Laser-induced Breakdown Spectroscopy Research Articles

Detection and diagnosis of bacterial pathogens in urine using laser-induced breakdown spectroscopy

The presence of bacterial cells from three species has been detected in clinical specimens of human urine using laser-induced breakdown spectroscopy (LIBS) by using a partial least squares discriminant analysis (PLS-DA) of 360 spectra obtained from 12 specimens of infected urine and 239 spectra obtained from eight specimens of sterile urine. Nominally sterile urine specimens obtained from four patients at a local hospital after being screened negative for the presence of bacterial pathogens were spiked with known aliquots of Escherichia coli, Staphylococcus aureus, and Enterobacter cloacae to simulate clinical urinary tract infections. Fifteen emission line intensities measured from the LIBS spectra and 92 ratios of those line intensities were used as 107 independent variables in the PLS-DA for discrimination between bacteria-containing specimens and sterile specimens. The PLS-DA models possessed a 98.3% sensitivity and a 97.9% specificity for the detection of pathogenic cells in urine when single-shot LIBS spectra were tested. To increase the signal to noise ratio, thirty spectra acquired from a single specimen were also averaged together and the averaged spectra were used to construct a model. When each averaged spectrum was withheld from the model individually for testing, the diagnostic test possessed a 100% sensitivity and a 100% specificity for the detection of bacterial cells in urine, although the number of test spectra was necessarily reduced.The entire LIBS spectrum from 200 nm – 590 nm was input into an artificial neural network analysis with principal component analysis pre-processing (PCA-ANN) to diagnose the bacterial species once detected. This PCA-ANN test possessed an overall sensitivity of 97.2%, an overall specificity of 98.6%, and an overall classification accuracy of 97.9% when using 80% of the data to build a model and withholding 20% for cross-validation testing. The PCA-ANN was also performed on each of the 12 bacteria-containing filters individually, using the other 11 filters to build the model. The average sensitivity of this test, calculated by averaging the sensitivities measured for each of the three bacterial species, was 70.9% and the average specificity was 85.5%. Based on these results, the average classification accuracy for the test when used to discriminate between the three microorganisms was 80.6%. These results indicate the potential usefulness of LIBS for rapidly detecting and possibly diagnosing urinary tract infections in a clinical setting.

A Review Of Development In The Research Of Self-Absorption On Laser-Induced Breakdown Spectroscopy

A Review Of Development In The Research Of Self-Absorption On Laser-Induced Breakdown Spectroscopy

Data Fusion of Laser-Induced Breakdown Spectroscopy (LIBS) and RAMAN Spectroscopy: Improving the Accuracy of Ginseng Classification

Ginseng, a traditional Chinese herbal medicine, has experienced increasing application. Due to variations in the medicinal value of ginseng based upon its cultivation location, an accurate method is required to identify its origin. Here laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy were employed with machine learning algorithms for the authentication of ginseng. Accurate origin identification was conducted on dried white ginseng. Initially, LIBS and Raman spectra were acquired separately. Regional differences were not observable in the LIBS spectra. Conversely, in the Raman spectra, similarity was observed between the Baishan and Dunhua regions, contributing to reduced accuracy. Subsequently, the LIBS and Raman spectra were fused. Principal component analysis (PCA) was employed to reduce the dimensionality of the LIBS, Raman, and fused data. The top 20 principal components for these data sets explained variances of 97.17%, 86.51%, and 94.22%, respectively. The reduced-dimension LIBS, Raman, and LIBS-Raman data were utilized as input variables for random forest, extreme gradient boosting, and categorical boosting (CatBoost) classification. The results indicate that the LIBS-Raman data yield the highest accuracy with CatBoost reaching 99%. Compared to the results using LIBS and Raman, there were improvements in accuracy of 11% and 6%. This method holds significance for the rapid and accurate identification of ginseng.

Application of handheld/portable spectroscopic tools to the identification, inner stratigraphy and mapping of archaeological metal artefacts

Field handheld/portable instrumentations, such as in-situ geochemical analyzers, have the potential to assist efficiently targeted geochemical archaeometry campaigns in detecting and quantifying specific elements. Non-destructive portable energy dispersive x-ray fluorescence and micro-destructive handheld laser-induced breakdown spectroscopy (LIBS) instrumentation were utilized to investigate the elemental composition, internal stratigraphy by depth profiling and microscale compositional mapping of five copper and two iron alloy artefacts collected from various ancient graves in the Minervino Murge area, Apulia, Italy. The primary elements identified by both techniques included Cu, Sn and Pb in copper alloys, and Fe with minor amounts of Cu and Pb in iron alloys. Furthermore, the elements Al, Ca, Si, Mg, Na and K, mostly originated from soil contamination, and the trace elements Sb, Ni and Zn were detected. The satisfactory performance of both techniques was assessed by their capacity to provide reproducible elemental composition data. Finally, the depth profile and mapping achieved by LIBS contributed to understanding the metal processing and history of the objects studied, so confirming both techniques to be robust analytical tools in outdoor archaeology and archaeometry campaigns.

RHDLPP: A multigroup radiation hydrodynamics code for laser-produced plasmas

In this paper, we introduce the RHDLPP, a flux-limited multigroup radiation hydrodynamics numerical code designed for simulating laser-produced plasmas in diverse environments. The code bifurcates into two packages: RHDLPP-LTP for low-temperature plasmas generated by moderate-intensity nanosecond lasers, and RHDLPP-HTP for high-temperature, high-density plasmas formed by high-intensity laser pulses. The core radiation hydrodynamic equations are resolved in the Eulerian frame, employing an operator-split method. This method decomposes the solution into two substeps: first, the explicit resolution of the hyperbolic subsystems integrating radiation and fluid dynamics; second, the implicit treatment of the parabolic part comprising stiff radiation diffusion, heat conduction, and energy exchange. Laser propagation and energy deposition are modeled through a hybrid approach, combining geometrical-optics ray-tracing in sub-critical plasma regions with a one-dimensional solution of the Helmholtz wave equation in super-critical areas. The thermodynamic states are ascertained using an equation of state, based on either the real gas approximation or the quotidian equation of state (QEOS). For ionization calculations, the code employs a steady-state collisional-radiation (CR) model using the screened-hydrogenic approximation. Additionally, RHDLPP includes RHDLPP-SpeIma3D, a three-dimensional spectral simulation post-processing module, for generating both temporally-spatially resolved and time-integrated spectra and imaging, facilitating direct comparisons with experimental data. The paper showcases a series of verification tests to establish the code's accuracy and efficiency, followed by application cases, including simulations of laser-produced aluminium (Al) plasmas, pre-pulse-induced target deformation of tin (Sn) microdroplets relevant to extreme ultraviolet lithography light sources, and varied imaging and spectroscopic simulations. These simulations highlight RHDLPP's effectiveness and applicability in fields such as laser-induced breakdown spectroscopy, extreme ultraviolet lithography sources, and high-energy-density physics.

Temporally modulating laser pulses to stabilize LIBS measurement locations under large gas temperature gradients

In combustion research, laser-induced breakdown spectroscopy (LIBS) has been widely employed in local equivalence ratio measurement. However, the potential temperature gradients in the probe volume can significantly affect the shape of induced plasmas, resulting in unstable measurement locations. In this work, we improved the stability of measurement locations by modulating the laser pulse duration. In a hot-cold gas flow interface with large temperature gradients, when using the original laser pulse with a full width at half maximum (FWHM) of 4 ns, the locations of initial plasma core were insensitive to gradient variations; however, the plasma expansion behaviors differed significantly after 3 ns. The hot spots of plasmas diverged bi-directionally under high temperature, resulting in two-lobe structures and unstable measurement locations. After the laser pulse was modulated to a shorter duration using a pressure chamber, the plasma expansion was suppressed which constrained the plasma volume. Specifically, using a modulated pulse with a FWHM of 1.9 ns, the two-lobe structure was eliminated across the interface, and the standard deviation of measurement locations was reduced to 0.27 mm. The measured equivalence ratios across the interface showed favorable agreement with the simulation.

Classification of e-waste using machine learning-assisted laser-induced breakdown spectroscopy.

Waste management and the economy are intertwined in various ways. Adopting sustainable waste management techniques can contribute to economic growth and resource conservation. Artificial intelligence (AI)-based classification is very crucial for rapid and contactless classification of metals in electronic waste (e-waste) management. In the present research work, five types of aluminium alloys, because of their extensive use in structural, electrical and thermotechnical functions in the electronics industry, were taken. Laser-induced breakdown spectroscopy (LIBS), a spectral identifier technique, was employed in conjunction with machine learning (ML) classification models of AI. Principal component analysis (PCA), an unsupervised ML classifier, was found incapable to differentiate LIBS data of alloys. Supervised ML classifier was then trained (for 10-fold cross-validation) on randomly selected 80% and tested on 20% spectral data of each alloy to assess classification capacity of each. In most of the tested variants of K nearest neighbour (kNN) the resulting accuracy was lower than 30% but kNN ensembled with random subspace method showed improved accuracy up to 98%. This study revealed that an AI-based LIBS system can classify e-waste alloys rather effectively in a non-contactless mode and could potentially be connected with robotic systems, hence, minimizing manual labour.

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.