Laser-induced Breakdown Spectroscopy System Research Articles

Development of a portable laser-induced breakdown spectroscopy device for in situ and rapid characterization of wall composition on the first wall of EAST tokamak

During the long-pulse operation of nuclear fusion devices, plasma-material interaction processes such as material erosion, transportation of eroded materials, and impurity redeposition can deteriorate the performance of plasma-facing components (PFCs). Accurately identifying impurities and their spatial distributions on the first wall is crucial to comprehensively and deep understanding these processes. Laser-induced breakdown spectroscopy (LIBS) is a powerful spectral analytical technique that offers rapid, in situ, multi-elemental, and portable instrumentation advantages over the other techniques. Due to its unique and distinctive features, LIBS is a practical technique for composition and profilometry analysis in nuclear fusion devices. In this study, we report the development of a novel, compact, and portable LIBS device for in situ, real-time measurement of impurity distributions in nuclear fusion devices. This device is equipped with a mini-sized laser that can deliver 10 mJ per pulse, a main unit enclosing a power supply module, spectrometer modules, and customized software. The system was employed to characterize impurities on the first wall of the EAST tokamak under atmospheric conditions after the 2021 EAST spring-summer campaign. The successful in situ measurement demonstrates the feasibility of using the portable LIBS system integrated into a robotic arm in nuclear fusion device. The implement of this portable device can facilitate the operation of the nuclear fusion device by enabling real-time and continuous monitoring of impurities as well as the fuel retention.

Analysis of Cu and Zn contents in aluminum alloys by femtosecond laser-ablation spark-induced breakdown spectroscopy

Abstract To enhance the sensitivity of detecting Cu and Zn in aluminum alloys, we incorporated a spark discharge unit into the femtosecond laser-induced breakdown spectroscopy (fs-LIBS) system, forming a femtosecond laser ablation spark-induced breakdown spectroscopy (fs-LA-SIBS) system. We utilized both fs-LA-SIBS and fs-LIBS techniques to detect Cu and Zn in various aluminum alloy samples with different contents. Our results reveal that the spark discharge extends the duration of laser-induced plasma atomic radiation and amplifies the peak intensity of atomic radiation, thereby significantly increasing the time-integrated signal intensity of laser-induced plasma. Under the present experimental conditions, the limits of detection (LoDs) of Cu and Zn in aluminum alloy by fs-LA-SIBS are 16 and 12 ppm, respectively. In contrast, the LoDs of Cu and Zn by fs-LIBS technique under the same laser pulse energy are 96 and 84 ppm, respectively. Hence, for Cu, the LoDs by the fs-LA-SIBS technique are 1/6 of those by the fs-LIBS technique, and for Zn, the LoDs by the fs-LA-SIBS technique are 1/7 of those by the fs-LIBS technique. Our findings demonstrate that the fs-LA-SIBS technique is more sensitive than the fs-LIBS technique in the quantitative analysis of elements and may be a practical approach for elemental analysis in alloys.

The effect of ablation crater on geomaterials caused by laser shot accumulation on the laser-induced plasma and shock wave

The stability of laser-induced breakdown spectroscopy (LIBS) signal is vital for its application in geomaterials, which is challenged by spectral fluctuations caused by variable ablation craters. To investigate the influence of the ablation crater caused by laser shot accumulation, the shot-to-shot variation of plasma was analyzed from the aspects of shock wave propagation, ablation crater morphology, plasma morphology, and spectral evolution, which was conducted based on a fiber-optic LIBS (FO-LIBS) system. A pressure sensor was applied to monitor the shock wave signal due to the shock wave propagation characteristics related to the number of shots and types of binding materials. The evolution of the ablation crater profile and depth was tracked, and a linear correlation model between crater depth and shock wave energy was established. It was found that the spectral intensity increases first rapidly and gradually decreases, which can be attributed to the competition between the enhancement of spatial confinement and the plasma cooling phase. Strong self-reversal was observed due to the laser pulse accumulation, with an increase in the plasma expanding distance. This study illustrates the interaction of laser pulses, ablation, and plasma from different aspects, and provides a simple method to track the ablation process.

Investigating the Soil Unconfined Compressive Strength Based on Laser-Induced Breakdown Spectroscopy Emission Intensities and Machine Learning Techniques.

Laser-induced breakdown spectroscopy (LIBS) is a remarkable elemental identification and quantification technique used in multiple sectors, including science, engineering, and medicine. Machine learning techniques have recently sparked widespread interest in the development of calibration-free LIBS due to their ability to generate a defined pattern for complex systems. In geotechnical engineering, understanding soil mechanics in relation to the applications is of paramount importance. The knowledge of soil unconfined compressive strength (UCS) enables engineers to identify the behaviors of a particular soil and propose effective solutions to given geotechnical problems. However, the experimental techniques involved in the measurements of soil UCS are incredibly expensive and time-consuming. In this work, we develop a pioneering technique to estimate the soil unconfined compressive strength using artificial intelligent methods based on the spectra obtained from the LIBS system. Decision tree regression (DTR) and support vector regression learners were initially employed, and consequently, the adaptive boosting method was applied to improve the performance of the two single learners. The prediction power of the established models was determined using the standard performance evaluation metrics such as the root-mean-square error, CC between the predicted and actual soil UCS values, mean absolute error, and R2 score. Our results revealed that the boosted DTR exhibited the highest coefficient of correlation of 99.52% and an R2 value of 99.03% during the testing phase. To validate the models, the UCS values of soils stabilized with lime and cement were predicted with an optimum degree of accuracy, confirming the models' suitability and generalization strength for soil UCS investigations.

Detection of Off-Gassed Products From Molten Salts Using Laser-Induced Breakdown Spectroscopy.

The detection of off-gassed sodium from molten sodium nitrate (NaNO3) at temperatures between 330 °C and 505 °C and off-gassed calcium from molten lithium chloride-potassium chloride eutectic (LKE) mixtures at 510 °C with laser-induced breakdown spectroscopy (LIBS) was demonstrated. NaNO3 and LKE samples were melted in a custom-built crucible that promoted the generation of off-gassed products from the molten sample. The off-gassed products were analyzed with a LIBS system designed to probe the high-temperature environment. Na D emission lines, Na(I)588.99 nm and Na(I) 589.59 nm, were detected from the NaNO3 samples after reaching a temperature threshold, which indicated the occurrence of phase change. In LKE mixtures, the detection of Ca impurities at a concentration of 78 mg/kg was possible using the emission lines Ca(II) 393.66 nm and Ca(II) 395.85 nm. This work demonstrates the real-time monitoring capabilities of LIBS in high-temperature environments that simulate the conditions of molten salt reactors.

Combined in situ XRF–LIBS analyses as a novel method to determine the provenance of central Mediterranean obsidians

This work presents a new calibration method for determining the provenance of obsidian artefacts based on the combined use of XRF and laser-induced breakdown spectroscopy (LIBS). At first, obsidian samples collected from the main Mediterranean sources were characterized using portable XRF and LIBS systems. After data treatment, elemental information was used to carry out principal component analysis (PCA) for each technique. Rb, Sr, Zr, Y and Fe elements, detected by using XRF, were found to be the key parameters enabling obsidians discrimination. Likewise, LIBS data helped differentiating the analysed patterns by the intensity of their main elemental components (Ca, Al, Mg and K). After selecting the key parameters detected by each technique, a new data matrix combining XRF and LIBS data was finally built. According to PCA results, the discrimination of Mediterranean sources based on combined XRF–LIBS data ensured a higher reliability over mono-analytical models, by increasing the Euclidean distance between sources projections over three-dimensional principal components plots. Knowing that the representativeness of elemental data could be compromised by the presence of superficial degradation products or deposition patinas, a shot-to-shot comparison of in-depth LIBS analyses is finally proposed as a method to disclose whether the spot under analysis was superficially contaminated or altered. Thus, the proposed strategy based on the combined use of portable XRF and LIBS spectrometers could be particularly useful for the in situ analysis of obsidian artefacts that underwent superficial alteration or could be covered by patina products.Graphical abstract

Double-enhanced LIBS system with N2 atmosphere and cylindrical cavity confinement for quantitative analysis of Sr element in soil

To effectively improve the quality of laser-induced plasma spectra and the detection accuracy of laser-induced breakdown spectroscopy (LIBS) simultaneously for Sr element in soil, the dual mechanism of N2 atmosphere combined with cylindrical cavity confinement for laser-induced Sr plasma emission enhancement was proposed and investigated in this paper, and a multi-spectral fusion internal standard analysis model was established. Optimum enhancement effect can be achieved under the conditions of 2 mm diameter and 6 mm height of the confinement cavity in N2 atmosphere, where the enhancement factor was about 3.25, the signal-to-noise ratio reached 710.28, and the LIBS spectrum quality was the best; the relative standard deviation value (2.64%) was the smallest, and the LIBS signal reproducibility was the best. For the samples in this study, the limit of detection of the sample Sr elements under the dual enhancement mechanism was 34.60 mg kg−1, which was 40.43% lower than the limit of detection (LOD) without enhancement mechanism (58.08 mg kg−1), and the R 2 of the multispectral fusion internal standard model was 1.23% higher and the relative error was 3.41% lower than that of the internal standard method. The results showed that the dual enhancement mechanism combining N2 atmosphere and cylindrical cavity confinement improved the spectral quality, signal reproducibility, and detection sensitivity.

Quantification of alloying elements in steel targets: The LIBS 2022 regression contest

We present the results of the regression contest organized for the LIBS 2022 conference. While the motivation and design of the contest are briefly presented, the work focuses on the methodologies of the three best-performing teams. The employed spectral preprocessing strategies, choice of regression models and its optimization are detailed for each team separately. The aim of the contest reflects the long-term challenges faced by quantitative laser-induced breakdown spectroscopy (LIBS) analysis. Thus, the contest was designed with the purpose of providing a transparent platform for comparing and evaluating the large range of data processing tools available in the LIBS literature. Namely, the contest consisted of the quantification of two major (Cr, Ni) and two minor (Mn, Mo) elements in 15 steel targets. For constructing an appropriate regression model, spectra of 42 targets were provided. The spectra were collected using a commercially available laboratory-based LIBS system and made publicly available. The contest lasted 53 days during which the teams did not receive feedback. In total, 21 teams participated out of which the three best-performing methodologies are presented here. A single linear partial least squares model and two artificial neural network regression models are presented. The corresponding feature selection strategies included emission line selection, spectral range selection, and automatized wavelength selection. Various spectral normalization strategies and data augmentation strategies are also presented.

Design, construction, and validation of an in-situ groundwater trace element analyzer with applications in carbon storage

It is estimated that carbon emissions should reach net-zero by 2050 to meet important climate targets. Carbon capture is likely necessary to reach these targets, requiring a long-term storage solution such as geological carbon sequestration. However, as with any subsurface activity, leakage can occur, potentially impacting groundwater quality near the storage site. Rapid detection is essential to mitigate damage to this resource. Since CO2 will acidify groundwater, the concentrations of acid soluble minerals and associated cations will increase. Thus, an in-situ, real-time element analysis system based on laser-induced breakdown spectroscopy (LIBS) is under development to monitor these elements. The system splits the traditional LIBS system into a miniature, all-optical sensor head built around a passively Q-switch laser fiber coupled to a control unit. Previous work has validated the LIBS technique for use at high pressure as well as the split system design. In this work, a fieldable prototype sensor is developed and tested in an onsite monitoring well where trace elements concentrations (approx. 0–3 ppm) were tracked over 20 days. These concentrations varied in response to local rainfall, diluting with increased rain, demonstrating the ability of a LIBS-based sensor to track trace elements under real-world conditions.

Analytical approaches for food authentication using LIBS fingerprinting

Laser-induced breakdown spectroscopy (LIBS) is a widely acknowledged elemental analysis approach used in various study domains due to its rapid measurement capability and minimal sample-preparation requirements. Recently, there has been an increase in interest in the applications of LIBS in the realm of food safety and quality. Given that the majority of commonly consumed foods exhibit only modest trace-element variations, discovering predictive spectral patterns through multivariate analysis is crucial for the data-analysis pipeline. The efficacy of multivariate analysis and machine-learning algorithms to identify the most predictive spectral features, conduct class recognition and classification was evaluated in this paper, utilizing both a custom-developed benchtop LIBS system and a commercially available portable one. Specifically, this study's objective was to evaluate the performance of spectral variable selection using elastic-net multinomial logistic regression. The data processing pipeline and the LIBS hardware were evaluated in the context of food authentication and identification, a rising field of research addressing the issue of food fraud. Our findings indicated that classifying food samples with carefully selected fewer variables reduces model overfitting and improves the accuracy of LIBS pattern classification. In a broader sense, the results support the continued development of field-deployable, portable LIBS equipment designed for food authentication and fingerprinting activities.

Adversarial Data Augmentation and Transfer Net for Scrap Metal Identification Using Laser-Induced Breakdown Spectroscopy Measurement of Standard Reference Materials.

In this study, we propose a transfer learning-based classification model for identifying scrap metal using an augmented training dataset consisting of laser-induced breakdown spectroscopy (LIBS) measurement of standard reference material (SRMs) samples, considering varying experimental setups and environmental conditions. LIBS provides unique spectra for identifying unknown samples without complicated sample preparation. Thus, LIBS systems combined with machine learning methods have been actively studied for industrial applications such as scrap metal recycling. However, in machine learning models, a training set of the used samples may not cover the diversity of the scrap metal encountered in field measurements. Moreover, differences in experimental configuration, where laboratory standards and real samples are analyzed in situ, may lead to a wider gap in the distribution of training and test sets, dramatically reducing the performance of the LIBS-based fast classification system for real samples. To address these challenges, we propose a two-step Aug2Tran model. First, we augment the SRM dataset by synthesizing spectra of unobserved types through attenuation of dominant peaks corresponding to sample composition and generating spectra depending on the target sample using a generative adversarial network. Second, we used the augmented SRM dataset to build a robust real-time classification model with a convolutional neural network, which is further customized for the target scrap metal with limited measurements through transfer learning. For evaluation, SRMs of five representative metal types, including aluminum, copper, iron, stainless steel, and brass, are measured with a typical setup to form the SRM dataset. For testing, scrap metal from actual industrial fields is experimented with three different configurations, resulting in eight different test datasets. The experimental results show that the proposed scheme produces an average classification accuracy of 98.25% for the three experimental conditions, as high as the results of the conventional scheme with three separately trained and executed models. Additionally, the proposed model improves the classification accuracy of arbitrarily shaped static or moving samples with various surface contaminations and compositions, and even for differing ranges of charted intensities and wavelengths. Therefore, the proposed Aug2Tran model can be used as a systematic model for scrap metal classification with generalizability and ease of implementation.

Effect of cavity-confinement and microwave-assistance on the sensitivity of LIBS for detecting Cu content in pig feed

The laser-induced breakdown spectroscopy (LIBS) is a potential tool for detecting heavy metals in pig feed. In order to implement a highly sensitive LIBS system required for the detection of Copper in pig feed and achieve better detection results, this work combines cavity-confinement and microwave-assistance with the LIBS system. Based on the characteristic spectral intensity of Cu I 324.75 nm, four factors including delay time, laser energy, cavity-confinement diameter, and microwave-assisted power are optimized by using single factor method and response surface methodology (RSM) for obtaining suitable experimental parameters and detection performance. The detection results show that as compared with the single factor method, the optimal experimental parameters obtained by using RSM increase the intensity of the characteristic spectral lines by 21.69% and the detection limit is as low as 0.878 mg/kg. As compared with the traditional LIBS techniques, the use of cavity-confinement combined with microwave-assistance increase the detection sensitivity of LIBS technique by 18.6-fold. The results show that RSM effectively analyzes the interactions between factors and their effects on the response values to effectively obtain better values for each factor. This enhances the LIBS spectra by cavity-confinement and microwave-assistance, and improves the sensitivity of detection of Cu in the pig feeds, which is suitable for realizing the quantitative detection of low concentration elements in pig feeds.

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.

Effect of laser ablation angles on the ablated depth/mass and spectral intensity of laser-induced plasma on EAST-like plasma-facing materials in a vacuum

Real-time acquisition of the dynamic information on the surface elemental composition of the first wall, especially in the divertor region, is of great significance for understanding the plasma-wall interaction (PWI) process and optimizing the experimental conditions of the tokamak. The effect of laser ablation angles (LAAs) needs to be further evaluated for the in-situ laser-induced breakdown spectroscopy (LIBS) system on the EAST upper divertor that is being upgraded. LIBS experiments with a coaxial collection configuration were carried out at 1 × 10−5 mbar in the laboratory and over a wide range of LAAs from 0° to 72°. Tungsten (W), molybdenum (Mo), and copper (Cu) which are important plasma-facing materials in EAST were ablated under variable LAAs. Their ablation depth and ablation mass were measured by confocal microscopy. The depth ablation rate show that the larger the LAAs, the higher the depth resolution of LIBS, which is helpful for the depth-profiling analysis of the wall surface. The number of photons at the characteristic spectral line radiated from the three elements at a certain solid angle were obtained by using the absolute calibration method. The variation trend of the photon numbers with the increase of LAAs is consistent with ablation mass but slower than the downward trend of laser power density. By analyzing the variation of the spot size, the spatial intensity distribution of the laser, and the ablation crater morphology caused by the LAAs, it is found that the lateral ablation of the laser is the possible reason for the difference. Moreover, the calibration coefficient of spectral intensity under variable LAAs is obtained, which provides a reference for in-situ LIBS measurement.

Rapid Food Authentication Using a Portable Laser-Induced Breakdown Spectroscopy System.

Laser-induced breakdown spectroscopy (LIBS) is an atomic-emission spectroscopy technique that employs a focused laser beam to produce microplasma. Although LIBS was designed for applications in the field of materials science, it has lately been proposed as a method for the compositional analysis of agricultural goods. We deployed commercial handheld LIBS equipment to illustrate the performance of this promising optical technology in the context of food authentication, as the growing incidence of food fraud necessitates the development of novel portable methods for detection. We focused on regional agricultural commodities such as European Alpine-style cheeses, coffee, spices, balsamic vinegar, and vanilla extracts. Liquid examples, including seven balsamic vinegar products and six representatives of vanilla extract, were measured on a nitrocellulose membrane. No sample preparation was required for solid foods, which consisted of seven brands of coffee beans, sixteen varieties of Alpine-style cheeses, and eight different spices. The pre-processed and standardized LIBS spectra were used to train and test the elastic net-regularized multinomial classifier. The performance of the portable and benchtop LIBS systems was compared and described. The results indicate that field-deployable, portable LIBS devices provide a robust, accurate, and simple-to-use platform for agricultural product verification that requires minimal sample preparation, if any.

Classification of aluminum scrap by laser induced breakdown spectroscopy (LIBS) and RGB + D image fusion using deep learning approaches

Integrating multi-sensor systems to sort and monitor complex waste streams is one of the most recent innovations in the recycling industry. The complementary strengths of Laser-Induced Breakdown Spectroscopy (LIBS) and computer vision systems offer a novel multi-sensor solution for the complex task of sorting aluminum (Al) post-consumer scrap into alloy groups. This study presents two novel methods for fusing RGB and Depth images with LIBS using Deep Learning models. The first method is a single-output model that combines LIBS UNET and two DenseNets in a late fusion framework. The second method is a multiple-output model that uses the structure of the single-output model to enhance learning and avoid overfitting. In particular, the network has two outputs that enable the regularization of the individual sensors. A data set of 773 aluminum scrap pieces was created with two sets of ground truth-values, corresponding to the two envisaged sorting tasks, to train and evaluate the developed models. The first sorting task is separating Cast and Wrought (C&W) aluminum. The second is the division of the post-consumer aluminum scrap into three commercially interesting fractions. The single-output model performs best for separating C&W, with a Precision, Recall, and F1-score of 99%. The multiple-output model performs best for classifying the three selected commercial fractions, with a Precision, Recall, and F-score of 86%, 83%, and 84%, respectively. The presented data fusion method for LIBS and computer vision images encompasses the great potential for sorting post-consumer aluminum scrap. By sorting mixed post-consumer aluminum scrap in alloy groups, more wrought-to-wrought recycling can occur, and quality losses can be mitigated during recycling.

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.

Spectral library transfer between distinct laser-induced breakdown spectroscopy systems trained on simultaneous measurements

Artificial neural networks-based methodology to transfer spectra between distinct LIBS systems, trained on simultaneous measurements.

Initial Drift Correction and Spectral Calibration of MarSCoDe Laser-Induced Breakdown Spectroscopy on the Zhurong Rover

The Mars Surface Composition Detector (MarSCoDe) carried by the Zhurong rover of China’s Tianwen-1 mission uses Laser-Induced Breakdown Spectroscopy (LIBS) to detect and analyze the material composition on Martian surfaces. As one extraterrestrial remote LIBS system, it is necessary to adopt effective and reliable preprocessing methods to correct the spectral drift caused by the changes in environmental conditions, to ensure the analysis accuracy of LIBS scientific data. This paper focuses on the initial spectral drift correction and estimates the accuracy of on-board wavelength calibration on the LIBS calibration target measured by the MarSCoDe LIBS. There may be two cases during the instrument launch and landing, as well as the long-term operation: (a) the initial wavelength calibration relationship can still apply to the on-board LIBS measurement; and (b) the initial wavelength calibration relationship has been changed, and a new on-board calibration is needed to establish the current relationship. An approach of matching based on global iterative registration (MGR) is presented in respect to case (a). It is also compared with the approach of particle swarm optimization (PSO) for case (b). Furthermore, their accuracy is estimated with the comparison to the National Institute of Standards and Technology (NIST) database. The experimental results show that the proposed approach can effectively correct the drift of the on-board LIBS spectrum. The the root-mean-square error (RMSE) of the internal accord accuracy for three channels is 0.292, 0.223 and 0.247 pixels, respectively, compared with the corrected Ti-alloy spectrum and the NIST database, and the RMSE of the external accord accuracy is 0.232, 0.316 and 0.229 pixels, respectively, for other samples. The overall correction accuracy of the three channels is better than one-third of the sampling interval.

Improving the accuracy of high-repetition-rate LIBS based on laser ablation and scanning parameters optimization.

Laser-induced breakdown spectroscopy system based on high-repetition-rate microchip laser (HR-LIBS) has been widely used in elemental analysis due to its high energy stability, good portability and fast spectral acquisition speed. However, repeated ablation on powder pellets like soil and coal using HR-LIBS system encounters the problem of serious decline in measurement accuracy. In this work, the relationship between laser ablation and scanning parameters, their correlation with spectral intensity, as well as the optimization approach were fundamentally studied. The correlations among the crater overlapping rate, crater depth and spectral intensity were obtained. An HR-LIBS system with microchip laser (4 kHz repetition rate, 100 µJ laser pulse energy) to perform repeated scanning ablation was established. A theoretical model of the ablation crater morphology for repeated scanning ablation was developed. By taking soil pellets as the experimental samples, the linear fitting curves of crater depth and the spectral intensity ratio were established with the R2 of 0.90∼0.99. The experimental results showed that as the crater depth developed during repeated ablation, the Si-normalized spectral intensity decreased, and thus the spectral repeatability decreased. It was found that by optimizing the overlapping rate to form a flat crater bottom, the confinement effect of the crater on the plasma could be avoided. As a result, the spectral repeatability was significantly improved. The relative standard deviation (RSD) of Si-normalized spectral intensity was improved from 5% to 0.6%. Finally, repeated ablation was performed with the optimized overlapping rate on soil pellets. The R2 of calibration curves of Fe, Mg, Ca, and Al were all above 0.993, and the average RSDs were between 0.5% and 1%. This study provides a fast, accurate, and stable method for the analysis of the samples consisting of various materials with high heterogeneity.