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1.
PAN Congyuan 《等离子体科学和技术》2015,17(8):682-686
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. 相似文献
2.
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. 相似文献
3.
WANG Shaolong 《等离子体科学和技术》2015,17(8):716-720
In order to maintain the pipeline better and remove the dirt more effectively,it was necessary to analyze the contents of elements in dirt.Mg in soil outside of the pipe and the dirt inside of the pipe was quantitatively analyzed and compared by using the laser-induced breakdown spectroscopy(LIBS).Firstly,Mg was quantitatively analyzed on the basis of Mg Ⅰ 285.213 nm by calibration curve for integrated intensity and peak intensity of the spectrum before and after subtracting noise,respectively.Then calibration curves on the basis of Mg Ⅱ 279.553 nm and MgⅡ 280.270 nm were analyzed.The results indicated that it is better to use integrated intensity after subtracting noise of the spectrum line with high relative intensity to make the calibration curve. 相似文献
4.
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. 相似文献
5.
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. 相似文献
6.
The quantitative determination of heavy metals in aquatic products is of great importance for food security issues. Laser-induced breakdown spectroscopy (LIBS) has been used in a variety of foodstuff analysis, but is still limited by its low sensitivity when targeting trace heavy metals. In this work, we compare three sample enrichment methods, namely drying, carbonization, and ashing, for increasing detection sensitivity by LIBS analysis for Pb and Cr in oyster samples. The results demonstrate that carbonization can remove a significant amount of the contributions of organic elements C, H, N and O; meanwhile, the signals of the metallic elements such as Cu, Pb, Sr, Ca, Cr and Mg are enhanced by 3–6 times after carbonization, and further enhanced by 5–9 times after ashing. Such enhancement is not only due to the more concentrated metallic elements in the sample compared to the dried ones, but also the unifying of the matter in carbonized and ashed samples from which higher plasma temperature and electron density are observed. This condition favors the detection of trace elements. According to the calibration curves with univariate and multivariate analysis, the ashing method is considered to be the best choice. The limits of detection of the ashing method are 0.52 mg kg−1 for Pb and 0.08 mg kg−1 for Cr, which can detect the presence of heavy metals in the oysters exceeding the maximum limits of Pb and Cr required by the Chinese national standard. This method provides a promising application for the heavy metal contamination monitoring in the aquatic product industry. 相似文献
7.
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. 相似文献
8.
YANG Chun 《等离子体科学和技术》2015,17(8):671-675
In this paper,two types of comparison analyses,bulk analysis and defect analysis,were carried out for marine steel.The results of laser-induced breakdown spectroscopy(LIBS)were compared with those of spark optical emission spectrometry(Spark-OES) and scanning electron microscopy/energy dispersion spectroscopy(SEM/EDS) in the bulk and defect analyses.The comparison of the bulk analyses shows that the chemical contents of C,Si,Mn,P,S and Cr obtained from LIBS agree well with those determined using Spark-OES.The LIBS is slightly less precise than Spark-OES.Defects were characterized in the two-dimensional distribution analysis mode for Al,Mg,Ca,Si and other elements.Both the LIBS and SEM/EDS results show the enrichment of Al,Mg,Ca and Si at the defect position and the two methods agree well with each other.SEM/EDS cannot provide information about the difference in the chemical constituents when the differences between the defect position and the normal position are not significant.However,LIBS can provide this information,meaning that the sensitivity of LIBS is higher than that of SEM/EDS.LIBS can be used to rapidly characterize marine steel defects and provide guidance for improving metallurgical processes. 相似文献
9.
The influence of the target temperature on the molecular emission of femtosecond laser-induced breakdown spectroscopy (LIBS) was investigated experimentally.An Al target was ablated to produce laser-induced plasma.The Al target was uniformly heated to a maximum of 250 ℃.The measured molecular emission was AlO (△υ =0) from the femtosecond LIBS of the Al target.The measurements indicated that the molecular emission of AIO increased as the temperature of the A1 target increased.In addition,a two-temperature model was used to simulate the evolution of the electron and lattice temperature of the Al target with different initial temperatures.The simulated results showed that the electron and lattice temperatures of Al irradiated by the femtosecond laser increased as the initial temperature of the A1 target increased;also,the simulated ablated depth increased.Therefore,an increase in the initial A1 target temperature resulted in an enhancement in the spectral signal of AlO from the femtosecond LIBS of Al,which was directly related to the increase in the size of the ablated crater.The study suggested that increasing the temperature of the target improves the intensity of molecular emission in femtosecond LIBS. 相似文献
10.
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. 相似文献
11.
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. 相似文献
12.
Severe matrix effects and high signal uncertainty are two key bottlenecks for the quantitative performance and wide applications of laser-induced breakdown spectroscopy (LIBS). Based on the understanding that the superposition of both matrix effects and signal uncertainty directly affects plasma parameters and further influences spectral intensity and LIBS quantification performance, a data selection method based on plasma temperature matching (DSPTM) was proposed to reduce both matrix effects and signal uncertainty. By selecting spectra with smaller plasma temperature differences for all samples, the proposed method was able to build up the quantification model to rely more on spectra with smaller matrix effects and signal uncertainty, therefore improving final quantification performance. When applied to quantitative analysis of the zinc content in brass alloys, it was found that both accuracy and precision were improved using either a univariate model or multiple linear regression (MLR). More specifically, for the univariate model, the root-mean-square error of prediction (RMSEP), the determination coefficients (R 2) and relative standard derivation (RSD) were improved from 3.30%, 0.864 and 18.8% to 1.06%, 0.986 and 13.5%, respectively; while for MLR, RMSEP, R 2 and RSD were improved from 3.22%, 0.871 and 26.2% to 1.07%, 0.986 and 17.4%, respectively. These results prove that DSPTM can be used as an effective method to reduce matrix effects and improve repeatability by selecting reliable data. 相似文献
13.
HE Li’ao 《等离子体科学和技术》2016,18(6):647-653
Supervised learning methods(eg.PLS-DA,SVM,etc.) have been widely used with laser-induced breakdown spectroscopy(LIBS) to classify materials;however,it may induce a low correct classification rate if a test sample type is not included in the training dataset.Unsupervised cluster analysis methods(hierarchical clustering analysis,K-means clustering analysis,and iterative self-organizing data analysis technique) are investigated in plastics classification based on the line intensities of LIBS emission in this paper.The results of hierarchical clustering analysis using four different similarity measuring methods(single linkage,complete linkage,unweighted pair-group average,and weighted pair-group average) are compared.In K-means clustering analysis,four kinds of choosing initial centers methods are applied in our case and their results are compared.The classification results of hierarchical clustering analysis,K-means clustering analysis,and ISODATA are analyzed.The experiment results demonstrated cluster analysis methods can be applied to plastics discrimination with LIBS. 相似文献
14.
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. 相似文献
15.
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. 相似文献
16.
Wei QI 《等离子体科学和技术》2021,23(4):45501-045501
The influence of the target temperature on the molecular emission of femtosecond laser-induced breakdown spectroscopy(LIBS) was investigated experimentally. An Al target was ablated to produce laser-induced plasma. The Al target was uniformly heated to a maximum of 250℃. The measured molecular emission was AlO(△ν=0) from the femtosecond LIBS of the Al target.The measurements indicated that the molecular emission of AlO increased as the temperature of the Al target increased. In addition, a two-temperature model was used to simulate the evolution of the electron and lattice temperature of the Al target with different initial temperatures. The simulated results showed that the electron and lattice temperatures of Al irradiated by the femtosecond laser increased as the initial temperature of the Al target increased; also, the simulated ablated depth increased. Therefore, an increase in the initial Al target temperature resulted in an enhancement in the spectral signal of AlO from the femtosecond LIBS of Al,which was directly related to the increase in the size of the ablated crater. The study suggested that increasing the temperature of the target improves the intensity of molecular emission in femtosecond LIBS. 相似文献
17.
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 R2c=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 R2v=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 R2v=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 R2v=0.9968 and RMSEV=0.0785. CNN analysis of time-resolved LIBS is capable of improving the determination of K in soil. 相似文献
18.
《等离子体科学和技术》2024,26(2)
Rapid online analysis of liquid slag is essential for optimizing the quality and energy efficiency of steel production.To investigate the key factors that affect the online measurement of refined slag using laser-induced breakdown spectroscopy(LIBS),this study examined the effects of slag composition and temperature on the intensity and stability of the LIBS spectra.The experimental temperature was controlled at three levels:1350 ℃,1400 ℃,and 1450 ℃.The results showed that slag composition and temperature significantly affected the intensity and stability of the LIBS spectra.Increasing the Fe content and temperature in the slag reduces its viscosity,resulting in an enhanced intensity and stability of the LIBS spectra.Additionally,42 refined slag samples were quantitatively analyzed for Fe,Si,Ca,Mg,Al,and Mn at 1350 ℃,1400 ℃,and 1450 ℃.The normalized full spectrum combined with partial least squares(PLS)quantification modeling was used,using the Ca Ⅱ 317.91 nm spectral line as an internal standard.The results show that using the internal standard normalization method can significantly reduce the influence of spectral fluctuations.Meanwhile,a temperature of 1450 ℃ has been found to yield superior results compared to both 1350 ℃ and 1400 ℃,and it is advantageous to conduct a quantitative analysis of the slag when it is in a\"water-like\"state with low viscosity. 相似文献
19.
Zhihui TIAN 《等离子体科学和技术》2022,24(8):84007
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. 相似文献
20.
Mingjing ZHAO 《等离子体科学和技术》2020,22(7):74003-074003
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. 相似文献