Laser-induced Breakdown Spectroscopy Signal Research Articles

Erratum to: Effect of ambient pressures on laser-induced breakdown spectroscopy signals

Erratum to: Effect of ambient pressures on laser-induced breakdown spectroscopy signals

Estimating the grain size of microgranular material using laser-induced breakdown spectroscopy combined with machine learning algorithms

Recent work has validated a new method for estimating the grain size of microgranular materials in the range of tens to hundreds of micrometers using laser-induced breakdown spectroscopy (LIBS). In this situation, a piecewise univariate model must be constructed to estimate grain size due to the complex dependence of the plasma formation environment on grain size. In the present work, we tentatively construct a unified calibration model suitable for LIBS-based estimation of those grain sizes. Specifically, two unified multivariate calibration models are constructed based on back-propagation neural network (BPNN) algorithms using feature selection strategies with and without considering prior information. By detailed analysis of the performances of the two multivariate models, it was found that a unified calibration model can be successfully constructed based on BPNN algorithms for estimating the grain size in the range of tens to hundreds of micrometers. It was also found that the model constructed with a prior-guided feature selection strategy had better prediction performance. This study has practical significance in developing the technology for material analysis using LIBS, especially when the LIBS signal exhibits a complex dependence on the material parameter to be estimated.

Quantitative measurement of sodium emissions during oxy-fuel combustion of a beech wood biomass particle using laser-induced breakdown spectroscopy

Oxy-fuel biomass combustion in a circulating fluidized bed is a clean and sustainable biomass utilization technology. Woody forest biomass is considered a promising source of renewable energy because of its widespread availability across the globe. However, effective biomass utilization in thermal power plants is largely limited by severe ash deposition and corrosion of heat transfer surfaces due to alkali metal and chlorine emissions during biomass combustion. In this study, laser-induced breakdown spectroscopy (LIBS) was applied to detect in-situ gas phase sodium (Na) emission during single particle combustion of widely available forest biomass, namely, beech wood. The operating conditions simulate the thermo-chemical conditions relevant to a fluidized bed combustor system, where the operating temperature varies between 1073K–1273K. This work aims to investigate the effect of O2 concentration, CO2 (oxyfuel environment), and particle size on the emission of sodium during the combustion of a single beech wood particle. The unresolved sodium doublet was used as the LIBS signal to quantify sodium emission concentration.Oxygen concentration is the most critical parameter affecting Na emissions, and an increase in O2 concentration led to higher Na emissions. It is observed that there is a significant reduction in the emission of Na in an O2/CO2 environment as compared to an O2/N2 environment. It is hypothesized that this drop in Na emissions in O2/CO2 environment could be due to lower particle temperatures caused by the lower diffusivity of O2 in CO2 as well as the endothermic gasification reaction in high CO2 environments. In addition, it is reported that the dissociation of sodium carboxylates and carbonates in the biomass matrix to release gaseous Na due to thermal decomposition is retarded at higher CO2 concentrations which could explain the drop in Na emissions. The effect of biomass particle size is pronounced mainly at lower concentration of O2 where, larger particles keep Na trapped in its matrix, however, at higher O2 concentrations (30%), the effect of particle size on Na emission is negligible.

Effect of ambient pressures on laser-induced breakdown spectroscopy signals

Effect of ambient pressures on laser-induced breakdown spectroscopy signals

Simultaneous Application of Raman and Laser-Induced Breakdown Spectroscopy in the Gas Phase with a Single Laser and Detector.

Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) are powerful tools for molecular and elemental analysis, respectively. Their combined application, however, is challenging due to the differences in the signal generation and detection characteristics. This note proposes three experimental schemes for the simultaneous application of Raman and LIBS for gas-phase diagnostics. Ring-cavity optical pulse stretchers facilitate shaping suitable pulse pairs from a Q-switched laser that enables the quasi-simultaneous detection of the Raman and LIBS signals on a single detector.

Effects of feature engineering on the robustness of laser-induced breakdown spectroscopy for industrial steel classification

Steel is a widely used material. Since steel has different properties and uses depending on the steel type, it is essential to accurately classify the steel types. LIBS is a promising technique for real-time classification due to its advantages such as rapid analysis and feasibility of measurement in air. However, enhancing the robustness of classification models is still challenging in industrial applications. This study investigates the effects of feature engineering on the robustness of laser-induced breakdown spectroscopy (LIBS) for industrial steel classification. To make LIBS applicable to the steel industry, a remote LIBS system was developed. The LIBS system was utilized to classify the six representative types of steel. To evaluate the performance of the LIBS system, the measurements were reproduced, with the same sample, at seven different laser energies. The robustness of the LIBS system was investigated by conducting experiments using various feature-engineering and learning-based algorithms on the LIBS data, with test datasets having laser energies different from those of the training datasets. The results indicate that the LIBS signal intensity ratio as the input data leads to a more robust classification model than using principal components or random forest-based datasets. In addition, the intensity ratios with similar upper-state energies were found to be more suitable input data for steel classification. These findings demonstrate the potential of the LIBS system for the highly accurate and robust classification of industrial-grade steel.

Rapid authentication of geographical origins of Baishao (Radix Paeoniae Alba) slices with laser-induced breakdown spectroscopy based on conventional machine learning and deep learning

The geographical origin of Baishao (Radix Paeoniae Alba) affects the components and content, which in turn affects its pharmacological action. Laser-induced breakdown spectroscopy (LIBS) was combined with conventional machine learning and deep learning methods to rapidly discriminate the geographical origins of Baishao slices without sample preparation. The influence of spatial variation of Baishao slices on the LIBS signal was investigated. The spectra that were averaged using 16-point spectra showed the best origin identification performance, with an accuracy of 96.7% as determined by partial least squares-discriminant analysis (PLS-DA). Meanwhile, the spectra obtained from a single point after voting showed the best origin identification performance using ResNet, with an accuracy of 95.0%. The preliminary results indicate the feasibility of using LIBS and machine learning for rapid, accurate, in situ origin identification of Baishao slices, which provides an approach for quality and adulteration supervision.

Investigating the mechanistic impact of pork soft tissue preparation techniques on the classification precision of laser-induced breakdown spectroscopy.

The investigation of the mechanism underlying the impact of biological soft tissue sample preparation methods on laser-induced breakdown spectroscopy (LIBS) signals can enhance the stability of LIBS signals. Our study focused on four specific preparation methods applied to pork samples: rapid freezing, fresh slicing, drying, and pressing. The influence of various preparation techniques on the signal-to-noise ratio and fluctuation of Ca, Na, Mg, and CN bands within the sample spectra was assessed. The signal-to-noise ratios for samples that were dried and pressed notably improved. And the pressing method effectively mitigated the uneven distribution of pork tissue components, displaying superior spectral line stability. To explain this phenomenon, we used the Saha-Boltzmann diagram to estimate the plasma temperature. Remarkably, there was a significant reduction in plasma temperature fluctuations across four pressed samples, with a standard deviation of 108.53. Furthermore, we undertook a classification analysis employing support vector machine models to corroborate the generalization efficacy of the sample preparation technique. Dried and pressed samples demonstrated notably higher classification accuracy, precision, and recall (all >93%) compared to frozen and fresh samples, where these metrics remained below 86%. The performance of the SVM model was ultimately evaluated using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC). The AUC for the frozen, fresh, dried, and pressed samples was 0.854, 0.907, 0.989, and 0.996, respectively. The findings revealed that the pressing method exhibited superior performance, followed by drying, fresh slicing, and freezing, in descending order of effectiveness.

Non-Gaussian Signal Statistics’ Impact on LIBS Analysis

A detailed study has been carried out to reveal signal statistics’ impact on analysis sensitivity in laser-induced breakdown spectroscopy (LIBS) measurements. For several signals measured simultaneously, it was demonstrated that space-, spectra- and time-integrated plasma emission followed a normal distribution while the spectra- and time-resolved LIBS signal (atomic line intensity, plasma background emissions) distribution functions were biased compared to a Gaussian distribution function. For the first time in LIBS, the impact of a non-Gaussian distribution function on the limit of detection (LOD)’s determination has been studied in detail for single-shot spectra as well as for averaged spectra. Here, we demonstrated that the non-symmetrical distribution of the LIBS signals influenced the estimated LODs, so knowledge of a LIBS signal’s distribution function provides more reliable results, and the analysis sensitivity can be wrongly estimated if Gaussian distribution is presumed.

Development of a radiation tolerant laser-induced breakdown spectroscopy system using a single crystal micro-chip laser for remote elemental analysis

ABSTRACT For the development of the remote elemental analysis method in a radiation environment based on the laser-induced breakdown spectroscopy (LIBS), the radiation effects on the laser oscillation properties of the single crystal (SC) Nd:YAG microchip laser (MCL) were investigated and compared with those of ceramics Nd:YAG MCL. The laser oscillation properties were measured under gamma ray irradiation as a function of dose rate. The effects on the SC MCL properties were found to be very small compared to those on the ceramics, indicating minimal radiation effects on the LIBS signal when using SC MCL. Pulse energy and oscillating build-up time (BUT) were measured for cumulative dose exceeding 1400 kGy. The pulse energy remained stable, and the laser continued to oscillate under irradiation. The BUT also remained stable, demonstrating negligible optical loss accumulation that could affect laser properties even at the demonstrated cumulative dose. The results indicate that the effects of dose rate and cumulative dose on SC MCL laser properties were minimal. The SC MCL was then integrated into the LIBS system, and the gadolinium signal of composite oxides, simulating fuel debris, was successfully measured at the dose rate of 5 kGy/hr. These findings highlight the radiation tolerance of SC MCL as a laser medium for remote LIBS applications in harsh radiation environments.

Study on laser-induced breakdown spectroscopy in high-pressure helium gas environment for deep ocean applications

Laser-induced breakdown spectroscopy (LIBS) has gained wide acceptance as an in situ detection technique for elements. However, in deep-sea applications, the sensitivity of LIBS detection will be reduced due to the high-pressure environment and the nature of water. To address the negative effects of high-pressure water, this study used the method of draining the water from the sample surface by passing high-pressure helium gas, which allowed the plasma excitation environment to be converted from high-pressure water to high-pressure gas. The available spectral signals of solid samples at 60 MPa gas pressure were obtained for the first time, and the peak intensity and spectral broadening of the spectral lines at different pressures were analyzed for comparison. We found a nonlinear decrease in the spectral intensity and a gradual increase in the spectral broadening during the pressure increase. We also investigated the effect of laser energy on the intensity and width of the spectral lines in a high-pressure helium environment and found that increasing the laser energy in a high-pressure environment enhanced the spectral intensity and that the change in laser energy almost did not affect the line width. Finally, by observing the plasma images at different pressures with different energies, this study found that the laser penetrated the high-pressure helium gas in advance and leaved a column of light in the gas, and the plasma was slowly made smaller as the ambient pressure increases. This finding explained the cause of laser energy loss and demonstrated that the LIBS signal intensity can be improved by increasing the laser energy and shortening the laser transmission distance in a high-pressure gas environment.

Minimally destructive laser-induced breakdown spectroscopy of brass assisted by a low-power atmospheric pressure plasma jet

Minimizing sample damage is crucial in laser-induced breakdown spectroscopy (LIBS) for applications involving valuable samples and elemental mapping. In this study, we introduced a low-power atmospheric pressure plasma jet (APPJ) to reduce sample damage by obtaining LIBS signals at significantly lower laser fluences. The proposed technique, APPJ-assisted LIBS (APPJ-LIBS), utilized an argon APPJ to provide seed electrons and enhance the excitation. The APPJ was generated by a 10 kHz alternating current power supply and made contact with the surface of a brass sample at a 30° angle. An infrared nanosecond Nd:YAG laser was focused onto the contacting zone, allowing the resulting laser-induced plasma to evolve within the surrounding APPJ and produce optical emission. The optimized APPJ-LIBS system reduced the laser fluence threshold for spectral detection of the brass sample by 97 %, from 1.43 J/cm2 to 0.05 J/cm2, which represented the lowest laser fluence threshold reported in LIBS studies on copper-based materials. Micrographs of the sample surface showed no visible damage after the APPJ-LIBS measurement at a near-threshold laser fluence and an APPJ input power as low as 6.0 W. Furthermore, gated images showed the plasma evolution in APPJ-LIBS and confirmed the excitation capability of the APPJ for the laser-ablated materials.

Wind turbine contaminant classification using machine learning techniques

It has been well established in the literature that surface contamination can adversely affect the aerodynamic performance of aerofoils and hence the efficiency with which turbines can convert wind to electrical power. Hence it is critical to ensure that turbine blades are kept as free as possible of contaminants. In this manuscript, we discuss LIBS and machine learning techniques on contaminated wind turbine blades with a view to the possibility of integrating these methods into a standoff laser ablation setup in the future. A LIBS signal will be a key step in the decision process, i.e., to determine if a wind turbine blade has been fouled in the first place. Relatedly one must also determine the point at which the laser has adequately removed contaminants from the current Area of Irradiation (AoI) before moving to the adjacent AoI, i.e., interim end-point-detection. For both steps (presence / absence of fouling) we are investigating laser-induced breakdown spectroscopy (LIBS) to discriminate clean from fouled wind turbine blade samples. In particular we have performed LIBS in both the Vacuum Ultraviolet (VUV) and Ultraviolet Visible (UV–Vis) spectral ranges. Analysis of the spectra showed only slight variations in the constituent materials between clean and contaminated blade samples. In order to address this challenge, the efficacy of a number of machine learning and statistical methods for clean versus contaminated blade classification was investigated. Four methods (Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machines (SVM), Competitive Learning (CL), and Convolutional Neural Networks (CNN)) were evaluated. The spectral regions where machine learning algorithms were applied was determined via a volumetric ellipsoid overlap test based on Principal Component Analysis (PCA). It was found that SVMs provided the most accurate methodology for binary classification of clean vs contaminated blades whilst also yielding the shortest run time.

The effect of ablation crater on geomaterials caused by laser shot accumulation on the laser-induced plasma and shock wave

The stability of laser-induced breakdown spectroscopy (LIBS) signal is vital for its application in geomaterials, which is challenged by spectral fluctuations caused by variable ablation craters. To investigate the influence of the ablation crater caused by laser shot accumulation, the shot-to-shot variation of plasma was analyzed from the aspects of shock wave propagation, ablation crater morphology, plasma morphology, and spectral evolution, which was conducted based on a fiber-optic LIBS (FO-LIBS) system. A pressure sensor was applied to monitor the shock wave signal due to the shock wave propagation characteristics related to the number of shots and types of binding materials. The evolution of the ablation crater profile and depth was tracked, and a linear correlation model between crater depth and shock wave energy was established. It was found that the spectral intensity increases first rapidly and gradually decreases, which can be attributed to the competition between the enhancement of spatial confinement and the plasma cooling phase. Strong self-reversal was observed due to the laser pulse accumulation, with an increase in the plasma expanding distance. This study illustrates the interaction of laser pulses, ablation, and plasma from different aspects, and provides a simple method to track the ablation process.

Evolution and interpretation of LIBS signals based on laser selective layered paint removal from aircraft composite tail

The reliability and controllability of laser selective layered paint removal depend on effective online monitoring technology. In the process of laser cleaning the surface paint layer of the aircraft composite tail, this paper studied the evolution and interpretation of laser induced breakdown spectroscopy (LIBS) signals. The laser power is adjusted to achieve different depths of the laser cleaned composite surface paint layer to compare the Pearson linear correlation between the LIBS online spectrum and the reference spectrum and decode the characteristic peak signal of the LIBS spectrum. The linear correlation of the LIBS spectra and the evolution of the characteristic peaks of the LIBS spectra are mutually verified to establish the intrinsic correlation between the controllability of layered paint removal and the variation of the LIBS spectra, which demonstrates the feasibility of the LIBS technology for online monitoring and feedback of laser paint removal. The results show that the Pearson correlation coefficient r was 0.87–0.89 for the laser cleaning depth range of 44.79–51.50 μm, the layered boundary of laser cleaning the topcoat without damaging the primer is reached, it is proved that the Pearson correlation coefficient r can determine the laser paint removal process. This research compared the different LIBS spectra with cleaning depths at 44.79 and 51.50 μm. It was found that the characteristic peak of strontium element Sr I at wavelength 460.66 nm could characterize the delamination boundary of laser paint removal from aircraft composite tail.

Enhancement of LIBS signal in lithium solution at the interface between liquid and an aluminum substrate

Detecting lithium (Li) in seawater in situ is of great significance for searching deep-sea hydrothermal activities and prospecting oceanic lithium reservoirs. Underwater LIBS (Laser-induced breakdown spectroscopy) has been proven to be an attractive technique for in situ element analysis, but its detecting sensitivity is still hard to satisfy the needs of less rich element analysis. Here, we propose an effective approach to enhance the LIBS signal of Li in bulk water with an immersed metal Al substrate. In this work, a laser beam focused on the substrate's surface with focal offsets between −2.5 mm to 1.5 mm from the surface. A 24 times enhancement factor of the Li signal was achieved with a typical pulse energy of 22 mJ at the optimal focal offset of −0.5 mm. The required minimum pulse energy for 100% breakdown was reduced by 5 times, and the plasma lifetime was 3 times longer, compared with the case without substrate assistance. The quantitative results show that both the LOD and analytical prediction capability of Li can be improved by twice compared to the direct measurement. Moreover, using the background as an internal reference, the average RSD at different concentrations can be further reduced from 45% to 18%. This study not only demonstrates the existence of a spectral enhancement effect at the liquid-solid interface of underwater LIBS, but also reveals an easy-to-implement signal enhancement method for upgrading the development of underwater in situ LIBS sensors.

In-situ and fast classification of origins of Baishao (Radix Paeoniae Alba) slices based on auto-focus laser-induced breakdown spectroscopy.

In this Letter, a rapid origin classification device and method for Baishao (Radix Paeoniae Alba) slices based on auto-focus laser-induced breakdown spectroscopy (LIBS) is proposed. The enhancement of spectral signal intensity and stability through auto-focus was investigated, as were different preprocessing methods, with area normalization (AN) achieving the best results-increasing by 7.74%-but unable to replace the improved spectral signal quality provided by auto-focus. A residual neural network (ResNet) was used as both a classifier and feature extractor, achieving higher classification accuracy than traditional machine learning methods. The effectiveness of auto-focus was elucidated by extracting LIBS features from the last pooling layer output using uniform manifold approximation and projection (UMAP). Our approach demonstrated that auto-focus could efficiently optimize the LIBS signal, providing broad prospects for rapid origin classification of traditional Chinese medicines.

Quantitative elemental mapping of biological tissues by laser-induced breakdown spectroscopy using matrix recognition

The present study demonstrates the importance of converting signal intensity maps of organic tissues collected by laser-induced breakdown spectroscopy (LIBS) to elemental concentration maps and also proposes a methodology based on machine learning for its execution. The proposed methodology employs matrix-matched external calibration supported by a pixel-by-pixel automatic matrix (tissue type) recognition performed by linear discriminant analysis of the spatially resolved LIBS hyperspectral data set. On a swine (porcine) brain sample, we successfully performed this matrix recognition with an accuracy of 98% for the grey and white matter and we converted a LIBS intensity map of a tissue sample to a correct concentration map for the elements Na, K and Mg. Found concentrations in the grey and white matter agreed the element concentrations published in the literature and our reference measurements. Our results revealed that the actual concentration distribution in tissues can be quite different from what is suggested by the LIBS signal intensity map, therefore this conversion is always suggested to be performed if an accurate concentration distribution is to be assessed.

Double-enhanced LIBS system with N2 atmosphere and cylindrical cavity confinement for quantitative analysis of Sr element in soil

To effectively improve the quality of laser-induced plasma spectra and the detection accuracy of laser-induced breakdown spectroscopy (LIBS) simultaneously for Sr element in soil, the dual mechanism of N2 atmosphere combined with cylindrical cavity confinement for laser-induced Sr plasma emission enhancement was proposed and investigated in this paper, and a multi-spectral fusion internal standard analysis model was established. Optimum enhancement effect can be achieved under the conditions of 2 mm diameter and 6 mm height of the confinement cavity in N2 atmosphere, where the enhancement factor was about 3.25, the signal-to-noise ratio reached 710.28, and the LIBS spectrum quality was the best; the relative standard deviation value (2.64%) was the smallest, and the LIBS signal reproducibility was the best. For the samples in this study, the limit of detection of the sample Sr elements under the dual enhancement mechanism was 34.60 mg kg−1, which was 40.43% lower than the limit of detection (LOD) without enhancement mechanism (58.08 mg kg−1), and the R 2 of the multispectral fusion internal standard model was 1.23% higher and the relative error was 3.41% lower than that of the internal standard method. The results showed that the dual enhancement mechanism combining N2 atmosphere and cylindrical cavity confinement improved the spectral quality, signal reproducibility, and detection sensitivity.

Post-fire assessment of heating temperatures experienced by concrete using short video imaging, hyperspectral imaging and laser-induced breakdown spectroscopy

This study develops rapid post-fire analysis methods to predict the heating temperature to which the concrete was exposed. Short video imaging (SVI), hyperspectral imaging (HSI) and laser-induced breakdown spectroscopy (LIBS) were used for the first time to obtain spectra of concrete after high-temperature exposure (100–800 °C). The differences in colour levels and spectroscopic signals due to varying temperatures were observed. To handle the complex relationship between spectra and temperatures, machine learning models were used to extract meaningful information from spectra for the quantification of temperature. Furthermore, domain knowledge related to concrete composition and LIBS signal was integrated into machine learning to improve quantification performance. The highest coefficients of determination for prediction achieved based on SVI, HSI and LIBS measurements were 91.4, 94.8 and 98.6, respectively. These results demonstrate that SVI, HSI and LIBS can be fast and viable methods for assessing the fire temperature that the concrete has experienced.