Uranium measurements using laser-induced breakdown spectroscopy in lithium chloride-potassium chloride salt of pyroprocessing

In pyroprocessing,uranium(U) is recovered from molten LiCl-KCl salt,and,for safeguard purposes,it is important to analyze the U and Plutonium(Pu) concentrations in a timely manner.In the present work,salt samples containing U were fabricated.The laser used in the present work was an Nd:YAG laser with a wavelength of 532 nm,a laser energy on the sample of11.5 mJ,and a pulse repetition rate of 10 Hz.The plasma emission light was measured with an Echelle spectrometer.A total of 100 points on the sample surface were measured as the laser incident position was changed.The U and potassium(K) peaks in the spectrum were identified.Univariate and multivariate analyzes were conducted to determine the accuracy and limit of detection(LOD) of the laser-induced breakdown spectroscopy.     

Time-resolved evaluation of uranium plasma in different atmospheres by laser-induced breakdown spectroscopy

This work reports spectroscopic studies of uranium containing plasma generated in air and argon environments. The 532 nm Q-switched Nd:YAG laser generates the optical breakdown plasma, which was recorded by a spectrometer and an intensified charge coupled device having a resolution of 25 pm. Neutral and ionized uranium lines in the wavelength range of 385.8–391.9 nm indicate significant width and shift variations during the first few microseconds. Electron temperature and density of the plasma are determined using the Boltzmann plot and the Saha–Boltzmann equation at various time delay. The study reveals the power law decay pattern of electron temperature and density, which changes to exponential decay pattern if large gate- width is used to acquire the signal, due to an averaging effect.     

Quantitative analysis of steel and iron by laser-induced breakdown spectroscopy using GA-KELM

According to the multiple researches in the last couple of years, laser-induced breakdown spectroscopy(LIBS) has shown a great potential for rapid analysis in steel industry.Nevertheless, the accuracy and precision may be limited by complex matrix effect and selfabsorption effect of LIBS seriously. A novel multivariate calibration method based on genetic algorithm-kernel extreme learning machine(GA-KELM) is proposed for quantitative analysis of multiple elements(Si, Mn, Cr, Ni, V, Ti, Cu, Mo) in forty-seven certified steel and iron samples.First, the standardized peak intensities of selected spectra lines are used as the input of model.Then, the genetic algorithm is adopted to optimize the model parameters due to its obvious capability in finding the global optimum solution. Based on these two steps above, the kernel method is introduced to create kernel matrix which is used to replace the hidden layer's output matrix. Finally, the least square is applied to calculate the model's output weight. In order to verify the predictive capability of the GA-KELM model, the R-square factor(R~2), Root-meansquare Errors of Calibration(RMSEC), Root-mean-square Errors of Prediction(RMSEP) of GAKELM model are compared with the traditional PLS algorithm, respectively. The results confirm that GA-KELM can reduce the interference from matrix effect and self-absorption effect and is suitable for multi-elements calibration of LIBS.     

Quantitative analysis and time-resolved characterization of simulated tokamak exhaust gas by laser-induced breakdown spectroscopy

Tokamak exhaust is an important part of the deuterium-tritium fuel cycle system in fusion reactions. In this work, we present a laser-induced breakdown spectroscopy (LIBS)-based method to monitor the gas compositions from the exhaust system in the tokamak device. Helium (He), a main impurity in the exhaust gas, was mixed with hydrogen (H2) in different ratios through a self-designed gas distribution system, and sealed into a measurement chamber as a standard specimen. A 532 nm wavelength laser pulse with an output power of 100 mJ was used for plasma excitation. The time-resolved LIBS is used to study the time evolution characteristics of the signal strength, signal-to-background ratio (SBR), signal-to-noise ratio (SNR) and relative standard deviation (RSD) of the helium and hydrogen characteristic lines. The Boltzmann two-line method was employed to estimate the plasma temperature of laser-induced plasma (LIP). The Stark-broadened profile of He I 587.56 nm was exploited to measure the electron density. From these studies, an appropriate time was determined in which the low RSD% was consistent with the high signal-to-noise ratio. The He I 587.56 nm and Hα emission lines with good signal-to-noise ratio were extracted from the spectrum and used in the external standard method and internal standard method for quantitative analysis. The test results for mixed gas showed that the average relative error of prediction was less than 11.15%, demonstrating the great potential of LIBS in detecting impurities in plasma exhaust gas.     

Analytical study of seashell using laser-induced breakdown spectroscopy

Seashell has been applied as an indicator for ocean research and element analysis of the seashell is used to track biological or environmental evolution.In this work,laser-induced breakdown spectroscopy(LIBS) was applied for elementary analysis of an ezo scallop-shell,and a graphite enrichment method was used as the assistance.It was found that LIBS signal intensity of Ca fluctuated less than 5%,in spite of the sampling positions,and Sr/Ca was related to the shell growth.A similar variation was also found when using a direct LIBS analysis on the shell surface,and it might be more practicable to track shell growth by investigating Sr/Ca ratio with Sr ionic line at 421.6 nm.The obtained results prove that calcium(Ca) is qualified as an internal reference for shell analysis,and LIBS is a potential analytical method for seashell study.     

Distinguish Fritillaria cirrhosa and nonFritillaria cirrhosa using laser-induced breakdown spectroscopy

As traditional Chinese medicines, Fritillaria from different origins are very similar and it is difficult to distinguish them. In this study, the laser-induced breakdown spectroscopy combined with learning vector quantization(LIBS-LVQ) was proposed to distinguish the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa. We also studied the performance of linear discriminant analysis, and support vector machine on the same data set. Among these three classifiers, LVQ had the highest correct classification rate of 99.17%. The experimental results demonstrated that the LIBS-LVQ model could be used to differentiate the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa.     

Effect of cylindrical cavity height on laser-induced breakdown spectroscopy with spatial confinement

In this paper,we present a study on the spatial confinement effect of laser-induced plasma with a cylindrical cavity in laser-induced breakdown spectroscopy(LIBS).The emission intensity with the spatial confinement is dependent on the height of the confinement cavity.It is found that,by selecting the appropriate height of cylindrical cavity,the signal enhancement can be significantly increased.At the cylindrical cavity(diameter = 2 mm) with a height of 6 mm,the enhancement ratio has the maximum value(approximately 8.3),and the value of the relative standard deviation(RSD)(7.6%) is at a minimum,the repeatability of LIBS signal is best.The results indicate that the height of confinement cavity is very important for LIBS technique to reduce the limit of detection and improve the precision.     

An efficient procedure in quantitative analysis using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy has become a general-purpose technique, and internal standard calibration is a common method for quantitative analysis. Calibration models should be reconstructed for different systems and application environments. This study presents an efficient procedure in the construction and selection of calibration models for LIBS analysis. The procedure concludes data preprocess, calibration model construction, and concentration calculation. These steps can be programmed without manual intervention. Results of the quantitative analysis of Ni-based alloys using the proposed procedure are presented in this study.Ten elements are calibrated, and most have an average relative standard error of less than 10%.The proposed procedure is an effective process for constructing and selecting calibration models.     

Elemental and proximate analysis of coal by x-ray fluorescence assisted laser-induced breakdown spectroscopy

Although laser-induced breakdown spectroscopy (LIBS), as a fast on-line analysis technology, has great potential and competitiveness in the analysis of chemical composition and proximate analysis results of coal in thermal power plants, the measurement repeatability of LIBS needs to be further improved due to the difficulty in controlling the stability of the generated plasmas at present. In this paper, we propose a novel x-ray fluorescence (XRF) assisted LIBS method for high repeatability analysis of coal quality, which not only inherits the ability of LIBS to directly analyze organic elements such as C and H in coal, but also uses XRF to make up for the lack of stability of LIBS in determining other inorganic ash-forming elements. With the combination of elemental lines in LIBS and XRF spectra, the principal component analysis and the partial least squares are used to establish the prediction model and perform multi-elemental and proximate analysis of coal. Quantitative analysis results show that the relative standard deviation (RSD) of C is 0.15%, the RSDs of other elements are less than 4%, and the standard deviations of calorific value, ash content, sulfur content and volatile matter are 0.11 MJ kg−1, 0.17%, 0.79% and 0.41% respectively, indicating that the method has good repeatability in determination of coal quality. This work is helpful to accelerate the development of LIBS in the field of rapid measurement of coal entering the power plant and on-line monitoring of coal entering the furnace.     

Remote open-path laser-induced breakdown spectroscopy for the analysis of manganese in steel samples at high temperature

A remote open-path laser-induced breakdown spectroscopy(LIBS) system was designed and studied in the present work for the purpose of combining the LIBS technique with the steel production line. In this system, the relatively simple configuration and optics were employed to measure the steel samples at a remote distance and a hot sample temperature. The system has obtained a robustness for the deviation of the sample position because of the open-path and alloptical structure. The measurement was carried out at different sample temperatures by placing the samples in a muffle furnace with a window in the front door. The results show that the intensity of the spectral lines increased as the sample temperature increased. The influence of the sample temperature on the quantitative analysis of manganese in the steel samples was investigated by measuring ten standard steel samples at different temperatures. Three samples were selected as the test sample for the simulation measurement. The results show that, at the sample temperature of 500 ℃, the average relative error of prediction is 3.1% and the average relative standard deviation is 7.7%, respectively.     

Detection of K in soil using time-resolved laser-induced breakdown spectroscopy based on convolutional neural networks

One of the technical bottlenecks of traditional laser-induced breakdown spectroscopy (LIBS) is the difficulty in quantitative detection caused by the matrix effect. To troubleshoot this problem, this paper investigated a combination of time-resolved LIBS and convolutional neural networks (CNNs) to improve K determination in soil. The time-resolved LIBS contained the information of both wavelength and time dimension. The spectra of wavelength dimension showed the characteristic emission lines of elements, and those of time dimension presented the plasma decay trend. The one-dimensional data of LIBS intensity from the emission line at 766.49 nm were extracted and correlated with the K concentration, showing a poor correlation of R²_c=0.0967, which is caused by the matrix effect of heterogeneous soil. For the wavelength dimension, the two-dimensional data of traditional integrated LIBS were extracted and analyzed by an artificial neural network (ANN), showing R²_v=0.6318 and the root mean square error of validation (RMSEV)=0.6234. For the time dimension, the two-dimensional data of time-decay LIBS were extracted and analyzed by ANN, showing R²_v=0.7366 and RMSEV=0.7855. These higher determination coefficients reveal that both the non-K emission lines of wavelength dimension and the spectral decay of time dimension could assist in quantitative detection of K. However, due to limited calibration samples, the two-dimensional models presented over-fitting. The three-dimensional data of time-resolved LIBS were analyzed by CNNs, which extracted and integrated the information of both the wavelength and time dimension, showing the R²_v=0.9968 and RMSEV=0.0785. CNN analysis of time-resolved LIBS is capable of improving the determination of K in soil.     

In-depth analysis of ITER-like samples composition using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopic in-depth measurements were undertaken for two ITER-like calibrated multi-layered samples made of W-Mo or W/C layers on Ti-substrates. The samples were previously characterized by glow discharge optical emission spectroscopy. For laser-induced breakdown spectroscopic measurements, pulses generated by Nd:YAG laser sources with 1064 nm, 532 nm, 355 nm and 266 nm wavelengths were applied. The effects of laser beam shaping, fluence and wavelength as well as the gas nature (air, Ar, He) and pressure were investigated. The results obtained with laser-induced breakdown spectroscopic in-depth measurements were compared to those obtained with glow discharge optical emission spectroscopy and found to be in agreement. However, a mixing of the layers was observed and attributed to diffusion through the melted material and to the non-homogeneity of the laser beam spatial distribution. The depth resolution was found of the order of several thermal diffusion lengths but should be improved by using picosecond laser pulse duration.The results promote applications to tritium concentration measurements with depth resolution in the deposited layers of Tokamak first walls, as in the case of the future fusion reactor ITER.     

Cluster analysis of polymers using laser-induced breakdown spectroscopy with K-means

Laser-induced breakdown spectroscopy(LIBS) combined with K-means algorithm was employed to automatically differentiate industrial polymers under atmospheric conditions.The unsupervised learning algorithm K-means were utilized for the clustering of LIBS dataset measured from twenty kinds of industrial polymers.To prevent the interference from metallic elements,three atomic emission lines(C I 247.86 nm,H I 656.3 nm,and O I 777.3 nm) and one molecular line C–N(0,0) 388.3 nm were used.The cluster analysis results were obtained through an iterative process.The Davies–Bouldin index was employed to determine the initial number of clusters.The average relative standard deviation values of characteristic spectral lines were used as the iterative criterion.With the proposed approach,the classification accuracy for twenty kinds of industrial polymers achieved 99.6%.The results demonstrated that this approach has great potential for industrial polymers recycling by LIBS.     

Quantitative analysis of saindha salt using laser induced breakdown spectroscopy and cross-validation with ICP-MS

Saindha salt is considered to be more advantageous than the other edible salts for the patients suffering from diabetes,blood pressure and kidney diseases.To explore the constituent elements of this salt,laser induced breakdown spectroscopy(LIBS) has been exploited for its qualitative and quantitative analysis.The third harmonic(355 nm) of a Q-switched Nd:YAG laser has been used to produce the saindha salt plasma and the time integrated optical emission spectra were registered using a set of six miniature spectrometers covering the spectral range of 230-805 nm.The spectroscopic analysis of the emission spectra predominately revealed numerous neutral or singly ionized emission lines of Ca,Mg,Na,K,Fe,Sr,Si,Li and Al.The laser produced plasma was characterized by calculating the electron temperature from the Boltzmann plots and the electron number density from the Stark broadened line profile as a function of laser irradiance and distance from the target sample.The relative concentration of the constituent elements was extracted by the integrated line intensities of the strongest spectral line of each element using the self-calibration-LIBS(SC-LIBS) and one-line calibration free-LIBS(OLCF-LIBS) methods.For cross-validation,the LIBS results have been compared with that obtained from the inductively coupled plasma-mass spectroscopy(ICP-MS) showing good agreement.     

Calibration curve and support vector regression methods applied for quantification of cement raw meal using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS) is a qualitative and quantitative analytical technique with great potential in the cement industrial analysis. Calibration curve(CC) and support vector regression(SVR) methods coupled with LIBS technology were applied for the quantification of three types of cement raw meal samples to compare their analytical concentration range and the ability to reduce matrix effects, respectively. To reduce the effects of fluctuations of the pulse-to-pulse, the unstable ablation and improve the reproducibility, all of the analysis line intensities were normalized on a per-detector basis. The prediction results of the elements of interest in the three types of samples, Ca, Si, Fe, Al, Mg, Na, K and Ti, were compared with the results of the wet chemical analysis. The average relative error(ARE),relative standard deviation(RSD) and root mean squared error of prediction(RMSEP) were employed to investigate and evaluate the prediction accuracy and stability of the two prediction methods. The maximum average ARE of the CC and SVR methods is 34.62% instead of 6.13%,RSD is 40.89% instead of 7.60% and RMSEP is 1.34% instead of 0.43%. The results show that SVR method can accurately analyze samples within a wider concentration range and reduce the matrix effects, and LIBS coupled with it for a rapid, stable and accurate quantification of different types of cement raw meal samples is promising.     

Improvement in classification accuracy of stainless steel alloys by laser-induced breakdown spectroscopy based on elemental intensity ratio analysis

Laser-induced breakdown spectroscopy(LIBS) is a useful technique for accurate sorting of metal scrap by chemical composition analysis.In this work,a method for intensity-ratiobased LIBS classification of stainless steel applicable to highly fluctuating LIBS signal conditions is proposed.The spectral line pairs for intensity ratio calculation are selected according to elemental concentration and upper levels of emission lines.It is demonstrated that the classification accuracy can be significantly improved from that of full-spectra principal component analysis or intensity-based analysis.The proposed method is considered to be suited to an industrial scrap sorting system that requires minimal maintenance and low system price.     

Quantitative analysis of C,Si, Mn,Ni, Cr and Cu in low-alloy steel under ambient conditions via laser-induced breakdown spectroscopy

A diode-pumped solid-state laser(DPSSL) with a high energetic stability and long service life is applied to ablate the steel samples instead of traditional Nd:YAG laser pumped by a xenon lamp,and several factors, such as laser pulse energy, repetition rate and argon flow rate, that influence laser-induced breakdown spectroscopy(LIBS) analytical performance are investigated in detail.Under the optimal experiment conditions, the relative standard deviations for C, Si, Mn, Ni, Cr and Cu are 3.3%–8.9%, 0.9%–2.8%, 1.2%–4.1%, 1.7%–3.0%, 1.1%–3.4% and 2.5%–8.5%,respectively, with the corresponding relative errors of 1.1%–7.9%, 1.0%–6.3%, 0.4%–3.9%,1.5%–6.3%, 1.2%–4.0% and 1.2%–6.4%. Compared with the results of the traditional spark discharge optical emission spectrometry technique, the analytical performance of LIBS is just a little inferior due to the less stable laser-induced plasma and smaller amount of ablated sample by the laser. However, the precision, detection limits and accuracy of LIBS obtained in our present work were sufficient to meet the requirements for process analysis. These technical performances of higher stability of output energy and longer service life for DPSSL, in comparison to the Q-switch laser pumped by xeon lamp, qualify it well for the real time online analysis for different industrial applications.     

A novel strategy for quantitative analysis of soil pH via laser-induced breakdown spectroscopy coupled with random forest

pH is one of the significant properties of soil,and is closely related to the decomposition of soil organic matter,anion-cation balance,growth of plants and many other soil processes.In the present work,laser-induced breakdown spectroscopy(LIBS) technique coupled with random forest(RF) was proposed to quantify the pH of soil.First,LIBS spectra of soil was collected,and some common elements in soil were identified based on the National Institute of Science and Technology database.Then,in order to obtain a better predictive result,the influence of different input variables(full spectrum,different spectral ranges,the intensity of characteristic bands and characteristic lines) on the predictive performance of RF calibration model was explored with the evaluation indicators of root mean square error(RMSE) and coefficient of determination(R2),the characteristic bands of four elements(AI,Ca,Mg and Si) were determined as the optimal input variables.Finally,the predictive performance of RF calibration model was compared with partial least squares calibration model with the optimal input variables and model parameters,and RF calibration model showed a better predictive performance,and the four evaluation indicators of R_p~2,RMSEP,mean absolute error and mean relative error were 0.9687,0.1285,0.1114 and 0.0136,respectively.It indicates that LIBS technique coupled with RF algorithm is an effective method for pH determination of soil.     

Online calibration of laser-induced breakdown spectroscopy for detection of heavy metals in water

In order to reduce the fluctuation of LIBS detection spectrum of liquid sample, the full-spectrum sum method and the internal standardization method is adopted, using an equal-RSD normalization algorithm to calibrate the detection spectrum. Experiment result shows that the full-spectrum sum method reduced the RSD of parallel samples of Cd and Cr to 9.4％ and 11.06％ from 28.32％ and 31.93％ respectively, yielded better overall calibration than the singleelement internal standardization approach, thereby suggesting that the former method is convenient and effective for online calibration of LIBS for detection of aqueous heavy metals.     

Quantitative analysis modeling for the ChemCam spectral data based on laser-induced breakdown spectroscopy using convolutional neural network

Laser-induced breakdown spectroscopy (LIBS) has been applied to many fields for the quantitative analysis of diverse materials. Improving the prediction accuracy of LIBS regression models is still of great significance for the Mars exploration in the near future. In this study, we explored the quantitative analysis of LIBS for the one-dimensional ChemCam (an instrument containing a LIBS spectrometer and a Remote Micro-Imager) spectral data whose spectra are produced by the ChemCam team using LIBS under the Mars-like atmospheric conditions. We constructed a convolutional neural network (CNN) regression model with unified parameters for all oxides, which is efficient and concise. CNN that has the excellent capability of feature extraction can effectively overcome the chemical matrix effects that impede the prediction accuracy of regression models. Firstly, we explored the effects of four activation functions on the performance of the CNN model. The results show that the CNN model with the hyperbolic tangent (tanh) function outperforms the CNN models with the other activation functions (the rectified linear unit function, the linear function and the Sigmoid function). Secondly, we compared the performance among the CNN models using different optimization methods. The CNN model with the stochastic gradient descent optimization and the initial learning rate=0.0005 achieves satisfactory performance compared to the other CNN models. Finally, we compared the performance of the CNN model, the model based on support vector regression (SVR) and the model based on partial least square regression (PLSR). The results exhibit the CNN model is superior to the SVR model and the PLSR model for all oxides. Based on the above analysis, we conclude the CNN regression model can effectively improve the prediction accuracy of LIBS.