Laser-induced Breakdown Spectroscopy Research Articles

Effect of single and double pulse laser-induced breakdown spectroscopy towards steel alloy in different gaseous media

In recent years, laser-induced breakdown spectroscopy (LIBS) technique showed its promising for material analysis, quality control, forensic science, and environmental safety monitoring. Besides, due to its rapid measurement capability and minimal sample-preparation requirements, it is a widely recognized elemental analysis approach used in a variety of study domains. The current work compares spatially resolved single (SP) and double pulse (DP), especially orthogonal DP, setups for LIBS to examine and enhance the limit of detection (LOD) in duplex stainless steels alloy. Using the Boltzmann plot method and the Stark broadening mechanism, the electron temperature and density of local arc plasma are highlighted due to single pulse and orthogonal pre-pulse double pulse configurations. With delay time, the circumstances of arc plasma's local thermodynamic equilibrium (LTE) of emission spectral lines are investigated. The experimental results show that there are significant increases in the spectral intensities of the Fe lines in double pulse LIBS under Ar-gas compared to those under He-gas. Internal standardization was used to create calibration curves for the spectral lines of Cr, Cu, and Mn in the alloy sample for the both studied media. It demonstrated that argon outperforms helium in determining the elemental composition of steel alloys. Furthermore, LOD for SP-LIBS and DP-LIBS in both media were calculated, which showed a higher detection LOD for DP-LIBS in the presence of Ar-gas compared to that in He-gas.

Uranium Single Particle Analysis for Simultaneous Fluorine and Uranium Isotopic Determinations via Laser-Induced Breakdown Spectroscopy/Laser Ablation-Multicollector-Inductively Coupled Plasma-Mass Spectrometry.

Uranyl fluoride (UO2F2) particles (<20 μm) were subjected to first-of-its-kind analysis via simultaneous laser-induced breakdown spectroscopy (LIBS) and laser ablation multi-collector inductively coupled plasma-mass spectrometry (LA-MC-ICP-MS). Briefly, a nanosecond pulsed high-energy laser was focused onto the sample (particle) surface. In a single laser pulse, the UO2F2 particle was excited/ionized within the microplasma volume, and the emission of light was collected via fiber optics such that emission spectroscopy could be employed for the detection of uranium (U) and fluorine (F). The ablated particle was simultaneously transported into the MC-ICP-MS for high precision isotopic (i.e., 234U, 235U, and 238U) analysis. This method, LIBS/LA-MC-ICP-MS was optimized and employed to rapidly measure 80+ UO2F2 particles, which were subjected to different calcination processes, which results in varying degrees of F loss from the individual particles. In measuring the particles, the average F/U ratios for the populations treated at 100 and 500 °C were 2.78 ± 1.28 and 1.01 ± 0.50, respectively, confirming loss of F through the calcination process. The average 235U/238U on the particle populations for the 100 and 500 °C were 0.007262 (22) and 0.007231 (23), which was determined to be <0.2% from the expected value. The 234U/238U ratios on the same particles were 0.000053 (11) and 0.000050 (10) for the 100 and 500 °C, respectively, <10% from the expected value. Notably, each population was analyzed in under 5 min, demonstrating the truly rapid analysis technique presented here.

Accurate method based on data filtering for quantitative multi-element analysis of soils using CF-LIBS

To obtain more stable spectral data for accurate quantitative analysis of multi-element, especially for the large-area in-situ elements detection of soils, we propose a method for a multi-element quantitative analysis of soils using calibration-free laser-induced breakdown spectroscopy (CF-LIBS) based on data filtering. In this study, we analyze a standard soil sample doped with two heavy metal elements, Cu and Cd, with a specific focus on the line of Cu I 324.75 nm for filtering the experimental data of multiple sample sets. Pre- and post- data filtering, the relative standard deviation for Cu decreased from 30% to 10%, The limits of detection (LOD) values for Cu and Cd decreased by 5% and 4%, respectively. Through CF-LIBS, a quantitative analysis was conducted to determine the relative content of elements in soils. Using Cu as a reference, the concentration of Cd was accurately calculated. The results show that post- data filtering, the average relative error of the Cd decreases from 11% to 5%, indicating the effectiveness of data filtering in improving the accuracy of quantitative analysis. Moreover, the content of Si, Fe and other elements can be accurately calculated using this method. To further correct the calculation, the results for Cd was used to provide a more precise calculation. This approach is of great importance for the large-area in-situ heavy metals and trace elements detection in soil, as well as for rapid and accurate quantitative analysis.

Quantitative analysis of Th and U in graphite matrix using femtosecond laser-induced breakdown spectroscopy

Quantitative analysis of Th and U in graphite matrix using femtosecond laser-induced breakdown spectroscopy

Improving the Limit of Detection in Laser-Induced Breakdown Spectroscopy and Measuring the Associated Plasma Parameters

Improving the Limit of Detection in Laser-Induced Breakdown Spectroscopy and Measuring the Associated Plasma Parameters

Enhancement of spectral model transferability in LIBS systems through LIBS-LIPAS fusion technique

BackgroundLaser-induced breakdown spectroscopy (LIBS) is extensively utilized a range of scientific and industrial detection applications owing to its capability for rapid, in-situ detection. However, conventional LIBS models are often tailored to specific LIBS systems, hindering their transferability between LIBS subsystems. Transfer algorithms can adapt spectral models to subsystems, but require access to the datasets of each subsystem beforehand, followed by making individual adjustments for the dataset of each subsystem. It is clear that a method to enhance the inherent transferability of spectral original models is urgently needed. ResultsWe proposed an innovative fusion methodology, named laser-induced breakdown spectroscopy fusion laser-induced plasma acoustic spectroscopy (LIBS-LIPAS), to enhance the transferability of support vector machine (SVM) original models across LIBS systems with varying laser beams. The methodology was demonstrated using nickel-based high-temperature alloy samples. Here, the area-full width at half maximum (AFCEI) Composite Evaluation Index was proposed for extracting critical features from LIBS. Further enhancing the transferability of the model, the laser-induced plasma acoustic signal was transformed from the time domain to the frequency domain. Subsequently, the feature-level fusion method was employed to improve the classification accuracy of the transferred LIBS system to 97.8 %. A decision-level fusion approach (amalgamating LIBS, LIPAS, and feature-level fusion models) achieved an exemplary accuracy of 99 %. Finally, the adaptability of the method was demonstrated using titanium alloy samples. Significance and noveltyIn this work, based on plasma radiation models, we simultaneously captured LIBS and LIPAS, and proposed the fusion of these two distinct yet origin-consistent signals, significantly enhancing the transferability of the LIBS original model. The methodology proposed holds significant potential to advance LIBS technology and broaden its applicability in analytical chemistry research and industrial applications.

Imaging the elemental distribution within human malignant melanomas using Laser-Induced Breakdown Spectroscopy

The diagnosis of malignant melanoma, often an inconspicuous but highly aggressive tumor, is most commonly done by histological examination, while additional diagnostic methods on the level of elements and molecules are constantly being developed. Several studies confirmed differences in the chemical composition of healthy and tumor tissue. Our study presents the potential of the LIBS (Laser-Induced-Breakdown Spectroscopy) technique as a diagnostic tool in malignant melanoma (MM) based on the quantitative changes in elemental composition in cancerous tissue. Our patient group included 17 samples of various types of malignant melanoma and one sample of healthy skin tissue as a control. To achieve a clear perception of results, we have selected two biogenic elements (calcium and magnesium), which showed a dissimilar distribution in cancerous tissue from its healthy surroundings. Moreover, we observed indications of different concentrations of these elements in different subtypes of malignant melanoma, a hypothesis that requires confirmation in a more extensive sample set. The information provided by the LIBS Imaging method could potentially be helpful not only in the diagnostics of tumor tissue but also be beneficial in broadening the knowledge about the tumor itself.

Spectrochemical Analysis of Stainless Steel Using 355 nm and 1064 nm Nd:YAG Laser-induced Breakdown Spectroscopy

Spectrochemical Analysis of Stainless Steel Using 355 nm and 1064 nm Nd:YAG Laser-induced Breakdown Spectroscopy

Enhancement of signal intensity of elements in human blood serum using a metal mesh-enhanced CO2 laser-induced breakdown spectroscopy in ambient He gas

Accurate and reliable identification and analysis of the elemental composition of human blood serum is of utmost importance. The present investigation included the analysis of human blood serums for elemental composition using a metal mesh-supported CO2 laser-induced breakdown spectroscopy under a helium gas environment at atmospheric pressure. In this study, a pulsed transversely excited atmospheric CO2 laser with a wavelength of 10.64 µm and a pulse duration of 200 ns was applied to irradiate the human blood serum film placed on a copper metal to induce a luminous plasma. A significant increment of elemental signal intensity occurs by introducing a metal mesh covering the serum film. Qualitative and quantitative analyses have been conducted on the human blood serum's major, minor, and trace elements. The concentrations of elements in the human blood are estimated to be 9160 parts per million (ppm) for carbon (C), 174 ppm for calcium (Ca), 600 ppm for potassium (K), 1410 ppm for magnesium (Mg), 343 ppm for phosphorus (P), 2180 ppm for sodium (Na), and 48 ppm for iron (Fe). The results of the CO2 LIBS method demonstrate a high level of concurrence with the results obtained by the conventional XRF method.

ANN based LIBS models for quasi-experimental spectra relevant for materials for next-step fusion reactors

Following the successful demonstration of machine learning (ML) models for laser induced breakdown spectroscopy (LIBS) adaptation in fusion reactor fuel retention monitoring using synthetic data [Gąsior et al., Spectrochim. Acta, Part B 199, 106576 (2023)], this study focuses on implementing operability on experimental data. To achieve this, Simulated Eperimental Spectra (SES) data are generated and used for validation of a chemical composition estimation model trained on dimensionally reduced synthetic spectral data (DRSSD). Principal component analysis is employed for dimensionality reduction of both SES and DRSSD. To simulate real experimental conditions, the synthetic data, generated by a dedicated tool [M. Kastek (2022), “SimulatedLIBS,” Zenodo. http://dx.doi.org/10.5281/zenodo.7369805] is processed through the transmission function of a real spectroscopy setup at IPPLM. Separate and optimized artificial neural network models are implemented for conversion and chemical composition estimation. The conversion model takes DR-SES as features and DR-SSD as targets. Validation using converted SES data demonstrates chemical composition predictions comparable to those from synthetic data, with the highest relative uncertainty increase below 40% and a normalized root-mean-square error of prediction below 7%. This work represents a significant step toward adapting ML-based LIBS for fuel and impurity retention monitoring in the walls of next-generation fusion devices.

Rapid Identification and Classification of Metal Waste by Laser-Induced Breakdown Spectroscopy

Rapid Identification and Classification of Metal Waste by Laser-Induced Breakdown Spectroscopy

Direct and Sensitive Lead Analysis of Wheat Flour Using Laser-Induced Fluorescence Combined with Laser-Induced Breakdown Spectroscopy Under Low-Energy Ultraviolet Laser Ablation

Direct and Sensitive Lead Analysis of Wheat Flour Using Laser-Induced Fluorescence Combined with Laser-Induced Breakdown Spectroscopy Under Low-Energy Ultraviolet Laser Ablation

Decision-level data fusion based on laser-induced breakdown and Raman spectroscopy: A study of bimodal spectroscopy for diagnosis of lung cancer at different stages

Lung cancer staging is crucial for personalized treatment and improved prognosis. We propose a novel bimodal diagnostic approach that integrates LIBS and Raman technologies into a single platform, enabling comprehensive tissue elemental and molecular analysis. This strategy identifies critical staging elements and molecular marker signatures of lung tumors. LIBS detects concentration patterns of elemental lines including Mg (I), Mg (II), Ca (I), Ca (II), Fe (I), and Cu (II). Concurrently, Raman spectroscopy identifies changes in molecular content, such as phenylalanine (1033 cm−1), tyrosine (1174 cm−1), tryptophan (1207 cm−1), amide III (1267 cm−1), and proteins (1126 cm−1 and 1447 cm−1), among others. The bimodal information is fused using a decision-level Bayesian fusion model, significantly enhancing the performance of the convolutional neural network architecture in classification algorithms, with an accuracy of 99.17 %, sensitivity of 99.17 %, and specificity of 99.88 %. This study provides a powerful new tool for the accurate staging and diagnosis of lung tumors.

Elemental quantitation and evaluation of hydrocarbon in shale using fiber-optic laser induced breakdown spectroscopy

To meet the demands of in situ analysis, a fiber-optic laser-induced breakdown spectroscopy (FO-LIBS) system was established to predict the elemental concentrations and pyrolytic parameters. The key parameters, including pulse energy, gate delay, type of surrounding gas, and flow rate of gas, were optimized to improve the signal-to-noise ratio of the spectral lines, and the evolution characteristics of the plasma morphology in different gas atmospheres were investigated. Under the optimized experimental conditions, C, H, Si, Ca, Fe, and Mg were calibrated using pellet samples based on partial least squares regression, and the average relative prediction error of the natural samples was lower than 10%. In addition, the support vector regression method was utilized to predict pyrolytic parameters such as TOC, Pg, and Tmax with the R2 of all calibration curves being higher than 0.97. The abundance and generation potential of hydrocarbons were evaluated using the predicted TOC and Pg, and Tmax could contribute to the thermal maturity of the kerogen.

Spatial confinement offered by a blocker on the laser-induced breakdown spectroscopy of Ti plasma

Spatial confinement effects offered by a blocker on the laser-induced plasma parameters of titanium (Ti) are evaluated using the Optical Emission Spectroscopy technique. Nd:YAG (1064 nm, 10 ns) laser is used as an irradiation source. To observe the spatial confinement effects, an Al blocker at different distances of 4, 6, and 8 mm from the target is placed along the plume path. All the measurements are performed under the Ar environment at different pressures. It is observed that with increasing laser irradiance plasma parameters such as excitation temperature (Te) and electron number density (ne) increase, whereas it is vice versa true for increasing blocker distances. Without the blocker, the maximum values of Te and ne are about 7000 K and 1.4 × 1018 cm−3, respectively, at an Ar pressure of 50 Torr. A significant increase in emission intensity along with Te ≈ 9810 K and ne ≈ 2.2 × 1018 cm−3 is achieved in the presence of blocker. The results show that spatial confinement is responsible for the enhancement of Te and ne, which is attributed to the increased collisional frequency of plasma species after compression by shockwaves. The ablation pressure and shock pressure are also analytically evaluated and vary from 0.15 to 0.25 GPa and from 0.1 to 0.2 GPa, respectively, with increasing laser irradiance. With increasing blocker distances from 4 to 8 mm, the work done by reflected shockwaves to compress the plume varies from 0.02 to 0.002 mJ.

Optimizing Cu-alloy surface characteristics through magnetic field-enhanced fs laser treatment

We report here the effect of the magnetic field on plasma parameters and the surface structuring of the Cu alloy after fs laser irradiation. A Ti:Sapphire (800 nm, 35 fs, 1 KHz) laser is employed at various irradiances (0.011–0.117 PW/cm2) to generate plasma. A Transvers Magnetic Field (TMF) of strength 1.1 T is employed for plasma confinement. All the measurements were performed with and without TMF. The Cu plasma parameters, i.e., excitation temperature (Texc) and electron number sensity (ne), determined by laser-induced breakdown spectroscopy analysis, are higher in the presence of TMF. This magnetic field confinement of Cu plasma was studied analytically by evaluating thermal beta (βt), directional beta (βd), confinement radius (Rb), and diffusion time (td). To correlate Cu-alloy plasma parameters with surface modifications, field emission scanning electron microscope analysis is performed. It reveals the formation of low-spatial-frequency laser-induced periodic surface structures (LIPSSs) and high-spatial-frequency LIPSSs, along with agglomers and nano-rims formation. Distinct and well-defined structures are observed in the presence of a magnetic field. It is concluded that controlled surface structuring can be achieved through magnetic confinement, which enhances key plasma parameters. The technique has the potential for enhancing the fabrication of nano-gratings and field emitters, where spatial uniformity is critically important.

Systematic comparison of unsupervised feature extraction methods for enhancing discrimination accuracy of brown rice flour adulteration in LIBS

Laser-induced breakdown spectroscopy (LIBS) is widely applied in various critical fields due to its technical advantages. However, with the explosive growth in spectral data volume, the challenges of high-dimension and redundant information of spectra are becoming increasingly prominent. Therefore, dimensionality reduction of spectra is crucial for LIBS. In this study, to enhance the discrimination accuracy of brown rice flour and its adulterants, a systematic comparative analysis of eight unsupervised feature extraction methods was conducted, including principal component analysis (PCA), non-negative matrix factorization (NMF), independent component analysis (ICA), multidimensional scaling (MDS), kernel PCA (KPCA), locally linear embedding (LLE), Laplacian eigenmaps (LE), and isometric mapping (ISOMAP). The results indicate that linear feature extraction methods (PCA, NMF, ICA, and MDS) show a more obvious improvement in the analytical performance of the classification models, compared to nonlinear feature extraction methods (KPCA, LLE, LE, and ISOMAP). Among these methods, the commonly used PCA has the best improvement effect. The accuracy, macro precision, macro recall, and macro F1-score of the classification models improve from 92.73%, 94.13%, 92.92%, and 0.93 to 95.52%, 95.67%, 95.63%, and 0.96. Therefore, this study can provide experience and guidance for selecting appropriate unsupervised feature extraction methods in subsequent research, contributing to the further application and development of LIBS.

Quantifying of Radioactive Elements in Soil, Water and Plant Samples using Laser Induced Breakdown Spectroscopy (LIBS) Technique

In this study, the laser-induced breakdown spectroscopy (LIBS) technique was applied to detect radioactive elements on surface soil, plant leaves (Tamarindus indica), and flow water collected from Bala, Mayo-Kebbi Quest, Chad. The survey process indicates that the samples contain radioactive elements U, Fr, Ce, Ac, Cm, Tb, and Pm. The concentrations of the detected elements in samples were determined using the calibration curve method, while the plasma temperatures (Texc) and electron density (Ne) of the detected radioactive elements were calculated from Boltzmann linear plots. Except for uranium, all elements in the soil sample had amounts below the acceptable limit. The concentration of uranium in soil samples ranges from 0.024236 to 0.23439 ppm, with a safety limit of 0.03 ppm. The average plasma temperature Texc for uranium (U) is 700, while the electron density Ne is 500 cm-3 .

Laser-induced breakdown spectroscopy analysis of tourmaline: protocols, procedures, and predicaments

Abstract. Laser-induced breakdown spectroscopy (LIBS) is an appropriate choice of analytical tool for analysis of complex minerals because it is rapid, requires little sample preparation, and acquires major and trace element compositional information on all naturally occurring elements at concentrations above their intrinsic levels of detection for the specific analyte material. Tourmaline, a complex borosilicate mineral supergroup, was chosen as a test mineral due to the complexity of its major and minor element composition. Four analytical issues were investigated during project development: (1) the spacing between analytical laser shots to avoid analysis of the recast from previous laser ablations, (2) the efficacy of using a cleaning shot prior to data acquisition, (3) the number of ablations required to collect an average spectrum that is representative of the tourmaline sample, and (4) the effect of spectrometer drift on principal component analysis (PCA) when using the entire LIBS spectra to model the compositional variations within the sample suite. The minimum spacing between locations of analysis was determined to be 800 µm for the analytical conditions used in this study by examining spectra acquired in a 2×2 grid across a quartz–tourmaline boundary. At a spacing of 100 µm, twice the diameter of the laser beam, the intensity of the boron I peaks at 249.68 and 249.77 nm was very low in the first location (quartz) but quite high in the fourth location (quartz) due to deposition of tourmaline-composition recast by laser shots in the second and third locations (both on tourmaline). Increasing the distance between locations to 800 µm ensured that the area analyzed largely avoided the recast layer from previous ablations. Given that the distribution of recast was taken into account, no cleaning shots were collected. PCA score plots calculated using successively larger numbers of spectra from the same sample show that a total of 64 spots, or 16 2×2 grids, are needed to acquire a representative average analysis of tourmaline. Spectrometer drift was recognized in PCA loading plots by a characteristic splitting of element peaks; half the peak indicates positive loading and the other half of the peak indicates negative loading. Drift correction was aligned by placing the Ca II peak at 393.34 nm in the 393.398 bin; this correction eliminated split peaks in loading plots. The resolution of these issues yielded LIBS spectra suitable for multivariate statistical analysis that can be applied to understanding geologic processes. These results contribute to the application of rapid LIBS analysis of complex geomaterials.

In-situ online detection of carbon during combustion via laser-induced breakdown spectroscopy

The combustion of fossil fuels is primarily responsible for disrupting the carbon cycle equilibrium by releasing greenhouse gases (GHGs). Therefore, detecting GHG emissions from fossil fuels is extremely important. In this study, utilizing laser-induced breakdown spectroscopy (LIBS), a new method for real-time in-situ detection of carbon fluctuations during combustion has been developed. The combustion of fossil fuels is emulated through the controlled burning of candles within a confined area, and the elemental content of the surrounding air during this process is analyzed. Fluctuations in the intensity of CN spectral lines were tracked to reveal changes in carbon concentration. The backpropagation neural network (BPNN) is used to identify and verify local air with different carbon concentrations, and the predictions are accurate. In conclusion, the integration of BPNN and LIBS for the purpose of identifying variations in carbon content during combustion provides an effective method for environmental management.