Mechanisms and efficient elimination approaches of self-absorption in LIBS

Laser-induced breakdown spectroscopy(LIBS) is a promising analytical spectroscopy technology based on spectroscopic analysis of the radiation emitted by laser-produced plasma.However, for quantitative analysis by LIBS, the so-called self-absorption effects on the spectral lines, which affect plasma characteristics, emission line shapes, calibration curves, etc, can no longer be neglected. Hence, understanding and determining the self-absorption effects are of utmost importance to LIBS research. The purpose of this review is to provide a global overview of self-absorption in LIBS on the issues of experimental observations and adverse effects,physical mechanisms, correction or elimination approaches, and utilizations in the past century.We believe that better understanding and effective solving the self-absorption effect will further enhance the development and maturity of LIBS.     

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.     

Self-absorption effects of laser-induced breakdown spectroscopy under different gases and gas pressures

The self-absorption effect is one of the main factors affecting the quantitative analysis accuracy of laser-induced breakdown spectroscopy. In this paper, the self-absorption effects of laserinduced 7050 Al alloy plasma under different pressures in air, Ar, and N₂ have been studied.Compared with air and N₂, Ar significantly enhances the spectral signal. Furthermore, the spectral self-absorption coefficient is calculated to quantify the degree of self-absorption, and the inf...     

Analysis of Pulverized Coal by Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy （LIBS） has been used to detect atomic species in various enviromnents. The quantitative analysis （C, H, O, N and S） of representative coal samples are being carried out with LIBS, and the effects of particle size are analyzed. A powerful pulse Nd：YAG laser is focused on the coal sample at atmosphere pressure, and the emission spectra from laser-induced plasmas are measured by time-resolved spectroscopy, and the intensity of analyzed spectral lines is obtained through observing the laser plasma with a delay time of 0.4 #s. The experimental results show that the slope of calibration curve is nearly 1 when the concentration of the analyzed element is relatively low, and the slope of curve is nearly 0.5 when the concentration of C is higher than other elements. In addition, using the calibration-free model without self-absorption effect, the results show that the decreasing of particle size leads to an increase of the plasma temperature.     

Development of miniaturized SAF-LIBS with high repetition rate acousto-optic gating for quantitative analysis

The self-absorption effect in laser-induced breakdown spectroscopy(LIBS) reduces the accuracy of quantitative measurement results.The self-absorption-free LIBS(SAF-LIBS) has been proved to directly capture the optically thin plasma spectra by setting an appropriate exposure time.In this work,a novel SAF-LIBS technique with high repetition rate acousto-optic gating is developed,in which an acousto-optic modulator is used as the shutter to diffract the optically thin fluorescence,and a high repeti...     

A Study of the Laser-Induced Breakdown Spectroscopy of Carbon in the Ultraviolet Wavelength Range Under Vacuum Conditions

The influence of a vacuum on the laser-induced breakdown spectroscopy(LIBS) of carbon in the ultraviolet wavelength range is studied.Experiments are performed with graphite using a LIBS system,which consists of a 1064 nm Nd:YAG laser,a vacuum pump,a spectrometer and a vacuum chamber.The vacuum varies from 10 Pa to 1 atm.Atomic lines as well as singly and doubly charged ions are confirmed under the vacuums.A temporal evolution analysis of intensity is performed for the atomic lines of C Ⅰ 193.09 nm and C Ⅰ 247.86 nm under different vacuum conditions.Both time-integrated and time-resolved intensity evolutions under vacuums are achieved.The lifetimes of the two atomic lines have similar trends,which supports the point of view of a 'soft spot'.Variations of plasma temperature and electron density under different vacuums are measured.This study is helpful for research on carbon detection using LIBS under vacuum conditions.     

Relative spectral response calibration using Ti plasma lines

This work introduces the branching ratio(BR) method for determining relative spectral responses,which are needed routinely in laser induced breakdown spectroscopy(LIBS). Neutral and singly ionized Ti lines in the 250–498 nm spectral range are investigated by measuring laser-induced micro plasma near a Ti plate and used to calculate the relative spectral response of an entire LIBS detection system. The results are compared with those of the conventional relative spectral response calibration method using a tungsten halogen lamp, and certain lines available for the BR method are selected. The study supports the common manner of using BRs to calibrate the detection system in LIBS setups.     

Simultaneous Determination of Trace Lead and Chromium in Water Using Laser-Induced Breakdown Spectroscopy and Paper Substrate

Toxic metals such as lead and chromium in aqueous solutions have been analyzed simultaneously by laser-induced breakdown spectroscopy(LIBS), in which the ordinary printing paper is used as a liquid absorber which was immerged into Pb(NO3)2and Cr(NO3)3aqueous solution to enrich the heavy metals. This method overcomes the drawbacks of splashing and low sensitivity in ordinary LIBS analysis of water, in which a laser beam is directly focused on a liquid surface. A good signal intensity and reproducibility has been demonstrated. The Pb 405.78 nm and Cr 427.48 nm spectral lines are used as the analytical lines. The variation of line intensity with immersion time was investigated. The calibration curve for quantitative measurement of Pb and Cr in water was established, and the detection limits are 0.033 mg/L and 0.026 mg/L respectively,which is about 2-3 orders of magnitude better than that in the ordinary LIBS analysis of heavy metal in solution.     

Weighted-averaging-based classification of laser-induced breakdown spectroscopy measurements using most informative spectral lines

In this study, efficient spectral line selection and weighted-averaging-based processing schemes are proposed for the classification of laser-induced breakdown spectroscopy(LIBS) measurements. For fast on-line classification, a set of representative spectral lines are selected and processed relying on the information metric, instead of the time consuming full spectrum based analysis. The most informative spectral line sets are investigated by the joint mutual information estimation(MIE)evaluated with the Gaussian kernel density, where dominant intensity peaks associated with the concentrated components are not necessarily most valuable for classification. In order to further distinguish the characteristic patterns of the LIBS measured spectrum, two-dimensional spectral images are synthesized through column-wise concatenation of the peaks along with their neighbors.For fast classification while preserving the effect of distinctive peak patterns, column-wise Gaussian weighted averaging is applied to the synthesized images, yielding a favorable trade-off between classification performance and computational complexity. To explore the applicability of the proposed schemes, two applications of alloy classification and skin cancer detection are investigated with the multi-class and binary support vector machines classifiers, respectively. The MIE measures associated with selected spectral lines in both applications show a strong correlation to the actual classification or detection accuracy, which enables to find out meaningful combinations of spectral lines. In addition, the peak patterns of the selected lines and their Gaussian weighted averaging with neighbors of the selected peaks efficiently distinguish different classes of LIBS measured spectrum.     

Enhancement of optical emission generated from femtosecond double-pulse laser-induced glass plasma at different sample temperatures in air

In double-pulse laser-induced breakdown spectroscopy(DP-LIBS), the collinear femtosecond double-pulse laser configuration is experimentally investigated with different initial sample temperatures using a Ti:sapphire laser. The glass sample is ablated to produce the plasma spectroscopy. During the experiment, the detected spectral lines include two Na(I) lines(589.0 nm and 589.6 nm) and one Ca(I) line at the wavelength of 585.7 nm. The emission lines are measured at room temperature(22 ℃) and three higher initial sample temperatures(T_s?=?100 ℃, 200 ℃, and 250 ℃). The inter-pulse delay time ranges from-250 ps to 250 ps.The inter-pulse delay time and the sample temperature strongly influence the spectral intensity,and the spectral intensity can be significantly enhanced by increasing the sample temperature and selecting the optimized inter-pulse time. For the same inter-pulse time of 0 ps(single-pulse LIBS), the enhancement ratio is approximately 2.5 at T_s?=?200 ℃ compared with that obtained at T_s?=?22 ℃. For the same inter-pulse time of 150 ps, the enhancement ratio can be up to 4 at T_s?=?200 ℃ compared with that obtained at T_s?=?22 ℃. The combined enhancement effects of the different initial sample temperatures and the double-pulse configuration in femtosecond LIBS are much stronger than that of the different initial sample temperatures or the double-pulse configuration only.     

Spectral characteristics of underwater laser-induced breakdown spectroscopy under high-pressure conditions

Laser-induced breakdown spectroscopy(LIBS) has been proven to be an attractive technique for in situ oceanic applications.However,when applying LIBS into deep-sea,the pressure effect caused by different ocean depths is inescapable and could have great influence on the LIBS signals.In this work,spectral characteristics of underwater LIBS were investigated as a function of pressure in the range of 0.1-45 MPa.A high-pressure chamber built in the laboratory was used to simulate the high-pressure deep-sea environment.Optimal laser energy and detection delay were first determined under different pressure conditions and were shown to be independent of the external pressure.The increase in pressure has a significant impact both on the peak intensity and line broadening of the observed spectra.The peak intensity of Na,Li and K lines increases with the increasing pressure until a maximum intensity is reached at 12.5 MPa.Above this value,the peak intensity decreases gradually up to 45 MPa.For Ca line,the maximum intensity was observed at 30 MPa.The line broadening keeps constant at low pressures from 0.1-10 MPa,while it increases linearly at higher pressures,indicating a higher electron density caused by the compression effect of the high external pressure.We also compared the spectral data obtained from the high-pressure chamber and from the field sea trials,and the good consistency between the laboratory data and sea-trial data suggested the key role of pressure effect on underwater LIBS signals for practical deep-sea applications.     

气体采样滤膜中钚的激光诱导击穿光谱定量分析研究

在核设施排放物的监测中,为确保环境安全及实现核材料衡算,需针对通风管气体采样滤膜中钚的含量进行分析。为探索利用激光诱导击穿光谱（LIBS）技术定量分析滤膜中钚元素含量的可行性,开展了某核设施热室内含钚气体采样滤膜的LIBS分析研究。通过分析含钚气体采样滤膜中钚的LIBS特征光谱信号,筛选出钚的有效发射谱线,并优化LIBS测量延迟时间参数,最终得到了浓度范围较低情况下的定量标准曲线,并进行了技术验证分析。研究结果显示,针对滤膜中的钚元素可采用有效特征谱线进行LIBS定量分析,能用于定量分析钚的可分析有效特征谱线为PuⅡ 443.298 nm、PuⅠ 460.721 nm、PuⅡ 466.389 nm。分析结果表明,LIBS分析结果的精度好于3%。     

Time-resolved spectroscopy of femtosecond laser-induced Cu plasma with spark discharge

The combination of spark discharge and laser-induced breakdown spectroscopy (LIBS) is called spark discharge assisted LIBS. It works under laser-plasma triggered spark discharge mode, and shows its ability to enhance spectral emission intensity. This work uses a femtosecond laser as the light source, since femtosecond laser has many advantages in laser-induced plasma compared with nanosecond laser, meanwhile, the study on femtosecond LIBS with spark discharge is rare. Time-resolved spectroscopy of spark discharge assisted femtosecond LIBS was investigated under different discharge voltages and laser energies. The results showed that the spectral intensity was significantly enhanced by using spark discharge compared with LIBS alone. And, the spectral emission intensity using spark discharge assisted LIBS increased with the increase in the laser energy. In addition, at low laser energy, there was an obvious delay on the discharge time compared with high laser energy, and the discharge time with positive voltage was different from that with negative voltage.     

Development of an Automated LIBS Analytical Test System Integrated with Component Control and Spectrum Analysis Capabilities

The present paper proposes an automated Laser-Induced Breakdown Spectroscopy(LIBS) analytical test system,which consists of a LIBS measurement and control platform based on a modular design concept,and a LIBS qualitative spectrum analysis software and is developed in C#.The platform provides flexible interfacing and automated control;it is compatible with different manufacturer component models and is constructed in modularized form for easy expandability.During peak identification,a more robust peak identification method with improved stability in peak identification has been achieved by applying additional smoothing on the slope obtained by calculation before peak identification.For the purpose of element identification,an improved main lines analysis method,which detects all elements on the spectral peak to avoid omission of certain elements without strong spectral lines,is applied to element identification in the tested LIBS samples.This method also increases the identification speed.In this paper,actual applications have been carried out.According to tests,the analytical test system is compatible with components of various models made by different manufacturers.It can automatically control components to get experimental data and conduct filtering,peak identification and qualitative analysis,etc.on spectral data.     

Rapid identification of volatile organic compounds and their isomers in the atmosphere

Isomers are widely present in volatile organic compounds (VOCs), and it is a tremendous challenge to rapidly distinguish the isomers of VOCs in the atmosphere. In this work, laser-induced breakdown spectroscopy (LIBS) technology was developed to online distinguish VOCs and their isomers in the air. First, LIBS was used to directly detect halogenated hydrocarbons (a typical class of VOCs) and the characteristic peaks of the related halogens were observed in the LIBS spectra. Then, comparing the LIBS spectra of various samples, it was found that for VOCs with different molecular formulas, although the spectra are completely the same in elemental composition, there are still significant differences in the relative intensity of the spectral lines and other information. Finally, in light of the shortcomings of traditional LIBS technology in identifying isomers, machine learning algorithms were introduced to develop the LIBS technique to identify the isomers of atmospheric VOCs, and the recognition results were very good. It is proved that LIBS combined with machine learning algorithms is promising for online traceability of VOCs in the atmospheric environment.     

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.     

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.     

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.     

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.     

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.