Laser-induced Breakdown Spectroscopy Method Research Articles

New Insights on Modern Age Coins by Calibration-Free Laser-Induced Breakdown Spectroscopy Method and Chemometric Approaches

New Insights on Modern Age Coins by Calibration-Free Laser-Induced Breakdown Spectroscopy Method and Chemometric Approaches

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.

Intangible Heritage and Sustainable Development Strategies

This paper analyses in detail the problems of protecting cultural heritage in rural regions and small towns, in traditional rural landscapes and in the context of the revival of rural settlements. The paper substantiates the necessity and possibility of measures for the preservation of tangible and intangible cultural heritage by measures of both organisational and technical nature. The international experience of safeguarding cultural heritage by organisational measures and digital preservation is considered; the need to correct this experience in accordance with local specifics is substantiated. The existing technical approaches to the digital preservation of cultural heritage are analysed. An original approach is proposed that most fully simulates complex objects such as artistic reliefs with painting – the method of synchronised laser triangulation, combined with the method of laser-induced breakdown spectroscopy.

A laser-induced breakdown spectroscopy method to assess the stochasticity of plasma-flame transition in sprays

An experimental approach is presented to evaluate the impact of plasma composition arising from pulse-to-pulse energy and mixture fluctuations on the non-resonant laser-induced ignition of sprays. This allows for spark events to be conditioned on the successful or failed establishment of a flame kernel, a phase dominated by plasma decomposition and recombination reactions and the on-set of combustion reactions, that is, independent of the subsequent flame growth phase controlled by propagation phenomena only, such as fuel availability and turbulent strain. For that, laser-induced breakdown spectroscopy of the spark-generated plasma is carried out, followed by OH high-speed imaging of the kernel. Exploratory experiments in spatially uniform and polydisperse kerosene droplet distributions in a jet suggest that the hydrogen concentration in the plasma deriving from the fuel dissociated by the spark is closely related to the generated OH radicals levels and, in turn, with the success of establishing a flame kernel. This suggests that the ignition process is heavily controlled by mixture fluctuations at the spark, inherent of spray flows. The instantaneous mixture at the spark is estimated with a stochastic model, with the probability density function of the equivalence ratio exhibiting values higher than twice the mean value, while the highest probability occurs at lean conditions between the gaseous equivalence ratio and the overall equivalence ratio. The findings corroborate insights on the early-phase ignition obtained from direct numerical simulations, and the framework paves the way for the development of smart online engine-health tools to assess relight capability in future aeroengines.

Influence of Standoff Distance and Sunlight on Detection of Pollution Deposits on Silicone Rubber Insulators Adopting Remote LIBS Analysis

Silicone rubber (SR) insulators have been coated with different concentrations of NaCl to study the contamination level adopting laser-induced breakdown spectroscopy (LIBS) analysis. An elemental study of LIBS spectral data has identified the sodium peaks, thereby indicating the presence of salt deposits. A regression coefficient is used for a better understanding of the direct correlation between the salt deposit density (SDD) and the normalized intensity ratio, at various standoff distances. A marginal decrement in the normalized intensity ratio of the sodium peaks has been noticed in the case of readings taken at morning and midday, compared to those taken at night at different SDD values and different energies of the laser pulse. With increase in SDD, the intensity distribution characteristics have a right shift on the intensity scale. The polluted SR specimens are successfully classified by employing artificial neural network technique. Overall, the LIBS method is successful in identifying the variations in the salt deposition on the surface of the insulator, even at far distances of 15 m and at any time of the day.

Measurement of Heavy Metal Concentrations of Marine Sediments in Yalova Section of Marmara Sea By LIBS Method And Evaluation of Pollution By Principal Component Analysis

The wastes formed as a result of increasing settlements, industrial establishments, agricultural activities and shipping activities on the shores of the Marmara Sea cause the pollution of the Sea of Marmara. Although the pollution in seawater is diluted in a short time by waves and water currents, heavy metals and other pollutants that precipitate chemically and physically create permanent pollution in the sediments. Heavy metal concentrations in Yalova and its surroundings were analyzed by LIBS (Laser-Induced Breakdown Spectroscopy) method in order to control the pollution in this region. In the analyzes made, the percent availability of heavy metals in the sediments was evaluated. With Principal Component Analysis (PCA), heavy metal contamination levels caused by domestic and industrial wastes in the sediments were analyzed and the results were interpreted. The high levels of Ca, Na, Mg elements in the region depend on the presence of carbonate rocks in the region. The presence of Fe, Al and some trace elements is due to the prevalence of volcanic and magmatic rocks in the region. The fact that the elements with high economic value such as Al, Ag and Ti are especially high in Core 1-6 is due to the granitic rocks outcropping in the eastern part of the study area where the samples were taken in, and it is an indication of the existence of important mineral mineralizations in the region. This situation shows that heavy metal pollution in the marine environment consists of both anthropogenic and natural sources.

Quantitative prediction accuracies derived from laser-induced breakdown spectra using optimized multivariate submodels

The accuracy of laser-induced breakdown spectroscopy (LIBS) methods for analyzing geological samples is improved when calibration standards and unknown targets are compositionally similar. A recent study suggests that customized submodels can be used to optimize calibration datasets to achieve more accurate predictions [1]. In practice, this is difficult to implement because the errors inherent in the methods used for sorting unknown targets by composition may affect how successfully this matching can occur. Moreover, creation of submodels intrinsically reduces the size of the dataset on which the model is trained, which has been shown to reduce prediction accuracy. This paper uses LIBS spectra of 2990 unique rock powder standards to compare the accuracy of 1) submodels generated for each element over its geochemical range, 2) submodels created using SiO2 content only, 3) submodels created using the ratio of Si(II)/Si(I) emission lines to group spectra by a proxy for approximate plasma temperature, and 4) models created using all data. Results indicate that prediction accuracies are not always improved by creating submodels because subdividing a dataset to optimize calibrations will always result in a smaller database available for each submodel, and the reduced training set size negatively affects accuracy. Customized LIBS standards for specific applications might overcome this problem in cases where the matrix is similar and the expected concentration range is known. But in a majority of geochemical applications, submodel approaches are only useful in improving prediction accuracies when the initial database is itself extensive enough to support large, robust submodel calibration suites.

Detection of copper in solution by laser-induced breakdown spectroscopy based on nanoparticle chip

We report a highly sensitive electrolytic copper (Cu) detection method using nanoparticle enhanced laser-induced breakdown spectroscopy (NELIBS) for metal smelting processes. The sulfate solution Cu with content ranging from 10 to 50 g/L was prepared and measured on the nanoparticle chip. The experimental results showed that the Cu spectral line intensity of NELIBS increased 5.5 to 9.4 times compared with typical LIBS without nanoparticles. The calibration curve was established based on the spectral line intensity at Cu I 327.40 nm according to the different Cu content in the solution. The determination coefficient R2 of the calibration curve was 0.99. The method of laser-induced breakdown spectroscopy based on drying liquid droplets on the nanoparticle chip can be applied to the rapid determination of Cu content in the copper electrolyte.

Classification of steel using laser-induced breakdown spectroscopy combined with deep belief network.

The identification of steels is a crucial step in the process of recycling and reusing steel waste. Laser-induced breakdown spectroscopy (LIBS) coupled with machine learning is a convenient method to classify the types of materials. LIBS can generate characteristic spectra of various samples as input variable for steel classification in real time. However, the performance of classification model is limited to the complex input due to similar chemical composition in samples and nonlinearity problems between spectral intensities and elemental concentrations. In this study, we developed a method of LIBS coupled with deep belief network (DBN), which is suitable to deal with a nonlinear problem, to classify 13 brands of special steels. The performance of the training and validation sets were used as the standard to optimize the structure of DBN. For different input, such as the intensities of full-spectra signals and characteristic spectra lines, the accuracies of the optimized DBN model in the training, validation, and test set are all over 98%. Moreover, compared with the self-organizing maps, linear discriminant analysis (LDA), k-nearest neighbor (KNN) and back-propagation artificial neural networks (BPANN), the result of the test set showed that the optimized DBN model performed second best (98.46%) in all methods using characteristic spectra lines as input. The test accuracy of the DBN model could reach 100% and the maximum accuracy of other methods ranged from 62.31% to 96.16% using full-spectra signals as input. This study demonstrates that DBN can extract representative feature information from high-dimensional input, and that LIBS coupled with DBN has great potential for steel classification.

Determination of blood cholesterol concentration using calibration-based laser plasma spectroscopy and Fourier transform infrared technique

In this paper, the analysis of blood cholesterol is demonstrated using calibration curves derived from laser-induced breakdown spectroscopy (LIBS) and Fourier transform infrared (FTIR) technique. First of all, five standard samples and one target sample are prepared by adding different concentrations of cholesterol (as the analyte) to normal human blood serum (as the matrix). The plasma emission spectrum of the above samples is measured through LIBS process. Subsequently, considering the constituent elements of cholesterol (C27H46O), the relative average intensity of spectral lines having appropriate signal-to-noise ratio related to these elements is calculated. In the next step, four calibration curves of the relative average intensities are drawn in terms of cholesterol concentration of the samples. Finally, the target cholesterol concentration and its standard error of the mean (SEM) are obtained by the use of the calibration curves. In another way, cholesterol concentration of the target is also examined using the FTIR spectrum of the samples. Absorption bands corresponding to vibrational modes of the cholesterol molecule are identified. Then, the transmittance of the identified absorption peaks is plotted versus the blood cholesterol of the standard samples, and the cholesterol concentration of the target is determined by the resulted calibration lines. The results yielded from both LIBS and FTIR technique are in very good agreement with each other and with the certified value of the target cholesterol concentration, which once again indicates that in the molecular analysis, in addition to FTIR technique, LIBS method can also be very efficient.

Discrimination of Radix Astragali according to geographical regions by data fusion of laser induced breakdown spectroscopy (LIBS) and infrared spectroscopy (IR) combined with random forest (RF)

Data fusion of Laser-induced breakdown spectroscopy (LIBS) and infrared spectroscopy (IR) combined with random forest (RF) was proposed to identify Radix Astragali from different geographical regions. Firstly, the LIBS and IR spectra of 19 Radix Astragali samples were collected and analyzed. Then, an unsupervised discriminant model based on principal components analysis (PCA) with single LIBS and IR spectra was applied to identify Radix Astragali. The results indicated that three types of Radix Astragali samples can not be accurately distinguished using PCA. To distinguish Radix Astragali accurately, a supervised discriminant model based on RF was applied to identify Radix Astragali. The optimal RF discriminant models based on single LIBS or IR were obtained by selecting appropriate pretreatment methods. In low-level data fusion, the optimal LIBS and IR spectra were concatenated into a matrix to construct RF discriminant model. In mid-level data fusion, feature variables were extracted by variable importance (VI) measurement on basis of low-level fused data and the optimal variables were used as input variables to construct RF discriminant model. The results indicated that the predictive performance of RF model based on data fusion was better than the single LIBS or IR method. The predictive performance of RF model based on mid-level data fusion was best and the corresponding sensitivity, specificity and accuracy for the test set were 0.9667, 0.9833 and 0.9778. Additionally, the modeling time was 6.9 s. Summarily, RF model on basis of data fusion of LIBS and IR can provide a rapid and accurate discriminant method for the Radix Astragali from different regions.

High-Throughput Recognition of Tumor Cells Using Label-Free Elemental Characteristics Based on Interpretable Deep Learning.

With cancer seriously hampering the increasing life expectancy of people, developing an instant diagnostic method has become an urgent objective. In this work, we developed a label-free laser-induced breakdown spectroscopy (LIBS) method for high-throughput recognition of tumor cells. LIBS spectra were straightly collected from cells dropped on a silicon substrate and built into a deep learning model for simultaneous classification of various cancers. To interpret the result of the deep learning algorithm, gradient-weighted class activation mapping was utilized to a one-dimensional convolution neural network (1D-CNN), and the saliency maps thus obtained amplified the differences between the spectra of cell lines. Overall results showed that the 1D-CNN algorithms achieved a mean sensitivity of 94.00%, a mean specificity of 98.47%, and a mean accuracy of 97.56%. Thus, the proposed method performed satisfactorily and is seen as an interpretable classification process for cancer cell lines.

Removal of heavy metals from aqueous media by sunflower husk: A comparative study of biosorption efficiency by using ICP-OES and LIBS

Presented research aimed to develop an alternative approach for the estimation of biosorption capability of sunflower husk. The acid-pretreated sunflower biomass was characterized by scanning electron microscopy combined with energy dispersive X-Ray spectroscopy (SEM-EDX) analysis and Fourier transform infrared (FTIR) spectroscopy. Biosorption efficiency has been evaluated using inductively coupled plasma optical emission spectroscopy (ICP-OES) and laser-induced breakdown spectroscopy (LIBS). The adsorption capacity of the adsorbent was determined as the function of the pH of the solution, the initial concentration of heavy metal solutions, and contact time. The optimal conditions were achieved after 15 min of contact at pH 6, while the percentage of the removal was from 80.0?98.7 %, depending on the element. The results obtained from the kinetic and isotherm studies show that maximum adsorption of ions was quickly reached and followed the pseudo-second-order kinetic model. Real samples were tested and obtained Ni values by LIBS method were 3100?200 and 1240?100 mg kg-1, while estimated values by the ICP-OES were 2995?20 and 1130?10 mg kg-1, respectively. The obtained results prove that LIBS method can be used as a ?green alternative? for the evaluation of biosorption efficiency.

The use of Laser Induced Breakdown Spectroscopy for the mineral chemistry of chromite, orthopyroxene and plagioclase from Merensky Reef and UG-2 chromitite, Bushveld Complex, South Africa

A new approach using scanning Laser Induced Breakdown Spectroscopy (LIBS) was developed to unravel cryptic variations in the mineral chemistry of chromite, orthopyroxene, and plagioclase in drill core from Merensky Reef and UG-2. The LIBS method provides continuous spatially resolved chemical data, elucidating variations in mineral chemistry and in element partitioning, taking into account textural features of the rock itself, of individual minerals, and of immediate neighbourhoods. Since the method is relatively fast and easy to apply to numerous core sections, it is of special interest when comparing the lateral variability of diagnostic horizons such as Merensky Reef and UG-2. This approach for measuring Mg/Fe, Cr/Al and Ca/Na ratios is based on the strength of LIBS in detecting chemical variations using intensity ratios within a single matrix, which in this application is one single particular type of mineral phase. Supervised classification followed by k-means clustering was applied to identify those spectra that consist of pure chromite, orthopyroxene, and plagioclase. Measurements and data processing strategies were focused on minimizing the standard deviation in the LIBS-based ratios to be able to detect relatively small variations in mineral chemistry. The LIBS-based Cr/Al and Mg/Fe ratios were successfully validated with respect to electron probe microanalysis on multiple polished sections. Distinct trends could indeed be extracted from a continuous LIBS measurement of 6 m drill core through the Merensky Reef. The data correlate with whole-rock geochemistry and hydrous phases such as phlogopite. The LIBS-based approach opens up unique insights into chemical and mineralogical expressions of large- and local-scale processes. This allows unique and detailed interpretations relevant for the genesis of mineralizations and metal distributions, based on a comprehensive dataset.

Rapid Determination and Quantification of Nutritional and Poisonous Metals in Vastly Consumed Ayurvedic Herbal Medicine (Rejuvenator Shilajit) by Humans Using Three Advanced Analytical Techniques.

‏Shilajit is used commonly as Ayurvedic medicine worldwide which is Rasayana herbo-mineral substance and consumed to restore the energetic balance and to prevent diseases like cognitive disorders and Alzheimer. Locally, Shilajit is applied for patients diagnosed with bone fractures. For safety of the patients, the elemental analysis of Shilajit is imperative to evaluate its nutritional quality as well as contamination from heavy metals. The elemental composition of Shilajit was conducted using three advanced analytical techniques (LIBS, ICP, and EDX). For the comparative studies, the two Shilajit kinds mostly sold globally produced in India and Pakistan were collected. Our main focus is to highlight nutritional eminence and contamination of heavy metals to hinge on Shilajit therapeutic potential. In this work, laser-induced breakdown spectroscopy (LIBS) was applied for qualitative and quantitative analysis of the Shilajit. Our LIBS analysis revealed that Shilajit samples composed of several elements like Ca, S, K, Mg, Al, Na, Sr, Fe, P, Si, Mn, Ba, Zn, Ni, B, Cr, Co, Pb, Cu, As, Hg, Se, and Ti. Indian and Pakistani Shilajits were highly enriched with Ca, S, and K nutrients and contained Al, Sr, Mn, Ba, Zn, Ni, B, Cr, Pb, As, and Hg toxins in amounts that exceeded the standard permissible limit. Even though the content of most elements was comparable among both Shilajits, nutrients, and toxins, in general, were accentuated more in Indian Shilajit with the sole detection of Hg and Ti. The elemental quantification was done using self-developed calibration-free laser-induced breakdown spectroscopy (CF-LIBS) method, and LIBS results are in well agreement with the concentrations determined by standard ICP-OES/MS method. To verify our results by LIBS and ICP-OES/MS techniques, EDX spectroscopy was also conducted which confirmed the presence above mentioned elements. This work is highly significant for creating awareness among people suffering due to overdose of this product and save many human lives.

Reducing self-absorption effect by double-pulse combination in laser-induced breakdown spectroscopy

The method of double-pulse laser-induced breakdown spectroscopy is usually employed to enhance the spectral signal intensity. However, in this study, double-pulse laser-induced breakdown spectroscopy is adopted to investigate the effect of the self-absorption reduction of the spectrum. This research explored that the influence of the change of the gas environment generated by the first laser beam on the self-absorption effect of the plasma spectrum by the second laser beam. Especially despite the different combinations of laser energy, for the three elements of Cu, Mn and Ni, the weakest spectral self-absorption effect can be obtained when the double-pulse delays are around 80 μs, 100 μs, and 110 μs, respectively. In addition, this paper also found that when the energy of the first laser beam is unchanged, the spectral self-absorption effect has a strong correlation with the double-pulse delay, and has a weak correlation with the change of the second laser energy.

Coupling Backpropagation Neural Network and AdaBoost Algorithm for Quantitative Analysis of Nickel via Laser-Induced Breakdown Spectroscopy

A wide area of cropland or soil might well be contaminated with heavy metals, contaminating agricultural goods and posing a risk to human health. As a result, it is required to evaluate the concentration of heavy metals in soil. The combination between laser-induced breakdown spectroscopy (LIBS) and multivariate chemometrics methods was employed to determine heavy metal Ni concentration in twelve soil samples. The comparison between univariate calibration curve, traditional backpropagation neural network (BPNN), and hybrid BPNN-AdaBoost was presented. The result revealed that BPNN-AdaBoost outperformed other models with the coefficient determination calibration , coefficient determination prediction , root mean square error calibration (RMSEC), root mean square error prediction (RMSEP) are 0.985, 0.977, 2.04, 3.18, respectively. This study indicates that BPNN-AdaBoost can be adopted as a reliable chemometric technique to enhance the quantitative analysis of heavy metals based on LIBS.

Exploring an Application of Principal Component Analysis to Laser-Induced Breakdown Spectroscopy of Stainless-Steel Standard Samples as a Research Project

Laser-induced breakdown spectroscopy (LIBS) and principal component analysis (PCA) are frequently used for analytical purposes in research and industry, but they seldom are part of the chemistry curriculum or laboratory exercises. This case study paper describes the combined application of LIBS and PCA during a research internship for an undergraduate student. The instructional method applied was based on a one-on-one mentorship, in which case the learner was engaged in a research work. The learning activities included theoretical introductions to the LIBS and PCA methods, numerical simulation, experiments, and data analysis. The study covered three main topics: analysis of LIBS spectra, application of PCA for clustering, and use of PCA for experimental design. The realization of the study was instructive for all parties involved: from the mentorship point of view, it is concluded that the topics can be covered during an internship or developed into a one semester long research-based module of a chemistry program or a final year project. The student, on the other hand, developed profound technical skills in performing experiments and using PCA software for data analysis.

Quantitative Analysis of Manganese in Underwater Steel Samples Using Long-Short Double-Pulse Laser-Induced Breakdown Spectroscopy.

The long-short double-pulse laser-induced breakdown spectroscopy (LS-DP-LIBS) method was applied to qualitative and quantitative analyses of underwater steel samples to improve the detection ability of underwater measurement. The stable plasma intensity and discrete emission lines were detected using LS-DP-LIBS when comparing the measured results of single-pulse LIBS (SP-LIBS) and LS-DP-LIBS. The long pulse produces a cavitation bubble without plasma, and the short pulse induces the plasma of steel samples within the bubble. The detection features of LS-DP-LIBS for underwater steel samples were discussed in different intra-pulse delay time, long-pulse width, and delay time conditions when analyzing the measured spectra, the signal intensity of Fe(I) at 400.524 nm and 402.187 nm, Mn(I) at 404.136 nm, and intensity ratio of Mn(I) 404.136 nm/Fe(I) 402.187 nm. The results indicated that the plasma stability and spectral signal intensity were improved significantly with a long-pulse width of 80 µs in the intra-pulse delay time of 70 µs, which were appropriate for bubble formation and plasma generation. According to the discussion of the delay time effect, the state of generated plasma was almost stable from 650 ns to 850 ns. Manganese (Mn) contents in steel samples were analyzed quantitatively when measuring five steel samples with different Mn contents using LS-DP-LIBS in optimal experimental conditions. A strong linear dependence was observed with R2=0.9842, which demonstrated the feasibility and appropriateness of quantitative analysis for underwater measurement using LS-DP-LIBS.

The effect of ablation angle on intensity of laser-induced breakdown spectroscopy under vacuum

The in situ diagnostic of the composition and content of the first wall materials in tokamak devices is an important research interest of the plasma-wall interaction. Laser-induced breakdown spectroscopy (LIBS) has become an attractive tool for in situ diagnostic of the first wall materials. It has the ability to analyze the composition of the first wall remotely and on-line without sample manipulation or pretreatment. In tokamak, using the LIBS method in a fixed window to diagnose wall materials in different positions will inevitably affect the LIBS measurement signal due to different laser ablation and light collection angles. In this paper, we got the determination of the relationship between the intensity of the tungsten spectral line and laser energy in the laboratory as a prerequisite. And the effects of different ablation angles on the temporal evolution of the spectrum of the laser ablated tungsten material were investigated in detail. This provides a reference for in situ LIBS analysis.