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.

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

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

Quantitative analysis of 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.

Classification of steel based on laser-induced breakdown spectroscopy combined with restricted Boltzmann machine and support vector machine

In recent years,a laser-induced breakdown spectrometer(LIBS)combined with machine learning has been widely developed for steel classification.However,the much redundant information of LIBS spectra increases the computation complexity for classification.In this work,restricted Boltzmann machines(RBM)and principal component analysis(PCA)were used for dimension reduction of datasets,respectively.Then,a support vector machine(SVM)was adopted to process feature information.Two models(RBM-SVM and PCA-SVM)are compared in terms of performance.After optimization,the accuracy of the RBM-SVM model can achieve 100%,and the maximum dimension reduction time is 33.18 s,which is nearly half of that of the PCA model(53.19 s).These results preliminarily indicate that LIBS combined with RBM-SVM has great potential in the real-time classification of steel.

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.

Noninvasive in vivo study of NADH fluorescence and its real-time intrinsic dynamical changes: Experiments and seven-layered skin model Monte Carlo simulations

Reduced nicotinamide adenine dinucleotide(NADH)plays a crucial role in many biochemical reactions in human metabolism.In this work,a flow-mediated skin fluorescence(FMSF)-postocclusion reactive hyperaemia(PORH)system was developed for noninvasive and in vivo measurement of NADH fluorescence and its real-time dynamical changes in human skin tissue.The real-time dynamical changes of NADH fluorescence were analyzed with the changes of skin blood flow measured by laser speckle contrast imaging(LSCI)experiments simultaneously with FMSFPORH measurements,which suggests that the dynamical changes of NADH fluorescence would be mainly correlated with the intrinsic changes of NADH level in the skin tissue.In addition,Monte Carlo simulations were applied to understand the impact of optical property changes on the dynamical changes of NADH fluorescence during the PORH process,which further supports that the dynamical changes of NADH fluorescence measured in our system would be intrinsic changes of NADH level in the skin tissue.

All laser direct writing process for temperature sensor based on graphene and silver

A highly sensitive temperature sensing array is prepared by all laser direct writing(LDW)method,using laser induced silver(LIS)as electrodes and laser induced graphene(LIG)as temperature sensing layer.A finite element analysis(FEA)photothermal model incorporating a phase transition mechanism is developed to investigate the relationship between laser parameters and LIG properties,providing guidance for laser processing parameters selection with laser power of 1–5 W and laser scanning speed(greater than 50 mm/s).The deviation of simulation and experimental data for widths and thickness of LIG are less than 5%and 9%,respectively.The electrical properties and temperature responsiveness of LIG are also studied.By changing the laser process parameters,the thickness of the LIG ablation grooves can be in the range of 30–120μm and the resistivity of LIG can be regulated within the range of 0.031–67.2Ω・m.The percentage temperature coefficient of resistance(TCR)is calculated as−0.58%/°C.Furthermore,the FEA photothermal model is studied through experiments and simulations data regarding LIS,and the average deviation between experiment and simulation is less than 5%.The LIS sensing samples have a thickness of about 14μm,an electrical resistivity of 0.0001–100Ω・m is insensitive to temperature and pressure stimuli.Moreover,for a LIS-LIG based temperature sensing array,a correction factor is introduced to compensate for the LIG temperature sensing being disturbed by pressure stimuli,the temperature measurement difference is decreased from 11.2 to 2.6°C,indicating good accuracy for temperature measurement.

Identification of Graves’ophthalmology by laser-induced breakdown spectroscopy combined with machine learning method

Diagnosis of the Graves’ophthalmology remains a significant challenge.We identified between Graves’ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy(LIBS)combined with machine learning method.In this work,the paraffin-embedded samples of the Graves’ophthalmology were prepared for LIBS spectra acquisition.The metallic elements(Na,K,Al,Ca),non-metallic element(O)and molecular bands((C-N),(C-O))were selected for diagnosing Graves’ophthalmology.The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis(LDA),support vector machine(SVM),k-nearest neighbor(ANN),and generalized regression neural network(GRNN),respectively.The results showed that the predicted accuracy rates of LDA,SVM,ANN,GRNN were 76.33%,96.28%,96.56%,and 96.33%,respectively.The sensitivity of four models were 75.89%,93.78%,96.78%,and 96.67%,respectively.The specificity of four models were 76.78%,98.78%,96.33%,and 96.00%,respectively.This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves’ophthalmopathy with a higher rate of accuracy.The ANN had the best performance by comparing the three nonlinear models.Therefore,LIBS combined with machine learning method can be an effective way to discriminate Graves’ophthalmology.