Aqueous ruthenium detection by microwave-assisted laser-induced breakdown spectroscopy

Aqueous ruthenium was detected in real-time under ambient conditions using microwave-assisted laser-induced breakdown spectroscopy (MW-LIBS). A 10 mJ laser energy and 750 W microwave power were directed at an open liquid jet sample of ruthenium. It was observed that, for liquid flow, the coupling efficiency between the microwave and the laser-induced plasma was limited to 43%. The improvement in the ruthenium's signal-to-noise ratio with MW-LIBS, with respect to LIBS, was 76-fold. Based on MW-LIBS, the limit of detection for aqueous ruthenium was determined to be 957 ± 84 ppb.     

Portable fiber-optic laser-induced breakdown spectroscopy system for the quantitative analysis of minor elements in steel

In this paper, we developed a portable laser-induced breakdown spectroscopy(LIBS) using an optical fiber to deliver laser energy and used it to quantitatively analyze minor elements in steel.The R~2 factors of calibration curves of elements Mn, Ti, V, and Cr in pig iron were 0.9965,0.9983, 0.9963, and 0.991, respectively, and their root mean square errors of cross-validation were 0.0501, 0.0054, 0.0205, and 0.0245 wt%, respectively. Six test samples were used for the validation of the performance of the calibration curves established by the portable LIBS. The average relative errors of elements Mn, Ti, V, and Cr were 2.5%, 11.7%, 13.0%, and 5.6%,respectively. These results were comparable with most results reported in traditional LIBS in steel or other matrices. However, the portable LIBS is flexible, compact, and robust, providing a promising prospect in industrial application.     

Rapid online analysis of trace elements in steel using a mobile fiber-optic laser-induced breakdown spectroscopy system

A mobile fiber-optic laser-induced breakdown spectrometer(FO-LIBS) prototype was developed to rapidly detect a large quantity of steel material online and quantitatively analyze the trace elements in a large-diameter steel tube.Twenty-four standard samples and a polynomial fitting method were used to establish calibration curve models.The R~2 factors of the calibration curves were all above 0.99,except for Cu,indicating the elements' strong self-absorption effect.Five special steel materials were rapidly detected in the steel mill.The average absolute errors of Mn,Cr,Ni,V,Cu,and Mo in the special steel materials were 0.039,0.440,0.033,0.057,0.003,and0.07 wt%,respectively,and their average relative errors fluctuated from 2.9% to 15.7%.The results demonstrated that the performance of this mobile FO-LIBS prototype can be compared with that of most conventional LIBS systems,but the more robust and flexible characteristics of the FO-LIBS prototype provide a feasible approach for promoting LIBS from the laboratory to the industry.     

Consistency of intensity ratio between spectral lines with similar self-absorption characteristics during ungated laser induced breakdown spectroscopy measurements

This work reports that the intensity ratio of spectral lines having similar self-absorption characteristics during laser induced breakdown spectroscopy(LIBS) analysis can become nearly constant over a wide range of irradiation conditions if the intensities are integrated over a sufficiently long time. It is shown that the plasma temperature and intensity ratio of these spectral lines have temporal similarity. The spectral lines with similar self-absorption properties may be selected to improve the accuracy and consistency of LIBS analysis results under an environment with fluctuating measurement conditions.     

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.     

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.     

Improvement of quantitative analysis of molybdenum element using PLS-based approaches for laser-induced breakdown spectroscopy in various pressure environments

An experimental setup has been designed and realized in order to optimize the characteristics of laser-induced breakdown spectroscopy system working in various pressure environments. An approach combined the normalization methods with the partial least squares(PLS) method are developed for quantitative analysis of molybdenum(Mo) element in the multi-component alloy,which is the first wall material in the Experimental Advanced Superconducting Tokamak. In this study, the different spectral normalization methods(total spectral area normalization,background normalization, and reference line normalization) are investigated for reducing the uncertainty and improving the accuracy of spectral measurement. The results indicates that the approach of PLS based on inter-element interference is significantly better than the conventional PLS methods as well as the univariate linear methods in the various pressure for molybdenum element analysis.     

Microanalysis of a ductile iron by microchip laser-induced breakdown spectroscopy

Laser beams with ns pulse width are generally employed as an excitation source in the process of detecting inclusions and elemental segregation on a workpiece surface by microanalysis of the laser-induced breakdown spectroscopy.In addition,the ablation crater interval of laser sampling on the sample surface is generally 20 μm or more.It is difficult to detect the morphology of inclusions smaller than 50 μm in diameter and the micro-segregation of elements.However,in this work,when the laser ablation crater is 10 μm and the sampling resolution of the laser on the sample surface is 5 μm,the morphology and distribution of spherical inclusions (20-60 μm) in ductile iron can be detected according to the difference of the Fe spectrum on the Fe matrix and the spheroidal inclusions.Moreover,the distribution of micro-segregation of Mg and Ti elements in ductile iron was also studied.     

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.     

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.     

Study of pressure effects on ocean in-situ detection using laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) has attracted extensive attention as a new technique for in-situ marine application. In this work, the influence of deep-sea high pressure environment on LIBS signals was investigated by using a compact LIBS-sea system developed by Ocean University of China for the in-situ chemical analysis of seawater. The results from the field measurements show that the liquid pressure has a significant effect on the LIBS signals. Higher peak intensity and larger line broadening were obtained as the pressure increases. By comparing the variations of the temperature and salinity with the LIBS signals, a weak correlation between them can be observed. Under high pressure conditions, the optimal laser energy was higher than that in air environment. When the laser energy exceeded 17 mJ, the effect of laser energy on the signal intensity weakened. The signal intensity decreases gradually at larger delays. The obtained results verified the feasibility of the LIBS technique for the deep-sea in-situ detection, and we hope this technology can contribute to surveying more deep-sea environments such as the hydrothermal vent regions.     

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.     

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.     

Experimental study on mercury content in flue gas of coal-fired units based on laser-induced breakdown spectroscopy

Accurate measurement of trace heavy metal mercury(Hg) in flue gas of coal-fired units is great significance for ecological and environmental protection.Mixed gas was used to simulate the actual flue gas of a power plant in this study.A laser-induced breakdown spectroscopy(LIBS)system for Hg measurement in mixed gas was built to study the effect of mixed gas pressure,Hg concentration in mixed gas and delay time on Hg measurement.The experimental results show that the appropriate low mixed gas pressure can obtain high Hg signal intensity and signal to noise ratio.The Hg signal intensity and signal to noise ratio increased with the increase of Hg concentration in mixed gas.The Hg signal intensity and signal to noise ratio decreased with the increase in delay time.According to the above results,the optimized measurement conditions can be determined.Different Hg concentrations in mixed gas were quantitatively analyzed by the internal standard method and traditional calibration method respectively.The relative error of prediction of the test sample obtained by the internal standard method was within 11.11%.The relative error of prediction of the traditional calibration method was less than 14.54%.This proved that the internal standard method can improve the accuracy of quantitative analysis of Hg concentration in flue gas using LIBS.     

Hybrid classification of coal and biomass by laser-induced breakdown spectroscopy combined with K-means and SVM

Laser-induced breakdown spectroscopy(LIBS) is a new technology suitable for classification of various materials. This paper proposes a hybrid classification scheme for coal, municipal sludge and biomass by using LIBS combined with K-means and support vector machine(SVM)algorithm. In the study, 10 samples were classified in 3 groups without supervision by K-means clustering, then a further supervised classification of 6 kinds of biomass samples by SVM was carried out. The results show that the comprehensive accuracy of the hybrid classification model is over 98%. In comparison with the single SVM classification model, the hybrid classification model can save 58.92% of operation time while guaranteeing the accuracy. The results demonstrate that the hybrid classification model is able to make an efficient, fast and accurate classification of coal, municipal sludge and biomass, furthermore, it is precise for the detection of various kinds of biomass fuel.     

The classification of plants by laser-induced breakdown spectroscopy based on two chemometric methods

The applications of laser-induced breakdown spectroscopy(LIBS) on classifying complex natural organics are relatively limited and their accuracy still requires improvement.In this work,to study the methods on classification of complex organics,three kinds of fresh leaves were measured by LIBS.100 spectra from 100 samples of each kind of leaves were measured and then they were divided into a training set and a test set in a ratio of 7:3.Two algorithms of chemometric methods including the partial least squares discriminant analysis(PLS-DA) and principal component analysis Mahalanobis distance(PCA-MD) were used to identify these leaves.By using 23 lines from 16 elements or molecules as input data,these two methods can both classify these three kinds of leaves successfully.The classification accuracies of training sets are both up to 100% by PCA-MD and PLS-DA.The classification accuracies of the test set are 93.3% by PCA-MD and 97.8% by PLS-DA.It means that PLS-DA is better than PCA-MD in classifying plant leaves.Because the components in PLS-DA process are more suitable for classification than those in PCA-MD process.We think that this work can provide a reference for plant traceability using LIBS.     

Detection of heavy metals in water samples by laser-induced breakdown spectroscopy combined with annular groove graphite flakes

The use of laser-induced breakdown spectroscopy(LIBS) for the analysis of heavy metals in water samples is investigated. Some factors such as splashing, surface ripples, extinction of emitted intensity, and a shorter plasma lifetime will influence the results if the water sample is directly measured. In order to avoid these disadvantages and the ‘coffee-ring effect', hydrophilic graphite flakes with annular grooves were used for the first time to enrich and concentrate heavy metals in water samples before being analyzed by LIBS. The proposed method and procedure have been evaluated to concentrate and analyze cadmium, chromium, copper, nickel, lead,and zinc in a water sample. The correlation coefficients were all above 0.99. The detection limits of 0.029, 0.087, 0.012, 0.083, 0.125, and 0.049 mgl~(-1) for Cd, Cr, Cu, Ni, Pb, and Zn,respectively, were obtained in samples prepared in a laboratory. With this structure, the heavy metals homogeneously distribute in the annular groove and the relative standard deviations are all below 6%. This method is very convenient and suitable for online in situ analysis of heavy metals.     

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 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.     

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.