Laser-induced Breakdown Spectra Research Articles

Analysis of laser induced breakdown spectra for distinguish between healthy and carious teeth

Analysis of laser induced breakdown spectra for distinguish between healthy and carious teeth

In-situ analysis of laser-induced breakdown spectra for online monitoring of femtosecond laser machining of sapphire

In-situ analysis of laser-induced breakdown spectra for online monitoring of femtosecond laser machining of sapphire

Rapid and accurate determination methods based on data fusion of laser-induced breakdown spectra and near-infrared spectra for main elemental contents in compound fertilizers

Compound fertilizer occupies a dominant position in the structure of fertilizer products in China. The contents of nitrogen, phosphorus and potassium are the key indicators affecting the fertilization efficiency and the price of compound fertilizers. Laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIRS) are two rapid analytical techniques suitable for online monitoring of the above components in compound fertilizer. However, accurate LIBS analysis needs to overcome matrix effects and interference from environmental elements, and NIRS also has the limitation of not being able to directly detect inorganic components in compound fertilizers. The combination of LIBS and NIRS techniques, namely LIBS-NIRS data fusion, has the potential to reduce interferences in the detection of single spectroscopic techniques and further improve the analysis accuracy. This study compared the LIBS-NIRS data fusion methods under different optimization conditions, and found that CARS-OPF (competitive adaptive reweighted sampling combined with outer product fusion) and CARS-EWF (competitive adaptive reweighted sampling combined with equal weight fusion) are two effective intermediate data fusion methods which can achieve better quantitative analysis results than single spectroscopic methods. The root mean square errors of prediction (RMSEP) for nitrogen, phosphorus, and potassium contents in compound fertilizers by using CARS-OPF are 0.901, 0.693, and 1.52, respectively, and the RMSEP for those indicators by using CARS-EWF are 0.934, 0.719, and 1.60, respectively. In these two methods, the LIBS and NIRS characteristic variables of compound fertilizers are firstly screened by CARS algorithm, and then intermediate data fusion was carried out by using equal weight fusion or outer product fusion. Redundant variables in the original data can be well removed in the data fusion process to ensure the accuracy of the analysis. Therefore, the combined methods of LIBS-NIRS based on CARS-OPF and CARS-EWF could be well applied to the rapid and accurate detection of main elemental contents in compound fertilizers.

Multi-element Quantitative Analysis of Single Micro-sized Suspended Particles in Air with High Accuracy Based on Random Forest and Variable Selection Strategies.

The chemical compositions of atmospheric particles have been studied for several decades, and the traditional techniques for particle analysis usually require time-consuming sample preparation. Within this study, simultaneous quantitative detection of multiple metallic species (Zn, Cu, and Ni) in single micro-sized suspended particles was investigated by combining random forest (RF) and variable selection strategies. Laser-induced breakdown spectra of 15 polluted black carbon samples were applied for establishing the RF model, and the movmean smoothing spectral pretreatment method and variable selection methods [variable importance measurement (VIM), genetic algorithm (GA), and variable importance projection (VIP)] were proposed. Finally, the optimized RF calibration model with the evaluation indicators of mean relative error (MRE), root-mean-square error (RMSE), and coefficient of determination (R2) was constructed based on the optimal input variables and model parameters. Compared with the univariate regression method, the VIP-RF (Zn) and VIM-RF (Cu and Ni) models showed a better correlation relationship (Rp2 = 0.9662 for Zn, Rp2 = 0.9596 for Cu, and Rp2 = 0.9548 for Ni). For Zn, Cu, and Ni, the values of RMSEP (RMSE of prediction) decreased by 116.44, 68.94, and 102.10 ppm, while the values of MREP (MRE of prediction) decreased by 67, 55, and 48%, respectively. The values of ratio of prediction to deviation (RPD) of VIP-RF (Zn), VIM-RF (Cu), and VIM-RF (Ni) models were 5.4, 5.0, and 4.7, respectively. The performance of this combined approach displays a notable accuracy improvement in the quantitative analysis of single particles, suggesting that it is a promising tool for real-time air particulate matter pollution monitoring and control in the future.

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.

Rapid identification of soil types in agricultural land based on laser induced breakdown spectroscopy (LIBS)

The difference in the nutrient content and harmful heavy metal content of different regions and types of soils can have a certain impact on the crops grown on them. Different crop varieties have their own ecologically suitable areas for planting. Identification of the types of agricultural soil from different regions, can quickly providing references for the selection and cultivation of crops on different types/regions of soil, which is conducive to ensuring the safety of agricultural products. This study is carried out using a laser-induced breakdown spectra (LIBS) device with a frequency-doubled 532 nm excitation wavelength, a laser pulse width of 8 ns, a repetition frequency of 5 Hz, and a pulsed laser energy of 15 mJ. The LIBS data of nine standard agricultural soils collected from different regions. Each sample is collected 60 sets of spectra and a total of 540 sets of data are obtained. At first, the all obtained spectral data are normalized to compensate for the spectral changes in the measurement process. Then the spectral data in the 200–800 nm band are analyzed. Randomly select 30 %, 50 %, and 70 % of the spectral data according to the Kennard-Stone (KS) classification as the training set, and the remaining spectral data as the test set. The Support Vector Machine (SVM) model is used to classify and identify the soil samples of agricultural soil in different areas. The results show that when 30 % of the spectral data are selected as the training set, the accuracy of identifying different agricultural soil types is 88.6 %, and the accuracy of identifying all nine types of agricultural soils is above 76.7 %. The accuracy of soil identification for the two types of agricultural soil is 100 %, and the identification accuracy for the seven types of soil is above 86 %. When 50 % spectral data are selected as training set, the overall recognition accuracy can reach 95.9 %, the recognition accuracy of 8 types of agricultural soil samples is above 90.5 %, and the recognition accuracy of 4 types of soils can reach 100 %, The recognition accuracy of only one type of soil is less than 90 %, which is 86.7 %. When the selected training set contains 70 % of spectral data, the overall recognition accuracy can reach 96.3 %, the recognition accuracy of 8 types of agricultural soil samples is above 90 %, and the accuracy of 1 type of agricultural soil recognition is less than 90 %, which is 89.19 %. The results show that LIBS technology can be applied to the rapid identification of soil types in agricultural soils.

A novel approach for discovering correlations between elemental and molecular composition using laser-based spectroscopic techniques.

Modern analytical techniques, including laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy, yield multidimensional data, which are most efficiently used in conjunction with chemometric techniques, including multi-block algorithms. In this study, we use several algorithms for the processing of laser-induced breakdown and Raman spectra of zooplankton organisms, which are found to accumulate lithium for an unknown reason. Correlations between elemental and molecular composition of zooplankton have been found. We studied 29 samples: crustaceans, arrow worms, and sea snails. The obtained spectra were examined by principal component analysis (PCA), non-negative matrix factorization (NMF), consensus PCA (CPCA), and analysis of common components and specific weights (CCSWA, or ComDim). LIBS spectra are more sensitive towards taxonometric differences than Raman spectra. All the algorithms gave similar results, although still differing in details. Data fusion revealed a number of relationships, including the correlation of Li with potassium (R = 0.83, n = 14), with Raman bands of carotenoids (R = 0.89, n = 11) and tryptophan (R = 0.94, n = 9). The correlations were most pronounced in light-coloured parts of the inhomogeneous biological material. Ratios of fatty acids are associated with Li concentration if above 200 mg kg-1. Valine is also related to the Li accumulation. Thus, it is shown that the combination of LIBS and Raman spectroscopy, followed by appropriate mathematical treatment, is a convenient tool for comprehensive studies of environmental objects.

Evaluation of Na and K in anti-diabetic ayurvedic medicine using LIBS.

Diabetes mellitus, known as diabetes, is a challenging issue, and to control diabetes, a large population is lining toward ayurvedic medicine. In the present study, four brands of anti-diabetic ayurvedic medicines, along with a home remedy, are analyzed using the laser-induced breakdown spectroscopic (LIBS) technique. The study is carried out to know the elements responsible for glycemic potential. The laser-induced breakdown (LIB) spectra elucidate the presence of organic and inorganic elements like Al, Ba, C, Ca, Cu, Fe, H, K, Mg, N, Na, O, Si, Sr, Zn, and the molecular band of CN molecule in medicines. LIBS result also reveals Na and K's distinct concentration, which plays a vital role in diabetes management. The presence of the CN band and organic elements indicate the presence of organic molecular compositions in medicines. For confirmation of organic composition in the drugs, Fourier transform infrared spectroscopy (FT-IR) has been performed. Principal component analysis (PCA) on the LIBS data of the medicines has been used for instant discrimination based on their elemental/molecular compositions.

Tackling the FeO orange band puzzle in meteor and airglow spectra through combined astronomical and laboratory studies

ABSTRACT The iron oxide ‘orange arc’ bands are unambiguously detected in persistent meteor trains, meteor wakes, and clouds, as well as in the terrestrial airglow. In contrast to the majority of other astronomically important diatomic molecules, theoretical simulation of the FeO rovibronic spectra is not feasible due to the extremely condensed and strongly perturbed multiplet structure of its excited states. In this work, the time-evolution of the laser-induced breakdown spectra (LIBS) of high-purity iron recorded in air at high temperature and impact conditions is used to mimic the FeO pseudo-continuum emission observed during meteor events and the terrestrial night airglow. The relative intensity distributions in the structural continuum of the LIBS spectra are measured at 530–660 nm and a plasma temperature of 1500–6500 K. The anomalous increase of the intensity observed at 620–640 nm and temperature < 2000 K could be attributed to the emission of higher oxides of iron as explained by the conducted thermodynamic and kinetic modelling of iron burning in the atmosphere.

Effects of Laser Beam Focusing Characteristics on Laser-Induced Breakdown Spectra.

The impact of altering laser focusing conditions on laser-induced breakdown spectroscopy experiments is investigated under ambient Earth laboratory and simulated Martian atmospheres. Experiments were performed in which the focal spot size was varied on a sample by altering the lens to sample distance with respect to targets of interest. Samples investigated include aluminum, copper, and steel. Specific neutral and ionic transitions of each sample were monitored. Atomic and ionic emissions show different intensity peak distributions along the varying lens to sample distance. Ionic species have peak emissions when laser plasma is initiated with a focused spot within the sample in ambient Earth laboratory air, while atomic emissions have peak intensities several millimeters deeper into a sample. In simulated Martian atmospheres, atomic emissions are observed to peak when the laser is focused within the sample, while ionic emissions have peak intensities when the laser is focused near the surface of a sample.

Automatic preprocessing of laser-induced breakdown spectra using partial least squares regression and feed-forward artificial neural network: Applications to Earth and Mars data

Due to its relatively simple and versatile nature, laser-induced breakdown spectroscopy experiments can yield enormous amount of data that normally needs to be preprocessed to remove background signal, electron continuum, and noise, and for some applications, correct for the instrument response function and normalize the signal prior to conducting spectroscopic analysis. In experiments where the focus is on the analysis of samples of similar composition, preprocessing can be repetitive and tedious. We show that preprocessing of such LIBS data can be performed in an automated or semi-automated manner using machine learning tools. To demonstrate this approach, we apply partial least squares regression and artificial neural networks on two laser-induced breakdown spectra datasets. The first dataset is used to develop predictive models for abundances of various elements in geological samples analyzed by a laboratory model of ChemCam. The second dataset consists of spectra obtained from ChemCam as it interrogates Martian targets. We show that using the two machine learning techniques, we can predict the preprocessed spectra of samples with a relatively high accuracy for both datasets.

激光诱导击穿光谱中分析谱线的自适应选择方法

激光诱导击穿光谱中分析谱线的自适应选择方法

Towards a Database for the Relative Spectral Lines' Intensities of Laser-Induced Breakdown spectra

The relative intensity is one of the parameters of the spectral lines that indicate the emission strength of such lines. The spectral lines relative intensity database for most elements has been recorded a long time ago through spectroscopic data obtained by arc, spark, and vacuum tube. The problem is that relative line intensity is source dependent and the current database cannot be used for Laser-Induced Breakdown Spectroscopy (LIBS). Since LIBS is dealing with transient plasma, then a lot of parameters should be studied in order to find suitable conditions and a suitable way to record this database. In this work, we are trying to find a way to construct a database for relative lines intensities that can be used properly in LIBS analysis. The LIBS spectral lines have been recorded for different delay times for different copper alloy samples. Then, some chosen copper’ spectral lines were compared. An empirical formula has been proposed to calculate the spectral line intensity for each delay time. It was found that for delay times between 1000 ns and 2000 ns the spectral lines were related together in a way that the copper concentration doesn’t affect the spectral line relative intensity but it only affects the value of the spectral line intensity.

A quantitative spectral component analysis method based on maximum likelihood.

In spectroscopy, the compositional analysis of the spectrum is important, such as extracting information about the species of spectral objects contributing to spectral data from an emission spectrum of photon energy. A quantitative spectral component analysis method based on Maximum Likelihood Estimation using Expectation Maximization (MLEM) is developed, which could quantitatively decompose out the components of the measured spectrum of low counts and surpass conventional techniques which belong to classification or regression. Abundant experimental and simulated spectra data on gamma-ray spectrum of radionuclides are presented to demonstrate and evaluate this method, while the ingredient radionuclides in the mixed spectrum are identified accurately with high precision. It will be a powerful and alternative method recommended for the circumstances needing fast and quantitative spectral analysis, including radionuclide identification (gamma-ray spectra), biomass or mineral composition (near-infrared spectra), laser-induced breakdown spectra and other spectroscopy scenarios.

Modeling of laser-induced breakdown spectroscopic data analysis by an automatic classifier

Laser-induced breakdown spectroscopy (LIBS) is a multi-elemental and real-time analytical technique with simultaneous detection of all the elements in any type of sample matrix including solid, liquid, gas, and aerosol. LIBS produces vast amount of data which contains information on elemental composition of the material among others. Classification and discrimination of spectra produced during the LIBS process are crucial to analyze the elements for both qualitative and quantitative analysis. This work reports the design and modeling of optimal classifier for LIBS data classification and discrimination using the apparatus of statistical theory of detection. We analyzed the noise sources associated during the LIBS process and created a linear model of an echelle spectrograph system. We validated our model based on assumptions through statistical analysis of “dark signal” and laser-induced breakdown spectra from the database of National Institute of Science and Technology. The results obtained from our model suggested that the quadratic classifier provides optimal performance if the spectroscopy signal and noise can be considered Gaussian.

Clean and dry route of synthesis of C60/polyvinylpyrrolidone composite using supercritical carbon dioxide

In the present investigation, we have studied polymerization of (a) vinylpyrrolidone (VP) and (b) VP with C60 molecule applying various supercritical conditions with 2,2-azobisisobutyronitrile (AIBN) as a catalyst. Products hence obtained are polyvinylpyrrolidone (PVP) and fullerene-polyvinylpyrrolidone (C60/PVP) polymers. With polymerization reaction, the yield of corresponding C60–PVP polymers was significantly affected under applied concentrations of AIBN (304.5–1218.0 × 10−3 mol/dL) supercritical pressure (1200–1800 psi), temperature (C60 90 ± 1°) and time (3–6 h). The concentration of VP ranged 7.04–12.59 × 10−3 mol/dL. The samples of the synthesized C60/PVP polymer with lowest (24.8%) and highest (45.0%) yields were characterized using UV–Vis, FT-IR, 1H NMR, laser-induced breakdown spectra, scanning electron microscopy and simultaneous TG–DTA–DSC. The morphology of the C60/PVP polymers was confirmed by scanning electron micrographs which indicates the existence of separated C60 phases with heterogeneous morphology in corresponding C60/PVP polymers in all the cases. Simultaneous TG–DTA–DSC revealed enhanced thermal stability of C60/PVP.

Ion-chelation based digital barcodes for multiplexing of a suspension array.

Ion-chelated microbeads (ICMs) for suspension arrays can be prepared by chelating metal ions (MIs), which are used as encoding materials. Stimulating the ICMs, laser induced breakdown spectra (LIBs) can be obtained and the atomic spectra of the chelated ions are chosen as the decoding signals. Our ICMs show digital characteristics with high stability due to the properties of LIBs. And, since there are many available coding materials and different kinds of coding materials can be easily combined, the coding capacity can be considerably enlarged. Further, the background interference in fluoroimmunoassay detection could be avoided, because the ICMs contain no fluorescence emission. In our studies, we achieved a total of 15 types of barcodes by taking full advantage of 4 kinds of ions, then a fluoroimmunoassay was performed to demonstrate the specificity and detection performance of our ICMs in multiplexing and the detection limit could reach 1.49 × 10-10 M, showing promising potential in applications.

Simulated laser-induced breakdown spectra of graphite and synthetic shergottite glass under Martian conditions

We report the results of a simulation of the laser-induced breakdown spectra of graphite and synthetic shergottite glass in an atmosphere similar to that of Mars using a 1-D, Lagrangian hydrodynamic model, a spherical geometry description of the laser ablation and plume expansion and a local thermodynamic equilibrium (LTE) approach for the emission spectra. We compare the LIBS spectra of two calibration targets on board of the Curiosity rover to the simulated ones calculated under nominally the same conditions as those encountered on Mars. The simulations provide an additional way, to laboratory based comparative studies, to better understand the effects of laser parameters and atmospheric pressures. We report on the effect of laser irradiance and ambient pressure on the electron and ion temperature, electron number density, and fluid velocity parameters characterizing the plasma. Finally, we show the effects of laser irradiance and ambient pressure on the simulated emission spectra of graphite and shergottite and compare them to those acquired by ChemCam. The agreement between the two is good, particularly for the prominent emission lines.

Time-resolved analysis of pure indium sample and LCD displays

We report spectroscopic study of the laser induced indium plasma produced in air by using a Q-switched pulsed Nd:YAG laser at 1064 nm. A streak camera coupled to a spectrograph is used to record optical plasma emission. Plasma parameters (excitation temperature and electron density) characterizing the laser induced plasma are determined using the time-resolved analysis of the emission spectra. Indium plays an important role in electronic devices and LCD displays. However, indium is one of the rare elements. There are strong needs for recovery and recycling of rare materials from the used products and waste in the world. We present time-resolved laser induced breakdown spectra of atomic (In I) and ionic (In II) indium lines and other elements contained in the LCD displays, recorded using the fast streak camera, in different time ranges (from 500 ns to 20 μs). The study of indium lines showed in this paper could be useful for detecting indium and other elements in electronic waste for the purpose of recycling. Our LIBS measurements are applicable for any multielemental analysis in environmental physics, food quality control and ecology.

Visualization of differences in nanosecond laser ablation behavior for geological standard samples with different matrices

A combination of time-resolved shadowgraphy and laser-induced breakdown spectra (LIBS) was performed to investigate the ablation behavior of United States Geological Survey (USGS) and National Institute of Standards and Technology (NIST) geological standard samples when ablated by focused nanosecond laser pulses in air. The shock wave (SW) radius achieved 1535 μm at 743 ns for the BCR-2G, and 1513 μm at 759 ns for the NIST SRM 612 standard samples when a laser pulse energy of 17 mJ was used. The maximum speed, pressure, temperature, and density of the shock wave were 5435 m/s, more than 600 MPa, 3 × 105 K, and 7.76 kg/m3, respectively, for NIST SRM 612. Three SWs were generated as these standard samples were ablated. Laser-induced SWs are expected to contribute to attain an approximate matrix-matched reference material, which would render direct solid concentration analysis possible.