Laser-induced Breakdown Research Articles

Assessing the feasibility of laser induced breakdown spectroscopy for detecting nitrogen in martian surface sediments

Despite the detection of fixed nitrogen in meteorites and directly on Mars' surface, the abundance and distribution of nitrogen sequestered in the martian crust remains unknown. Given that nitrogen is a bioessential element that is required for the synthesis of amino acids, nucleic acids, and other organic molecules vital for life, this gap in knowledge is one of the most important challenges in constraining martian habitability. Laser-induced breakdown spectroscopy (LIBS) has the capability to detect N in natural rock samples and is available as a stand-off survey instrument on multiple currently active Mars rovers, creating an immediate opportunity to map the stratigraphic distribution of N within diverse depositional settings. However, little has been published regarding the detection of N with LIBS.To lay a foundation for N detection on Mars using LIBS, we synthesized a comprehensive suite of samples with variable amounts of nitrogen (as nitrate or ammonium) in either a Mars regolith simulant or a clay matrix. We present baseline spectra of N emission in Mars-relevant matrices and identify spectral interferences. Our results indicate that 17 diagnostic N emission lines are reliably detectable from mineral-bound N against a basaltic background, but only four lines exhibit sufficient sensitivity to be detected across a range of N concentrations and within all tested matrices. To elucidate optimized strategies for quantification, we present an iterative series of PLS models. We find that prediction accuracy is improved by restricting the compositional range of the training set, normalizing the data, subtracting baseline continuum emission, and simultaneously modeling the emission behaviour of multiple diagnostic N lines at once. We observe that the prediction uncertainty increases (worsens) from 8.4% to 29.9% if models are used to predict N in samples with a dissimilar matrix than those used during training, suggesting poor generalizability outside the training range. Consequently, future work should focus on developing a larger, more diverse training set that encompasses the range of N concentrations and phases expected to be encountered on Mars, which may be used to train generalizable models. Overall, this work demonstrates that LIBS is a promising tool for determining the abundance of N sequestered in martian surface materials and lays a foundation for future development.

Analysis of microscopy techniques to measure segregation in continuous-cast steel slabs.

The accurate characterisation of centreline segregation requires precise measurements of composition variations over large length scales (10 ) across the centreline of the cast product, while having high resolution, sufficient to quantify the significant composition variations between dendrites due to microsegregation at very small length scales (10 ). This study investigates the potential of a novel microscopy technique, named Synchrotron Micro X-ray Flurorescence (SMXRF), to generate large-scale high-resolution segregation maps from a steel sample taken from a thin slab caster. Two methods, Point Analysis and Regression Analysis, are proposed for SMXRF data calibration. By comparing with the traditional Laser-Induced Breakdown Spectroscopy (LIBS), and Electron Probe Micro Analyser (EPMA) techniques, we show that SMXRF is successful in quantitative characterisation of centreline segregation. Over large areas (e.g. 12 16 ) and at high resolution (10-50 pixel size) various techniques yield comparable outcomes in terms of composition maps and solute profiles. The findings also highlight the importance of both high spatial resolution and large field of view to have a quantitative, accurate, and efficient measurement tool to investigate segregationphenomena.

Laser-induced breakdown spectroscopy (LIBS) of animal fat (lard): Efficient sample preparation for onsite analysis and influence of sample temperature on the signal intensity and plasma parameters

We report an efficient sample preparation method (freezing) for onsite fat and meat analysis via a specially designed thermoelectric cooling and temperature-controlling system. This investigation also focused on the effect of phase change on the sensitivity and reproducibility of LIBS emission signals and plasma parameters. The plasma emissions of animal fats (lard) were recorded when the sample was frozen (−2 °C), fluid (15 °C), and in a liquid state (37 °C) with a thermoelectric cooling system. At each temperature, the plasma emissions were acquired at laser pulse energy from 50 to 300 mJ and detector gate delay (DGD) from 0.5 to 5 μs. With increasing sample temperature, the DGD, where the optical emission intensity reached a maximum, decreased. At a laser pulse energy of 200 mJ and a sample temperature of −2 °C, the emission signals increased fourfold, the signal-to-noise ratio (SNR) improved tenfold, and the self-absorption in the emission lines decreased significantly. The repeatability of the emission signals and plasma parameters of frozen and liquid fat samples was determined using the relative standard deviation (RSD) of Se I (473.08 nm) and K I (766.48 nm) emission lines. The RSDs of the emission signals improved from 40 to 18 % and 37 to 16 %, whereas the shot-to-shot RSDs of the electron temperature and electron number density get improved from 11 to 6 % and 12 to 6.8 %, respectively.

Determination of La in rare earth ores using laser-induced breakdown spectroscopy combined with bidirectional long short-term memory

Determination of La in rare earth ores using laser-induced breakdown spectroscopy combined with bidirectional long short-term memory

Confocal controlled laser-induced breakdown spectroscopy for quantitative detection of cadmium in soil

Soil, one of the most precious natural resources on Earth, gradually accumulates heavy metals, inevitably causing significant damage to the ecological environment. Here, we introduce confocal controlled laser induced breakdown spectroscopy (CCLIBS) technology for the quantitative analysis of the heavy metal cadmium in soil for the first time. CCLIBS offers better spatial consistency and stable plasma temperature during sample ablation compared to traditional LIBS, thereby reducing matrix effects to improve the accuracy of the quantitative results. The fluctuation of the spectrum and limit of detection are reduced by 0.6 times and 0.39 times, respectively. An effective prediction model was established using the partial least squares method, with a determination coefficient increased to 0.96. The root mean square error of prediction and average relative error are reduced to 67.67 and 0.20, respectively. These results indicate that CCLIBS provides consistent ablation conditions for elemental quantification and yields reliable test results, which is significant for monitoring heavy metals in the ecological environment and effectively intervene and mitigate environmental contamination.

A theoretical study of the signal enhancement mechanism of coaxial DP-LIBS

In the field of dual-pulse laser-induced breakdown spectroscopy (DP-LIBS) research, the pursuit of methods for determining pulse intervals and other parameters quickly and conveniently in order to achieve optimal spectral signal enhancement is paramount. To aid researchers in identification of optimal signal enhancement conditions and more accurate interpretation of the underlying signal enhancement mechanisms, theoretical simulations of the spatiotemporal processes of coaxial DP-LIBS-induced plasma have been established in this work. Using a model based on laser ablation and two-dimensional axisymmetric fluid dynamics, plasma evolutions during aluminum–magnesium alloy laser ablation under single-pulse and coaxial dual-pulse excitations have been simulated. The influences of factors, such as delay time, laser fluence, plasma temperature, and particle number density, on the DP-LIBS spectral signals are investigated. Under pulse intervals ranging from 50 to 1500 ns, the time evolutions of spectral line intensity, dual-pulse emission enhancement relative to the single-pulse results, laser irradiance, spatial distribution of plasma temperature and species number density, as well as laser irradiance shielded by plasma have been obtained. The study indicates that the main reason behind the radiation signal enhancement in coaxial DP-LIBS-induced plasma is attributed to the increased species number density and plasma temperature caused by the second laser, and it is inferred that the shielding effect of the plasma mainly occurs in the boundary layer of the stagnation point flow over the target surface. This research provides a theoretical basis for experimental research, parameter optimization, and signal enhancement tracing in DP-LIBS.

Inhibiting Effect of Topological Charge on Laser-Induced Breakdown of Gases: Miniaturization of an SRS Laser with Vortex Raman Outputs

Inhibiting Effect of Topological Charge on Laser-Induced Breakdown of Gases: Miniaturization of an SRS Laser with Vortex Raman Outputs

Examining the role of magnetic fields in plasma behavior and surface evolution of a Mg alloy with varied irradiances in a femtosecond laser treatment

This paper reports the effect of a magnetic field on plasma parameters and surface structuring of the Mg alloy after laser irradiation. Femtosecond pulses of a Ti:sapphire laser system (800 nm, 35 fs, 1 KHz) are employed as the source of irradiation at various irradiances ranging from 0.011PW/cm2 to 0.117PW/cm2 to generate ablated Mg-alloy plasma. A transvers magnetic field (TMF) of strength 1.1 Tesla is employed to confine laser generated Mg plasma. All the measurements are performed with and without TMF. The two plasma parameters, i.e., excitation temperature (Texc) and electron number density (n e ) of Mg plasma, have been evaluated by laser-induced breakdown spectroscopy (LIBS) analysis. It is observed that the values of Texc and n e of laser produced plasma (LPP) of the Mg alloy are higher in the presence of a magnetic field as compared to the field free case. Both show initially an increasing trend with increasing laser irradiance and after attaining their respective maxima a decreasing trend is observed with the further increase of irradiance. The magnetic confinement validity is confirmed by analytically evaluating thermal beta (β t ), directional beta (β d ), confinement radius (R b ), and diffusion time (t d ) for LPP of the Mg alloy. To correlate the LPP parameters of the Mg alloy with surface modifications a field emission scanning electron microscope (FE-SEM) analysis is performed. It was revealed that structures like laser-induced periodic surface structures (LIPSSs), agglomerates, islands, large sized bumps, along with channels and multiple ablative layers are observed. Distinct and well-defined surface structuring is observed in the presence of TMF as compared to the field free case. It is concluded that by applying an external magnetic field during laser irradiation, controlled material surface structuring is possible for fabrication of nanogratings and field emitters where spatial uniformity is critically important.

Efficient and non-destructive classification of lily bulbs by laser-induced breakdown spectroscopy combined with machine learning methods

The study presented here is a pioneering attempt to utilize Laser-Induced Breakdown Spectroscopy (LIBS) for the non-destructive testing and classification of lily bulbs. Given the complexities and time-consuming nature of traditional classification methods based on organic matter content detection, the gradual adoption of spectral technologies as an alternative tool is timely. In this research, the Isolation Forest algorithm was employed to eliminate outliers, thereby enhancing data quality. Various variable selection methods combined with five different classification algorithms were used to establish models. Both full spectrum and feature band variable classification models achieved classification accuracy rates exceeding 99% on the prediction set. Among these, the Logistic Regression (LR) model demonstrated the most exceptional performance, with classification accuracy rates of 100% in both cross-validation and prediction sets. Through the analysis of feature variable importance ranking and spectral line elements, elements such as Na, K, Fe, Ca, P, Mg, and N were identified as potentially crucial for the identification of the origin of lily bulbs. These findings demonstrate the effectiveness of using LIBS combined with machine learning to classify lily bulbs, and provide a theoretical reference for origin identification and variety recognition of other medicinal and edible foods.

From sensor fusion to knowledge distillation in collaborative LIBS and hyperspectral imaging for mineral identification

Multimodal spectral imaging offers a unique approach to the enhancement of the analytical capabilities of standalone spectroscopy techniques by combining information gathered from distinct sources. In this manuscript, we explore such opportunities by focusing on two well-known spectral imaging techniques, namely laser-induced breakdown spectroscopy, and hyperspectral imaging, and explore the opportunities of collaborative sensing for a case study involving mineral identification. In specific, the work builds upon two distinct approaches: a traditional sensor fusion, where we strive to increase the information gathered by including information from the two modalities; and a knowledge distillation approach, where the Laser Induced Breakdown spectroscopy is used as an autonomous supervisor for hyperspectral imaging. Our results show the potential of both approaches in enhancing the performance over a single modality sensing system, highlighting, in particular, the advantages of the knowledge distillation framework in maximizing the potential benefits of using multiple techniques to build more interpretable models and paving for industrial applications.

Immediate traceability of ash based on laser-induced breakdown spectroscopy and machine learning

This article reports on an advanced level physical laboratory experiment designed for college level undergraduate education and for scholars who need specialized training on using and interpreting Laser-Induced Breakdown Spectroscopy. The technical principle, experimental operation and sample preparation of Laser-Induced Breakdown Spectroscopy are introduced in detail. A presentation and discussion of the use of Laser-Induced Breakdown Spectroscopy in the traceability of four common samples is emphasized. Combining Laser-Induced Breakdown Spectroscopy with Machine Learning, two distinct datasets are constructed through the extraction of spectral features. Dimensionality reduction of spectral data is performed using Linear Discriminant Analysis, while the Random Forest model is employed for provenance classification. Finally, the interpretability of the Random Forest model is leveraged to explore the contributions of different spectral elements to provenance tracing. Results demonstrated the system’s effectiveness in not only accurately identifying ash types but also in elucidating the influential chemical components, offering significant implications for material analysis and environmental monitoring. On an educational standpoint, this paper will allow any reader, in particular, undergraduate and graduate students, to gain a better understanding of the theory and practice of Laser-Induced Breakdown Spectroscopy and machine learning.

Cellulose-based homogeneous method for suppressing coffee ring effect of cadmium detection in water using laser-induced breakdown spectroscopy

Heavy metal pollution has a negative impact on water resources and poses a threat to crop safety and human health through biogeochemical cycles. To detect heavy metals in water, the dried droplet method (DDM) combined with laser-induced breakdown spectroscopy (LIBS) is commonly used. However, the uneven distribution of heavy metals caused by the coffee ring effect (CRE) can adversely affect the intensity and stability of the detection signal. In this study, the cellulose-based homogeneous method (CHM) was proposed to suppress CRE, ensure a uniform distribution of heavy metal (represented by Cadmium (Cd) in experiments) on the liquid-solid conversion deposition layer, and enhance the detection signal. A rapidly evaluated approach was developed using LIBS to visualize the homogeneity of Cd distribution on the deposition layer, which could also determine the type and concentration of cellulose. Furthermore, a comprehensive investigation of the mechanism was conducted from the perspective of fluid dynamics. The use of CHM resulted in a 94.81 % increase in signal average intensity and reduced the relative standard deviation (RSD) to 0.13 times. The R2 value achieved 0.997, and the limit of detection (LOD) reached 0.029 mg/L. CHM was validated using natural lake water samples spiked with a standard Cd solution resulting in a recovery rate ranging from 105.3 % to 109.06 %. The proposed method exhibits great potential to detect heavy metals in water resources, industrial wastewater, and agricultural water to assess water quality and water security.

Measurement of transition probabilities of La II using Laser Induced Breakdown Spectroscopy (LIBS)

Atomic data, such as transition probabilities of heavy elements, holds great importance in astrophysical studies. The analysis of optical and infrared IR emissions, specifically kilonovae following neutron star mergers, requires transition probabilities of the bound–bound transitions for the heavy elements involved. According to previous studies, heavy metals, particularly lanthanides such as La and Ce, contribute to the properties of the emission. However, the current databases, such as the Atomic Spectra Database at the National Institute of Standards and Technology, are incomplete, particularly for heavy elements. Therefore, we evaluate and measure the transition probabilities of lanthanide elements using laser-induced breakdown spectroscopy, which is an atomic emission spectroscopic technique based on optical emission from a laser-generated plasma. In this study, we present the transition probabilities for La II lines. The experimentally obtained transition probabilities are compared with existing data.

Blank sample denoising algorithm (BSDA): An effective spectral noise reduction in water sample LIBS detection

Laser-induced breakdown spectroscopy (LIBS), as an elemental composition analysis technique, has many unique advantages and great potential for applications in water detection. However, the quality of LIBS spectral signals, such as signal-to-noise ratio and stability, is often poor due to the matrix effects of water, limiting its practical performance. To effectively remove the inherent weak radiation in experimental spectral data that can be easily mistaken for noise, this paper proposes a denoising algorithm for processing spectral data using a self-built blank sample spectral database of deionized water samples, and designs a complete data processing workflow. It includes steps such as blank sample data screening, internal standard correction, blank sample correction, and spectral smoothing. Against the backdrop of marine applications, experimental spectral data for target elements Na, Mg, Ca, K, Sr, and Li were processed with this algorithm. The results show that after algorithm processing, the spectral quality was significantly improved, with the signal-to-noise ratio and detection limits of various elements improved by at least one order of magnitude. The signal-for Li increased by up to 36 times, and the detection limit for K decreased by up to 25.2 times. Additionally, tiny spectral peaks that could not be observable in the original spectral data could be effectively extracted after processing. From a technical implementation perspective, the database establishment and data process are simple and practical, with universal applicability. Therefore, this method has good potential and wide foregrounds in many other water sample LIBS detection technologies.

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.

Element recognition of laser-induced breakdown spectroscopy by comparing vectors of peak quantities

Element recognition of laser-induced breakdown spectroscopy (LIBS) can be achieved by analyzing the corresponding peaks of plasma emissions. However, it is still challenging to make quick judgments due to database disagreements, such as wavelength or line-ratio. This study presents a simple method to conduct element recognition based on comparing vectors established by the quantities of spectral peaks, which allows an accurate recognition of the principle elements regardless of wavelength shifts. By examining deep-sea samples, this method demonstrates the recognition effectiveness for rare earth elements (REEs) with an accuracy over 80%. The developed method proves to be practical in LIBS qualitative analysis, and it can serve as an effective complement to existing approaches for element identification.

Correction of self-absorption effect in laser-induced breakdown spectroscopy based on temperature iteration

Laser-induced breakdown spectroscopy (LIBS) is an ideal real-time on-line method for the detection of minor elements in alloy steel, but it needs to solve the undesired self-absorption effect in plasma. Since the plasma electron temperature (T), radiation particle number density and absorption path length (Nl) determine the degree of self-absorption and affect the corrected spectral line intensity, a new temperature iterative self-absorption correction method based on the plasma thermal equilibrium radiation model is proposed to continuously calculate and correct these two parameters. Compared with generally applied self-absorption correction methods, this method has obvious advantages of simple programming, high computational efficiency, and independence of the availability or accuracy of Stark broadening coefficients. The quantitative analysis results of Cu show that the linearity of calibration curves and the measurement accuracy of elemental content are significantly improved with the self-absorption correction. In addition, this method can directly obtain the accurate radiation particle number density and absorption path length, which benefits the plasma diagnostics and quantitative analysis.

Sensitive Detection of Silicon in Aqua Phase by Microwave-Assisted Laser-Induced Breakdown Spectroscopy

Sensitive Detection of Silicon in Aqua Phase by Microwave-Assisted Laser-Induced Breakdown Spectroscopy

Laser-Induced Breakdown Spectroscopy for composition monitoring during directed energy deposition of graded Fe-Ni alloys

Laser-Induced Breakdown Spectroscopy for composition monitoring during directed energy deposition of graded Fe-Ni alloys

Impacts of Heavy Metal Pollution on Agricultural Production and Response Strategies

Agriculture is one of human beings’ most essential productive activities, and it is closely linked to human sustenance. Due to the ignorance of environmental production, production waste treatment in industries, and the lack of government regulation, the heavy metal pollution problem in agriculture has gradually become an important problem that needs to be solved urgently. Heavy metal pollution in agricultural land is a widespread problem that can contaminate downstream water systems, reduce crop yields, and threaten human safety. This paper examines the main sources of heavy metal pollutants and their impacts on agriculture and proposes related treatment measures for the public. Firstly, in part, by analyzing the sources of heavy metal pollutants, researchers find out that the pollutants are usually categorized into factory emissions, municipal waste pollution, and the use of pesticides. Secondly, the paper focuses on illustrating the impacts on agriculture. Mentioning the destruction of crop growth and health hazards posed on humans. Finally, the treatment measure is divided into the aspects of present technologies (including Laser-induced breakdown spectroscopy (LIBS) and Phytoremediation), treatments for governments, and measures for farmers. In the future, to ensure people’s health, human beings should put more effort into solving heavy metal pollution, caring for agricultural safety, and protecting the environment. The paper will contribute to recalling society’s awareness of environmental protection, offering new aspects of viewing heavy metal pollution, and providing practical methods of solving the agricultural pollution problem.