Study of deuterium retention on lithiated tungsten exposed to high-flux deuterium plasma using laser-induced breakdown spectroscopy

Tungsten is under consideration for use as a plasma-facing material in the divertor region of ITER. Lithiation can significantly improve plasma performance in long-pulse tokamaks like EAST. The investigation of lithiated tungsten is important for understanding the lithium conditioning effects for EAST, where tungsten will be used as a plasma-facing material. In this paper, a few important issues of lithiated tungsten interacting with high-flux deuterium plasma have been studied, such as the effect of lithiation on deuterium retention, the profile of elemental distribution, and the chemical state of lithiated tungsten. Deuterium retention inside both pure and lithiated tungsten has been investigated for the first time in the linear plasma simulator Magnum-PSI by in-situ laser induced breakdown spectroscopy (LIBS). The results indicate that, after deuterium plasma exposure, deuterium retention could be saturated in the lithiation layer, and the lithium in the lithiated layer is chemically bound with deuterium. Moreover, the lithiation can inhibit the blistering on the tungsten surface. These results can be valuable for the application of LIBS as a diagnostic technique for plasma-facing components of tokamaks.     

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.     

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.     

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.     

Temporal and spatial evolution measurement of laser-induced breakdown spectroscopy on hydrogen retention in tantalum

Fuel retention measurement on plasma-facing components is an active field of study in magnetic confinement nuclear fusion devices.The laser-induced breakdown spectroscopy(LIBS)diagnostic method has been well demonstrated to detect the elemental distribution in PFCs.In this work,an upgraded co-axis LIBS system based on a linear fiber bundle collection system has been developed to measure the hydrogen(H) retention on a tantalum(Ta) sample under a vacuum condition.The spatial resolution measurement of the different positions of the LIBS plasma can be achieved simultaneously with varying delay times.The temporal and spatial evolution results of LIBS plasma emission show that the H plasma observably expands from the delay times of 0-200 ns.The diameter of Ta plasma is about 6 mm which is much less than the size of H plasma after 200 ns.The difference in the temporal and spatial evolution behaviors between H plasma and Ta plasma is due to the great difference in the atomic mass of H and Ta.The depth profile result shows that H retention mainly exists on the surface of the sample.The temporal and spatial evolution behaviors of the electron excited temperature are consistent with that of the Ta emission.The result will further improve the understanding of the evolution of the dynamics of LIBS plasma and optimize the current collection system of in situ LIBS in fusion devices.     

A rapid classification method of aluminum alloy based on laser-induced breakdown spectroscopy and random forest algorithm

As an important non-ferrous metal structural material most used in industry and production,aluminum(Al) alloy shows its great value in the national economy and industrial manufacturing.How to classify Al alloy rapidly and accurately is a significant, popular and meaningful task.Classification methods based on laser-induced breakdown spectroscopy(LIBS) have been reported in recent years. Although LIBS is an advanced detection technology, it is necessary to combine it with some algorithm to reach the goal of rapid and accurate classification. As an important machine learning method, the random forest(RF) algorithm plays a great role in pattern recognition and material classification. This paper introduces a rapid classification method of Al alloy based on LIBS and the RF algorithm. The results show that the best accuracy that can be reached using this method to classify Al alloy samples is 98.59%, the average of which is 98.45%. It also reveals through the relationship laws that the accuracy varies with the number of trees in the RF and the size of the training sample set in the RF. According to the laws, researchers can find out the optimized parameters in the RF algorithm in order to achieve,as expected, a good result. These results prove that LIBS with the RF algorithm can exactly classify Al alloy effectively, precisely and rapidly with high accuracy, which obviously has significant practical value.     

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.     

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.     

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.     

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.     

Laser-induced breakdown spectroscopy for the classification of wood materials using machine learning methods combined with feature selection

In this paper, we explore whether a feature selection method can improve model performance by using some classical machine learning models, artificial neural network, k-nearest neighbor,partial least squares-discrimination analysis, random forest, and support vector machine(SVM),combined with the feature selection methods, distance correlation coefficient(DCC), important weight of linear discriminant analysis(IW-LDA), and Relief-F algorithms, to discriminate eight species of wood(African rosewood, Brazilian bubinga, elm, larch, Myanmar padauk,Pterocarpus erinaceus, poplar, and sycamore) based on the laser-induced breakdown spectroscopy(LIBS) technique. The spectral data are normalized by the maximum of line intensity and principal component analysis is applied to the exploratory data analysis. The feature spectral lines are selected out based on the important weight assessed by DCC, IW-LDA,and Relief-F. All models are built by using the different number of feature lines(sorted by their important weight) as input. The relationship between the number of feature lines and the correct classification rate(CCR) of the model is analyzed. The CCRs of all models are improved by using a suitable feature selection. The highest CCR achieves(98.55...0.39)% when the SVM model is established from 86 feature lines selected by the IW-LDA method. The result demonstrates that a suitable feature selection method can improve model recognition ability and reduce modeling time in the application of wood materials classification using LIBS.     

Preliminary results of in situ laser-induced breakdown spectroscopy for the first wall diagnostics on EAST

Post-mortem methods cannot fulfill the requirement of monitoring the lifetime of the plasma facing components(PFC) and measuring the tritium inventory for the safety evaluation.Laserinduced breakdown spectroscopy(LIBS) is proposed as a promising method for the in situ study of fuel retention and impurity deposition in a tokamak.In this study,an in situ LIBS system was successfully established on EAST to investigate fuel retention and impurity deposition on the first wall without the need of removal tiles between plasma discharges.Spectral lines of D,H and impurities(Mo,Li,Si,...) in laser-induced plasma were observed and identified within the wavelength range of 500-700 nm.Qualitative measurements such as thickness of the deposition layers,element depth profile and fuel retention on the wall are obtained by means of in situ LIBS.The results demonstrated the potential applications of LIBS for in situ characterization of fuel retention and co-deposition on the first wall of EAST.     

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.     

Effect of distances between lens and sample surface on laser-induced breakdown spectroscopy with spatial confinement

Spatial confinement can significantly enhance the spectral intensity of laser-induced plasma in air.It is attributed to the compression of plasma plume by the reflected shockwave.In addition,optical emission spectroscopy of laser-induced plasma can also be affected by the distance between lens and sample surface.In order to obtain the optimized spectral intensity,the distance must be considered.In this work,spatially confined laser-induced silicon plasma by using a Nd:YAG nanosecond laser at different distances between lens and sample surface was investigated.The laser energies were 12 mJ,16 mJ,20 mJ,and 24 mJ.All experiments were carried out in an atmospheric environment.The results indicated that the intensity of Si (I) 390.55 nm line firstly rose and then dropped with the increase of lens-to-sample distance.Moreover,the spectral peak intensity with spatial confinement was higher than that without spatial confinement.The enhancement ratio was approximately 2 when laser energy was 24 mJ.     

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.     

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.     

Comparison of emission signals for different polarizations in femtosecond laser-induced breakdown spectroscopy

In this study, a femtosecond laser was focused to ablate brass target and generate plasma emission in air. The influence of lens to sample distance(LTSD) on spectral emission of brass plasma under linearly and circularly polarized pulses with different pulse energies was investigated. The results indicated that the position with the strongest spectral emission moved toward focusing lens with increasing the energy. At the same laser energy, the line emission under circularly polarized pulse was stronger compared with linearly polarized pulse for different LTSDs. Next, electron temperature and density of the plasma were obtained with Cu(Ⅰ) lines,indicating that the electron temperature and density under circularly polarized pulse were higher compared to that under linearly polarized pulse. Therefore, changing the laser polarization is a simple and effective way to improve the spectral emission intensity of femtosecond laserinduced plasma.     

Comparison of sample temperature effect on femtosecond and nanosecond laser-induced breakdown spectroscopy

In this paper, we investigated the emission spectra of plasmas produced from femtosecond and nanosecond laser ablations at different target temperatures in air. A brass was selected as ablated target of the experiment. The results indicated that spectral emission intensity and plasma temperature showed similar trend for femtosecond and nanosecond lasers, and the two parameters were improved by increasing the sample temperature in both cases. Moreover, the temperature of nanosecond laser-excited plasma was higher compared with that of femtosecond laser-excited plasma, and the increase of the plasma temperature in the case of nanosecond laser was more evident. In addition, there was a significant difference in electron density between femtosecond and nanosecond laser-induced plasmas. The electron density for femtosecond laser decreased with increasing the target temperature, while for nanosecond laser, the electron density was almost unchanged at different sample temperatures.     

Signal processing for real-time identification of similar metals by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS) is regarded as a promising technique for realtime sorting of scrap metals due to its capability of fast multi-elemental and in-air analysis. This work reports a method for signal processing which ensures high accuracy and high speed during similar metal sorting by LIBS. Similar metals such as aluminum alloys or stainless steel are characterized by nearly the same constituent elements with slight variations in elemental concentration depending on metal type. In the proposed method, the original data matrix is substantially reduced for fast processing by selecting new input variables(spectral lines) using the information for the constituent elements of similar metals. Specifically, principal component analysis(PCA) of full-spectra LIBS data was performed and then, based on the loading plots, the input variables of greater significance were selected in the order of higher weights for each constituent element. The results for the classification test with aluminum alloy, copper alloy,stainless steel and cast steel showed that the classification accuracy of the proposed method was nearly the same as that of full-spectra PCA, but the computation time was reduced by a factor of 20 or more. The results demonstrated that incorporating the information for constituent elements can significantly accelerate classification speed without loss of accuracy.     

Development of a fiber-coupled laser-induced breakdown spectroscopy instrument for analysis of underwater debris in a nuclear reactor core

To inspect the post-accident nuclear core reactor of the TEPCO Fukushima Daiichi nuclear power plant (F1-NPP), a transportable fiber-coupled laser-induced breakdown spectroscopy (LIBS) instrument has been developed. The developed LIBS instrument was designed to analyze underwater samples in a high-radiation field by single-pulse breakdown with gas flow or double-pulse breakdown. To check the feasibility of the assembled fiber-coupled LIBS instrument for the analysis of debris material (mixture of the fuel core, fuel cladding, construction material and so on) in the F1-NPP, we investigated the influence of the radiation dose on the optical transmittance of the laser delivery fiber, compared data quality among various LIBS techniques for an underwater sample and studied the feasibility of the fiber-coupled LIBS system in an analysis of the underwater sample of the simulated debris in F1-NPP. In a feasible study conducted by using simulated debris, which was a mixture of CeO₂ (surrogate of UO₂), ZrO₂ and Fe, we selected atomic lines suitable for the analysis of materials, and prepared calibration curves for the component elements. The feasible study has guaranteed that the developed fiber-coupled LIBS system is applicable for analyzing the debris materials in the F1-NPP.