, P, Mn, Cu, and silt content, excellent predictions were obtained for K, Fe, and clay content. The comparison of the three different spectrometers showed that although the lab spectrometer gives the best results, measurements with both field spectrometers also yield good results. This allows for a method transfer to the in-field measurements.

Interpreting convolutional neural network classifiers applied to laser-induced breakdown optical emission spectra

Laser-induced breakdown spectroscopy (LIBS) is a well-established industrial tool with emerging relevance in high-stakes applications. To achieve its required analytical performance, LIBS is often coupled with advanced pattern-recognition algorithms, including machine learning models. Namely, artificial neural networks (ANNs) have recently become a frequently applied part of LIBS practitioners’ toolkit. Nevertheless, ANNs are generally applied in spectroscopy as black-box models, without a real insight into their predictions. Here, we apply various post-hoc interpretation techniques with the aim of understanding the decision-making of convolutional neural networks. Namely, we find synthetic spectra that yield perfect expected classification predictions and denote these spectra class-specific prototype spectra. We investigate the simplest possible convolutional neural network (consisting of a single convolutional and fully connected layers) trained to classify the extended calibration dataset collected for the ChemCam laser-induced breakdown spectroscopy instrument of the Curiosity Mars rover. The trained convolutional neural network predominantly learned meaningful spectroscopic features which correspond to the elements comprising the major oxides found in the calibration targets. In addition, the discrete convolution operation with the learnt filters results in a crude baseline correction.

Quantitative analysis of Li, Na and K in single-element standard solutions using portable laser-induced breakdown spectroscopy

Laboratory-based laser-induced breakdown spectroscopy (LIBS) instruments have proven themselves for trace element analysis of an extensive range of elements. As portable devices have evolved greatly in recent years, new areas of field application are opening up. However, no portable LIBS (pLIBS) has yet been used for quantitative inorganic water analysis. The aim of this study was to explore whether pLIBS combined with a surface-enhanced liquid-to-solid conversion method can quantify light alkali element concentrations in standard solutions. Multivariate calibrations were performed with single-element standard solutions at detection limits of 0.006, 0.011 and 0.007 mg l −1 for Li, Na and K, respectively. Coefficients of determination ( R 2 ) for the calibration lines from 0.1 to 100 mg l −1 for Li and Na, and 0.1 to160 mg l −1 for K were between 0.96 and 0.99. It has been shown that the surface preparation technique used improves the homogeneity of the spread and shape of the evaporation residue and thus the accuracy and precision of the analysis. Hence, this study demonstrates that it is possible to quantify light alkali metals in single-element standard solutions in the range of 0.1 to 160 mg l −1 using handheld LIBS.

Sensitive and accurate determination of nitrogen in simulated Martian soil and environment with LIBS spectrum fusion and regression based on neural network

Nitrogen is a key element necessary for the emergence and development of life. It is one of the elements targeted by the landed missions on Mars in accordance with their scientific goal of investigation of habitability and search for traces of life. A gas chromatography mass spectrometer (GCMS) instrument on board the Mars Science Laboratory Curiosity rover has revealed the existence of oxidized nitrogen-bearing compounds on Martian surface with an equivalent nitrogen concentration up to 0.01 wt%. Although the detection with laser-induced breakdown spectroscopy (LIBS) also on board the Curiosity rover is desirable, the current performance of LIBS for nitrogen analysis does not show the capacity in terms of limit of detection (LOD) and accuracy. Research on a suitable method should therefore be first engaged in laboratory in order to guide further improvements of LIBS instrument on board Mars rover, as well as the data treatment method. Beyond the sensitivity issue, matrix effect also affects LIBS determination of nitrogen, especially due to its various chemical speciation in geological materials. Method research should answer thus double requirements of improving the sensitivity and reinforcing the robustness with respect to different nitrogen-bearing compounds. An experimental configuration of double detections with a narrow bandwidth Czerny-Tuner (CT) spectrometer and a broad bandwidth Echelle spectrometer, was implemented in this work, in such way that the first ensured a sensitive detection of emission lines from nitrogen and the second complemented with those from major elements in the sample. The fusion of the simultaneously acquired spectra took into account the emission characteristics of the both two types of elements, necessary for an effective and robust multivariate regression based on a neural network. In addition, for a better treatment of different chemical speciation of nitrogen in samples, generalized spectrum was used for training of regression models, after an unsupervised clustering having assigned a type label to each training spectrum. The trained model was tested by collections of independent test samples, resulting in a limit of detection (LOD) of 0.18 wt%, and a root mean square error of prediction (RMSEP) of 0.041 wt%, representing a step forward to nitrogen detection using LIBS on Mars.

A data analysis method to rapidly characterize gallium concentration in plutonium matrices using LIBS

The processing of actinide samples is a complex and costly endeavor that requires compositional analysis at various stages. Laser-induced breakdown spectroscopy (LIBS) has been used to analyze actinide-containing samples in many nuclear applications including waste management, fuel processing and forensics. The LIBS spectrum obtained from actinide materials are generally extremely complex, exhibiting many thousands of strong emission lines. This makes it difficult to identify other elements within the sample of interest, given the rich and dominant actinide spectrum. In this article we describe a recent effort to identify and quantify impurities and alloying constituents in plutonium matrices using a hand-held LIBS instrument that is used to rapidly and efficiently measure an emission spectrum from a material sample. We tabulate the emission line positions and intensities of plutonium. We report the development of machine-learning software that can identify gallium and quantify its concentration in plutonium matrices. This work has the potential to provide a rapid and nearly non-destructive technique that allows more confidence in characterizing the composition of materials that are present within complex actinide associated targets. We describe how our LIBS measurements and data analysis methods have successfully quantified the gallium concentration in a variety of samples.

Laser-induced breakdown spectroscopy instrument and spectral analysis for deep-ocean Fe-Mn crusts

IntroductionMarine ferromanganese crusts are potentially important metal resources.The deep-ocean research and survey ships often need to carry out rapid chemical element component analysis of mineral resources, so as to plan for the geological resource exploration mission.MethodsThe laser-induced breakdown spectroscopy can obtain the spectrum of chemical elements by the plasma excited by high-energy laser irradiation on the surface of the sample. A laser induced breakdown spectroscopy optical system for the detection of deepocean ferromanganese crusts is designed and built, which can meet the requirements of near-insitu chemical component detection of deep-ocean mineral resources on ocean-going survey ships.ResultsHyperspectral data of Fe-Mn crusts are carried out by the Laser-induced breakdown spectroscopy (LIBS) system during a deep-sea exploration mission at a depth of 2,490 m in the South China Sea. The experimental parameters of laser energy and spectral acquisition delay are optimized to improve the spectral measurement accuracy. Based on the calibration-free method, the significant spectral features of the chemical elements Fe and Mn were obtained through proper alignment with the National Institute of Standards and Technology (NIST) chemical element spectral library.DiscussionThe LIBS instrument can be placed on board long-range survey vessels in the future to provide a fast, convenient, accurate, and economical detection method for deep-ocean resource exploration.

Geochemical identification and classification of cherts using handheld laser induced breakdown spectroscopy (LIBS) supported by supervised machine learning algorithms

The elemental analysis of cherts is very important for understanding their geological history, formation and origin. In this work, chert samples collected from a mining complex located in the Gargano promontory (Apulia, Italy) have been investigated by laser-induced breakdown spectroscopy (LIBS) using a commercial handheld (h) LIBS instrument. In order to discriminate chert samples according to their mining site, the broadband LIBS spectra acquired on the specimens were subjected to multivariate data analysis. In particular, machine learning (ML) algorithms, including discriminant analysis (DA), support vector machine (SVM), and k-nearest neighbors (KNN), were applied to the data set from the averaged LIBS spectra of each sample dimensionally reduced by principal components analysis (PCA). Among the classifiers tested, the highest accuracy (72.34%) in identifying the chemical features of five different chert suites and discriminating their sources was achieved by Leave-One-Out Cross-Validation (LOO-CV) using the cosine KNN ML algorithm. The exclusion of PC1, which appeared to be mainly influenced by elements featuring large intra-class variation and engraved the clusterization, allowed to improve the accuracy of the classification reaching the value of 80.85% using the linear DA algorithm. The classification and discrimination between 2 classes (1 × 1) reached a high rate of correct predictions, which implied that this discrimination could be used as a subsequent mean to confirm if the class predicted in the previously performed multiclass classification was correct. In conclusion, the results of this study demonstrated that even chemically similar geological materials could be discriminated using LIBS analysis combined with appropriate multivariate chemometric data processing, in particular with the aim of recognizing and differentiating specific geological chert sources and identifying the origin of chert archaeological artifacts.

Portable nanosecond laser for handheld laser-induced breakdown spectroscopy instruments

The portable laser is indispensable for handheld laser-induced breakdown spectroscopy (LIBS) instruments. We developed a passive, Q-switched pulsed, Nd:YAG laser system with small size and low cost. The maximum pulse energy of the laser was 10 mJ and the pulse duration was <2 ns. The peak power was >5 MW. The laser exhibited very high stability without any cooling system and its relative standard deviation of the pulse energy was <0.2 % . The laser was also used as a light source of LIBS to measure the concentrations of Cr, Cu, Mn, and Ni in alloys. The experimental results show that the determination coefficient of the calibration curves for these four elements is all better than 0.99. For its shorter pulse duration, the laser can decrease the plasma shield effect to improve the coupling of laser pulse and the target and stability of spectra intensity. The portable laser developed is a good laser source for handheld LIBS instruments.

Feasibility of a Low-Power, Low-Resolution Laser-Induced Breakdown Spectroscopy Instrument for Analysis of Nickel Alloys: Quantification of the Major Alloying Elements and Classification.

A simple cost-effective laser-induced breakdown spectroscopy (LIBS) instrument was used for quantification of major elements in several nickel alloys and also sorting them. A compact low-power diode-pumped solid-state laser and a miniature low-resolution spectrometer were assembled for the LIBS instrument. Material properties of the nickel alloys depend mainly on the composition of the major elements, Ni, Cr, and Fe, ranging from a few to ∼60 wt%. The emission peaks at 547.7 nm, 520.4 nm, and 438.1 nm for Ni, Cr, and Fe, respectively, were chosen for this analysis. The analytical performance was found to be enough for the quantification of Ni, Cr, and Fe in the nickel alloys. Limits of detection and accuracy were estimated to be a few weight percent (wt%) and measurement precisions were less than 10% in terms of relative standard deviation. The calibration performance of this intensity-based method was compared with that of the "ratio method" which is used in conventional optical emission spectroscopy analyses. The comparison indicates that the intensity-based method is more appropriate with the low-performance LIBS instrument that detects emission peaks of only a few major elements. Also, multivariate modeling of the six different nickel alloy samples based on the emission peak intensities of Ni, Cr, and Fe was performed using k-nearest neighbors (KNN) and linear discriminant analysis (LDA). The KNN and ordinary LDA models showed 95.0% and 98.3% classification correctness for the separate test data set, respectively. To improve classification performance further, the two-step LDA model was trained. In this approach, the two closest sample classes responsible for the decrease in the classification correctness were separately modeled in the second step to exploit their difference effectively. The two-step LDA model showed 100% correctness in classifying the test objects. Our results indicate that such a low-performance LIBS instrument can be effectively utilized for quantitative analysis of the major elements in the nickel alloys and their rapid identification or sorting in combination with an appropriate multivariate modeling algorithm.

Accurate Quantitative Analysis of LIBS With Image Form Spectra by Using a Hybrid Deep Learning Model of a Convolutional Block Attention Module-Convolutional Neural Network-Long Short-Term Memory

With the development of miniaturized Laser-induced breakdown spectroscopy (LIBS) instruments, the desire for accuracy and stability of quantitative analysis of portable LIBS systems is growing. In this research, a hybrid deep learning model of a convolutional block attention module combined with a convolutional neural network and a long short-term memory (CBAM-CNN-LSTM) was for the first time used for quantitative analysis in a LIBS system with a portable spectrometer. Among them, the spectra collected by LIBS were in the form of the image, the CNN module was responsible for feature extraction of image spectra, mining deep features through the CBAM, and the simultaneous accurate quantitative analysis of Ca, Mg, Na and Ba was realized by the LSTM module. Meanwhile, a linear regression (LR), a CNN and a CNN-LSTM model were compared to the CBAM-CNN-LSTM model. The results showed that the performance of this model was much better than the LR model. More importantly, compared to CNN and CNN-LSTM, the average relative error of CBAM-CNN-LSTM was reduced by 80.5% and 68.1%, the average root mean square error was reduced by 56.7% and 53.4%, and the average stability was increased by 62.3% and 58.8%, respectively. In addition, the feature visualization results of CNN-LSTM and CBAM-CNN-LSTM displayed that CBAM can more effectively extract the features of characteristic peaks of corresponding elements and suppress the irrelative features. It indicated that the model can achieve an accurate and stable quantitative analysis of LIBS, which has the potential to be applied in the in-site analysis.

Generation and evolution of laser-induced shock waves under martian atmospheric conditions

SuperCam on NASA's Mars 2020 mission is the first laser-induced breakdown spectroscopy (LIBS) instrument on Mars that also employs a microphone to help with the evaluation of the LIBS spectra. When a LIBS measurement is performed in an atmosphere, an acoustic signal is emitted that can be recorded and used, for example, as a normalization standard to help in the interpretation of spectral data. The investigation of this pressure wave in Martian atmospheric conditions is important to better understand the relation between the acoustic signals and the LIBS spectra that are measured by SuperCam. However, the mechanisms involved in the generation of the pressure wave in Martian atmospheric conditions have not been studied in detail yet. To improve our understanding of how the acoustic signal is generated and how it can be used to aid in the analysis of the spectroscopic data, we developed an experimental setup combining different experimental methods to study the pressure waves generated during LIBS measurements in a simulated Martian atmosphere. It incorporates a schlieren imaging system for shock wave analysis, a plasma imaging setup for spatially and temporally resolved plasma analysis and a microphone to study the acoustic signal generated by the shock wave. The schlieren system is used to investigate the shock wave's density structure and expansion dynamics. The system was designed to record schlieren images in low-pressure Mars-analogue atmosphere with a high temporal resolution. We present first measurements including schlieren images of the laser-induced shock wave expanding from an iron target in a simulated Martian atmosphere. The measurements confirm numerical simulations of the density structure of laser-induced shock waves for the first time. Furthermore, we find that the Taylor-Sedov model gives a good estimate of the shock wave dynamics up until sonic speeds, when a linear model is appropriate. By comparing the dynamics of the shock wave to the expansion of the plasma, we find that the shock wave decouples from the luminous plasma between 400 ns and 500 ns after plasma ignition. We demonstrate the capabilities of this new setup by giving a detailed description of how the acoustic signal is generated from the expanding plasma. In future studies, the presented setup will be used to investigate the influence of material properties and experimental parameters on the generation of the shock wave and the acoustic signal to support the analysis of SuperCam measurements on Mars.