Laser-induced Breakdown Spectroscopy Spectra 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.

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.

Isotopic analysis of uranium at the picogram level with single-shot laser induced breakdown spectroscopy

Uranium was measured by laser induced breakdown spectroscopy (LIBS) with a limit of detection of 1.3 picograms with spectral-peak height as the analytical parameter, which shows an improvement of several orders of magnitude compared to other reports in the literature. The improvement is a result of optimizing the choice of the U analytical line, the system hardware, and the light collection and detection efficiency. The research established properties of U line emission (intensity, signal-to-background ratio, isotopic splitting, and spectral interferences) to select an optimum wavelength for analysis. The samples were mock particles from dried, monodispersed U solution droplets (∼65 pL in volume) deposited on Si wafers. LIBS emission spectra from several 235U-enriched samples were measured to demonstrate the performance for isotopic analysis. For isotopic measurements, two masses for total U, 100 pg and 325 pg, were studied. The 235U: 238U isotopic ratio was obtained by peak fitting the LIBS spectra without requiring calibration with the use of enriched standards. Depending on the total U mass and the 235U enrichment, typical relative standard deviations of the isotopic analysis ranged from several percent down to about 1%. Further, the developed method and the reported analytical performance were all based on single-shot LIBS. Overall, the study highlights the potential of LIBS as a robust and sensitive technique for isotopic analysis of U-containing particles.

Research on a new multiple-screening method for laser-induced plasma spectroscopy utilizing Lorentz

In the field of Laser Induced Breakdown Spectroscopy (LIBS) research, the screening and extraction of complex spectra play a crucial role in enhancing the accuracy of quantitative analysis. This paper introduces a novel approach for multiple screenings of LIBS spectra using Lorentz Screening and Sensitivity and Volatility Analysis. Initially, Create symmetrical sampling standards for Lorentz fitting. Then the Lorentz fitting is used to uniformly screen the collected spectral information on both sides in order to eliminate adjacent interference peaks. Subsequently, Sensitivity and Volatility Analysis is employed to further remove overlapping peaks and select spectra with low volatility and high sensitivity. Sensitivity and Volatility Analysis is a spectral discrimination method proposed on the premise of intensity's correlation with concentration. It utilizes a Z-score method that incorporates both deviation and standard deviation for effective analysis. Furthermore, it meticulously selects spectral lines with minimal interference and volatility, thereby augmenting the precision of quantitative analysis. The quantitative accuracy (R2) for Chromium (Cr) and Nickel (Ni) elements can reach 0.9919 and 0.9768, respectively. Their average errors can be reduced to 0.0566 % and 0.1024 %. The study demonstrates that Lorentz Screening and Sensitivity and Volatility Analysis can select high-quality characteristic spectral lines to improve the performance of the model.

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

The ability to rapidly and accurately detect and identify pathogenic bacteria in clinically-obtained blood specimens with laser-induced breakdown spectroscopy (LIBS) is evaluated. Samples of blood obtained from five patients in a local hospital were confirmed to be negative for the presence of bacteria by the pathology department and were then tested with LIBS. Specimens of blood were tested as obtained from the hospital with no other alteration as control samples and were also intentionally spiked with known aliquots of Escherichia coli, Staphylococcus aureus, Enterobacter cloacae, and Pseudomonas aeruginosa to simulate blood infections. LIBS spectra were acquired from blood deposited on nitrocellulose filters. The intensities of 15 emission lines measured in the spectra and 92 ratios of those line intensities were used as 107 independent variables in a partial least squares discriminant analysis (PLS-DA) to discriminate between sterile control samples and those spiked with bacteria. In addition, 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.The PLS-DA test possessed a 96.3% sensitivity and a 98.6% specificity for the detection of pathogenic bacteria in blood when 776 spectra from 26 filters were tested by removing one entire filter at a time from the model and testing each spectrum individually. When all the spectra obtained from a filter were averaged to enhance the signal to noise of the spectrum, 19 of 19 filters of infected blood tested positive and 7 of 7 filters with sterile blood tested negative, yielding 100% sensitivity and 100% specificity. The PCA-ANN test performed on the entire LIBS spectrum possessed a 100% sensitivity and 100% specificity when using 80% of the data to build a model and withholding 20% for cross-validation testing. The same PCA-ANN performed on each of the 19 filters individually, using the other 18 filters to build the model, possessed an average sensitivity of 85.5%, an average specificity of 95.0%, and a classification accuracy of 92.5%. These results indicate the potential usefulness of LIBS for detecting and diagnosing blood infections in a clinical setting.

Quantification of H2O in Amorphous and Crystalline Ferric Sulfates Relevant to Mars

AbstractCrystalline ferric sulfates (e.g., ferricopiapite and Fe(OH)SO4) have been proposed at multiple locations on Mars by the orbiter. While at Meridiani Planum and Gale Crater by rover missions, amorphous ferric sulfates were also suggested to exist in soils and sedimentary rocks. Ferric sulfates with different hydration degrees may play a key role in the water cycle on Mars. In order to understand the role of the hydrated ferric sulfates in the water cycle and their exact hydration states on Mars, five crystalline ferric sulfates and seven amorphous ferric sulfates with different hydration states were synthesized in the laboratory. The water content (number of H2O molecules per ferric sulfate molecule) was quantified by Raman spectroscopy and Laser‐induced breakdown spectroscopy (LIBS), respectively. It was found that the amorphous ferric sulfates water content has a good relationship with the SO4 tetrahedron main Raman feature position around 1,000 cm−1, shown as the intensity and area of water feature around 3,500 cm−1 over SO4 tetrahedron main Raman feature around 1,000 cm−1, respectively. Twelve ferric sulfates' Hα emission line area at 656.7 nm was normalized by the O emission line area at 778 nm in LIBS spectra. The crystalline and amorphous ferric sulfates all showed a good relationship between the values of normalized results and the water content. These results will aid us in precisely constraining the exact phases of hydrated ferric sulfates, provide a better reference for ChemCam, SuperCam, and Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals data interpretation and their use to quantify the water content in detected targets.

Surface hardness determination of laser cladding using laser-induced breakdown spectroscopy and machine learning (PLSR, CNN, ResNet, and DRSN).

In this study, we employed laser-induced breakdown spectroscopy (LIBS) along with machine learning algorithms, which encompass partial least squares regression (PLSR), the deep convolutional neural network (CNN), the deep residual neural network (ResNet), and the deep residual shrinkage neural network (DRSN), to estimate the surface hardness of laser cladding layers. (The layers were produced using Fe316L, FeCrNiCu, Ni25, FeCrNiB, and Fe313 powders, with 45 steel and Q235 serving as substrates.) The research findings indicate that both linear and nonlinear models can effectively fit the relationship between LIBS spectra and surface hardness. Particularly, the model derived from the ResNet exhibits superior performance with an R 2 value as high as 0.9967. We hypothesize that the inclusion of numerous noises in the LIBS spectra contributes to the enhanced predictive capability for surface hardness, thereby leading to the superior performance of the ResNet compared to the DRSN.

Determination of inorganic and organic carbons in a Martian soil simulant under the Martian CO2 atmosphere using LIBS coupled with machine learning

Carbon plays a crucial role in the search for extraterrestrial life and serves as an indicator for the habitability and the paleoatmospheric CO2 reservoir on Mars. Previous exploration missions provided evidence of carbon on Mars with different chemical speciation using various instruments including laser-induced breakdown spectroscopy (LIBS). Quantitative determination with LIBS onboard Mars rovers is still precluded because of the important contribution of the atmospheric carbon in the LIBS plasma, in addition to matrix effects omnipresent for elemental determination with LIBS of geological materials. In this work, we performed a series of LIBS experiments in a simulated Martian atmosphere using samples prepared with a Martian soil simulant mixed with various carbonates and organic C-bearing materials. Convoluted influences on LIBS spectra due to the ambient gas and the different chemical speciation of carbon were observed for inorganic and organic C-bearing materials, which explains the unsatisfactory performance for a univariate regression model based on a carbon-related emission line. In particular, the influence of ambient gas was observed more important for inorganic carbon brought into the samples with carbonates. Multivariate models were then developed based on a back-propagation neural network (BPNN), for ensembles of samples with inorganic and organic carbons respectively, and then for the fusion of the two ensembles. The results showed respective limits of detection (LODs) of 0.247 wt%, 1.022 wt% and 0.873 wt%, and respective root mean square errors of prediction (RMSEPs) of 0.036 wt%, 0.133 wt%, and 0.062 wt% for the three collections of samples. Moreover, the model training process is investigated in detail in order to understand the way in which the most significant spectral features are selected, processed and mapped to the carbon concentrations of the samples by a neural network.

Assessment of the Electrolyte Heterogeneity of Tissues in Mandibular Bone-Infiltrating Head and Neck Cancer Using Laser-Induced Breakdown Spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) was recently introduced as a rapid bone analysis technique in bone-infiltrating head and neck cancers. Research efforts on laser surgery systems with controlled tissue feedback are currently limited to animal specimens and the use of nontumorous tissues. Accordingly, this study aimed to characterize the electrolyte composition of tissues in human mandibular bone-infiltrating head and neck cancer. Mandible cross-sections from 12 patients with bone-invasive head and neck cancers were natively investigated with LIBS. Representative LIBS spectra (n = 3049) of the inferior alveolar nerve, fibrosis, tumor stroma, and cell-rich tumor areas were acquired and histologically validated. Tissue-specific differences in the LIBS spectra were determined by receiver operating characteristics analysis and visualized by principal component analysis. The electrolyte emission values of calcium (Ca) and potassium (K) significantly (p < 0.0001) differed in fibrosis, nerve tissue, tumor stroma, and cell-rich tumor areas. Based on the intracellular detection of Ca and K, LIBS ensures the discrimination between the inferior alveolar nerve and cell-rich tumor tissue with a sensitivity of ≥95.2% and a specificity of ≥87.2%. The heterogeneity of electrolyte emission values within tumorous and nontumorous tissue areas enables LIBS-based tissue recognition in mandibular bone-infiltrating head and neck cancer.

Rare earth elements and radioactive elements in black sands from the Konkan and Malabar coasts of India

ABSTRACT The objective of the present work is to demonstrate the ability of the laser-induced breakdown spectroscopy (LIBS) technique for quick detection and quantification of rare earth elements (REEs) and radioactive elements in the black sands at selected locations of the Konkan coast (Goa) and Malabar coast (Kerala) in India. Several REEs such as Ce, Nd, Sm, Dy, Ho, Tm, Gd, and Th have been detected in the LIBS spectra. Calibration-free LIBS method has been used to calculate the concentration of REEs and radioactive elements. The experimental result shows that the concentration of REEs such as Sm, Dy, Ho, and radioactive element Th is maximum in the sands collected from Thiruvananthapuram (Kerala) beach and minimum in the Baga (Goa) beach. The reliability of the results (qualitative and quantitative) obtained by the LIBS method has been examined by other spectroscopic techniques like XRF and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and found to be in close agreement. The multivariate approach (principal component analysis) has been applied to discern the samples based on their spectral data. Photoacoustic spectroscopy and XRD technique are used to detect compounds/minerals of REEs. The relative hardness, degree of crystallinity (DOC), and crystallite size have been calculated.

Study on the spectra of Tieguanyin tea and ash by laser-induced breakdown spectroscopy

Tea ash is the inorganic residue of tea after a series of chemical and physical reactions under high temperature environment. The compositions of tea and ash are very different. In addition, the compositions of ash are closely related to the quality of tea. In this work, the compositions of Tieguanyin tea and ash were analyzed by laser-induced breakdown spectroscopy (LIBS), and the differences of elements in tea and ash were analyzed qualitatively. Tea contains the main elements, such as Ca, Na, Mg, and K, trace elements Fe, Cu, Zn, Mn and Sr, and other metallic elements Al, Li and Ba. Comparing the atomic and ionic lines in LIBS spectra of tea and ash, it was found that the spectral line intensities of metallic elements, i.e. Ca, Al, Mn, Mg, K, Na, Li, in the ash were significantly enhanced. In addition, there were CN, C2 and CaO molecular lines in the LIBS spectra of tea, but only CaO free radical was detected in the LIBS spectra of ash. The reason of the differences between the molecular lines of LIBS spectra of tea and ash was analyzed. This work is to determine the material compositions of tea and ash, which provides a basic foundation for the quality judgment of tea and a new idea for the enrichment detection of metallic elements.

Chert sourcing using LIBS: The case of Cova del Parco, Spain

AbstractCherts originating from carbonate lacustrine environments have been widely exploited by Upper Palaeolithic hunter‐gatherers in the south of the Pyrenees. Archaeo‐petrographic sourcing studies have identified different potential sources but were unable to distinguish them. This study conducted geochemical characterisation of geological and archaeological lacustrine chert samples using laser‐induced breakdown spectroscopy (LIBS), and applied the supervised classification model partial least squares–discriminant analysis to LIBS spectra. The model was able to discriminate geological sources and provide predictions for the origin of a selection of chert artefacts recovered at the Middle Magdalenian level from Cova del Parco (Lleida, Spain).

Characteristics of laser-induced breakdown spectroscopy of liquid slag

Rapid online analysis of liquid slag is essential for optimizing the quality and energy efficiency of steel production. To investigate the key factors that affect the online measurement of refined slag using laser-induced breakdown spectroscopy (LIBS), this study examined the effects of slag composition and temperature on the intensity and stability of the LIBS spectra. The experimental temperature was controlled at three levels: 1350 °C, 1400 °C, and 1450 °C. The results showed that slag composition and temperature significantly affected the intensity and stability of the LIBS spectra. Increasing the Fe content and temperature in the slag reduces its viscosity, resulting in an enhanced intensity and stability of the LIBS spectra. Additionally, 42 refined slag samples were quantitatively analyzed for Fe, Si, Ca, Mg, Al, and Mn at 1350 °C, 1400 °C, and 1450 °C. The normalized full spectrum combined with partial least squares (PLS) quantification modeling was used, using the Ca II 317.91 nm spectral line as an internal standard. The results show that using the internal standard normalization method can significantly reduce the influence of spectral fluctuations. Meanwhile, a temperature of 1450 °C has been found to yield superior results compared to both 1350 °C and 1400 °C, and it is advantageous to conduct a quantitative analysis of the slag when it is in a “water-like” state with low viscosity.

Investigating the effect of changing the substrate material analyzed by laser-induced breakdown spectroscopy on the antenna performance

Miniaturized microstrip antennas are efficiently utilized in MICS band wearable and implantable medical applications. However, the properties of the materials employed for antenna fabrication influence its resultant parameters and play a vital role in its performance. Rogers have been widely used as a substrate material in various antenna designs. In this work, a proof of concept study has been conducted to determine how altering the substrate used in antenna construction affects antenna performance. Using the laser-induced breakdown spectroscopy (LIBS) approach, the elements present in the two distinct substrate raw materials were compared to investigate potential effects on the antenna’s performance. Given their accessibility and widespread use, two types of Rogers’ substrates, RO 3210 and RO 4003, were selected. Furthermore, two identical antenna designs were modeled and fabricated using the two substrate materials. The reflection coefficient (S11) and other antenna parameters were determined and compared. Moreover, the recorded LIBS spectra were evaluated using principle component analysis and partial least square regression techniques. The LIBS spectra showed different copper and iron contents between the two Rogers (i.e., other dielectric properties), leading to a frequency shift. Additionally, impurities in the fabricated material increase the possible losses. Consequently, the elemental contents of the utilized Rogers control the antenna’s performance and can ensure its safety in wearable and implant applications.

Sharing calibration information among laser-induced breakdown spectroscopy instruments using spectral line binning and calibration transfer

The ability of a publicly accessible preexisting laser-induced breakdown spectroscopy (LIBS) database to train multivariate models to predict rock and mineral compositions from other laboratories or from remotely collected spectra is evaluated using LIBS spectra collected on >2500 unique geological targets using three different instruments and a range of collection protocols. Datasets collected under increasingly disparate conditions are utilized, including a single instrument with different resolution settings; two different instruments with very similar ablation and collection optics; and a benchtop instrument and portable instrument with different collection protocols, resolutions, and plasma conditions. Cross-calibration among datasets is performed for a range of scenarios designed to test the efficacy of post-processing techniques.Major element predictions are most accurate when instrument parameters match among training and test spectra. Use of a piecewise direct standardization-partial least squares (PDS-PLS) calibration transfer algorithm reduces major element prediction uncertainties when the resolution of training and test spectra do not match. Even when training and test spectra are collected on different instruments, reasonable predictions can be derived by binning peak areas prior to training calibration models. Finally, incorporating a large, preexisting database into a smaller dataset collected under test conditions has the potential to greatly improve the reliability of predicted compositions. With careful consideration to match plasma conditions and utilize post-processing, existing large-scale LIBS calibration databases like the one used in this study can be used to boost LIBS accuracy and expand the potential of LIBS as a widely-applicable quantitative geochemical tool.

Novel Calibration Approach for Monitoring Aerosol Hydrogen Isotopes Using Laser-Induced Breakdown Spectroscopy for Molten Salt Reactor Off-Gas Streams.

Online monitoring is a key challenge for the continued development of molten salt reactor (MSR) technology. Laser-induced breakdown spectroscopy (LIBS) has previously been demonstrated to be a viable tool for monitoring aerosolized species and noble gases in real time, but the ability to discern varying isotopes in these streams has not yet been investigated for MSR applications. Tritium will form in MSRs from ternary fission and from (n,α)-reactions occurring in lithium-containing salts. This study compares three spectrometers of varying resolutions and types for measuring hydrogen isotope shifts in LIBS spectra of wetted filter paper. For each spectrometer, multivariate models were built (i.e., principal component regression, partial least squares regression, and multivariate curve resolution) to quantify the isotope ratio. The top models were then modified and corrected to apply the models to aerosol samples with varying isotope ratios. This novel calibration strategy offers an 82% reduction in volume of the calibration samples needed and is a more viable pathway for calibrating deployable LIBS systems. Lastly, this calibration model was compared with an all-aerosol trained model for monitoring hydrogen isotopes during a real-time test where the protium/deuterium ratio, along with representative salt species (i.e., lithium, sodium, and potassium) were adjusted dynamically. Results of this test validated the predictive capabilities of the transferred model and highlighted the capabilities of LIBS for real-time monitoring of MSR effluent streams.

Fusion of laser-induced breakdown spectroscopy technology and deep learning: a new method to identify malignant and benign lung tumors with high accuracy.

Precisely distinguishing between malignant and benign lung tumors is pivotal for suggesting therapeutic strategies and enhancing prognosis, yet this differentiation remains a daunting task. The growth rates, metastatic potentials, and prognoses of benign and malignant tumors differ significantly. Developing specialized treatment protocols tailored to various tumor types is essential for enhancing patient survival outcomes. Employing laser-induced breakdown spectroscopy (LIBS) in conjunction with a deep learning methodology, we attained a high-precision differential diagnosis of malignant and benign lung tumors. First, LIBS spectra of malignant tumors, benign tumors, and normal tissues were collected. The spectra were preprocessed and Z score normalized. Then, the intensities of the Mg II 279.6, Mg I 285.2, Ca II 393.4, Cu II 518.3, and Na I 589.6 nm lines were analyzed in the spectra of the three tissues. The analytical results show that the elemental lines have different contents in the three tissues and can be used as a basis for distinguishing between the three tissues. Finally, the RF-1D ResNet model was constructed by combining the feature importance assessment method of random forest (RF) and one-dimensional residual network (1D ResNet). The classification accuracy, precision, sensitivity, and specificity of the RF-1D ResNet model were 91.1%, 91.6%, 91.3%, and 91.3%, respectively. And the model demonstrates superior performance with an area under the curve (AUC) value of 0.99. The above results show that combining LIBS with deep learning is an effective way to diagnose malignant and benign tumors.

Characterization of impurity distribution and composition on the shutter plate for an optical diagnosis in EAST tokamak using laser-induced breakdown spectroscopy

The quantification of impurities in tokamak devices is of great significance as it allows for a better understanding of the plasma performance and the lifetime of plasma-facing components (PFCs). This study focuses on the characterization of the impurity distribution and composition on the surface of a shutter plate for an optical diagnosis in the Experimental Advanced Superconducting Tokamak (EAST) by Laser-Induced Breakdown Spectroscopy (LIBS). The primary impurity elements, including copper (Cu), tungsten (W), molybdenum (Mo), lithium (Li), and Sodium(Na), were identified by analyzing the LIBS spectra obtained from the surface of the shutter plate. The spatial distribution of these impurities on both sides of the plate was investigated. The analysis results indicate an increase in impurity content on the outer surface of the shutter plate as proximity to Last Closed Flux Surface (LCFS), followed by a near-linear decrease with the distance from the LCFS. The results of the inner ring on the inner side facing the glass window show fewer metal impurities than the outer rings on the edge for the inner side of the plate. Moreover, the thickness of the deposition layer was determined by estimating the Average Ablation Rate (AAR) through the measurement by confocal microscopy. In addition, Calibration-Free LIBS method were performed to determine the relative content of impurities. The results of this study provide valuable references for studying the migration and transportation of impurities.

Impact of intensity error on temperature estimation in laser-induced breakdown spectroscopy: a numerical study

Laser-induced breakdown spectroscopy (LIBS) is a cutting-edge technique for the compositional analysis of multi-element materials. Under standard circumstances for laser-induced plasma (T e = 1 eV and N e = 1016 cm−3), we simulated the emission spectrum of a binary alloy (with 70 wt.% Cu–30 wt.% Al). We used the Saha ionization equilibrium formulas to calculate the population of neutral and ionized species of each constituent element, and the Boltzmann distribution to estimate the intensities of emission lines with radiative transition probabilities. The Stark broadening equation is then used to determine the line broadening, yielding a Lorentzian profile for each line. The sum of line emissions of all constituent species will approximate the alloy’s LIBS spectra in an assumption of ideal analytical plasma. Then, we generated random errors in the intensities of spectral lines ranging from 5% to 35%. To investigate temperature estimation accuracy, we utilized three well-established approaches: the Boltzmann plot (BP) method, the Saha–Boltzmann plot (SBP) method, and the Multi-elemental SBP (MESBP) method. As intensity error increases from 5% to 35%, the estimated temperature in the BP method deviates from 0.25% to 18.3%. Whereas the intensity error is almost unaffected using the SBP method and the MESBP method. The temperature deviation is less than 2% in both situations. This study is relevant to calibration-free LIBS, in which the exact temperature determination is crucial for the abundance estimation of trace, major, and minor elements.

Laser-induced breakdown spectroscopy in inhomogeneous fuel sprays: A novel calibration methodology for local stoichiometry

This paper presents a novel calibration for estimating the local stoichiometry of inhomogeneous sprays when using Laser-Induced Breakdown Spectroscopy (LIBS). The LIBS spectrum in inhomogeneous sprays appears to consist of coexisting spectra. This renders the traditional curve-fit calibration method inapplicable and impedes the determination of local equivalence ratios. A hypothesis is proposed that the spray LIBS spectra can be regarded as a discrete superposition of regions in the detection area with distinct compositions. A novel calibration approach is then proposed to test the simplest version of this hypothesis by identifying the best correlation between the spray LIBS spectra and a binary superposition of the calibration spectra. A local equivalent stoichiometry can then be estimated for the best correlated match. This approach generates spectra with high correlation to the test spray spectra for the majority of sparks, and hence provides a novel insight into LIBS application for inhomogeneous fuel/air mixtures. Test cases where the leaner region already contains fuel (ϕlean > 0) have high probability of successful ignition.