Laser-induced Breakdown Spectroscopy Research Articles

Application of Laser-Induced Breakdown Spectroscopy to quantify the changes in plasma characteristics in calcite-precipitated soils

The changes in calcium carbonate precipitation of modified soil by Enzyme Induced Calcite Precipitation (EICP) were investigated using Laser-Induced Breakdown Spectroscopy (LIBS). This experiment enabled elemental composition analysis of the soil samples. The plasma electron density and temperature were also extracted from the collected spectra of distinct soil samples i.e., W1 and W2 treated with EICP. The type of the crystal of CaCO3 deposited during the treatment process was investigated using scanning electron microscopy (SEM) and X-ray diffraction (XRD) tests. This research was conducted by utilizing an Nd: YAG laser with a wavelength of 532 nm, a pulse duration of 6 ns, and an energy of 25 mJ, from where the plasma temperature and electron density of modified soil throughout several treatment cycles were analyzed. The spectra were collected from different locations within the sample and the spectral range considered for the study was 370 nm-800 nm. The plasma electron density and plasma temperature determined using LIBS spectral data increase with the increase in the treatment cycle. With the increase in the treatment cycle, the deposition of CaCO3 was found to increase and correspondingly plasma temperature increased by 220 % for soil W1 and 117.3 % for soil W2. A similar rise in plasma electron density was noticed at the highest cycle of the treatment process; plasma electron density is augmented by 106.4 % for W1 and 21.50 % for W2. This investigation of the LIBS spectral data information indicates a positive correlation between the number of treatment cycles and the plasma temperature, meaning that as the treatment cycles rise, the plasma temperature also increases indicating a larger deposition of CaCO3. The findings are valuable in the domain of geotechnical engineering, where lasers are extensively employed for the examination of the soil profile and can prove to be a useful method for the characterization of the modified soils.

Correlation of Plasma Temperature in Laser-Induced Breakdown Spectroscopy with the Hydrophobic Properties of Silicone Rubber Insulators

Correlation of Plasma Temperature in Laser-Induced Breakdown Spectroscopy with the Hydrophobic Properties of Silicone Rubber Insulators

Analytical Techniques for Detecting Rare Earth Elements in Geological Ores: Laser-Induced Breakdown Spectroscopy (LIBS), MFA-LIBS, Thermal LIBS, Laser Ablation Time-of-Flight Mass Spectrometry, Energy-Dispersive X-ray Spectroscopy, Energy-Dispersive X-ray Fluorescence Spectrometer, and Inductively Coupled Plasma Optical Emission Spectroscopy

Rare earth elements (REEs) hold significant industrial, scientific, and modern technological worth. This study focused on detecting and quantifying REEs in various geological ore samples. These samples were collected from different REE-bearing locations recommended by geological experts. The analysis was conducted using laser-induced breakdown spectroscopy (LIBS) and laser ablation time-of-flight mass spectrometry (LA-TOF-MS). In this work, LIBS methodology was employed using three different configurations: standard LIBS, LIBS with an applied magnetic field, and LIBS with both an applied magnetic field and target sample heating within an optimal temperature range. Elements from the REE group, specifically lanthanum (La), cerium (Ce), and neodymium (Nd), were identified and quantified. To detect, quantify, and validate the results from LIBS and LA-TOF-MS, we utilized an array of analytical techniques—Energy-Dispersive X-ray Spectroscopy (EDX), Energy-Dispersive X-ray Fluorescence Spectrometer (ED-XRF), and Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Interestingly, the quantitative results for REEs (La, Ce, and Nd) in the ore samples obtained using the LIBS technique with various configurations were found to be in agreement with those from LA-TOF-MS, EDX, XRF, and ICP-OES. In addition, LIBS enables detailed microchemical imaging, allowing the map of the spatial distribution of elements within the mineral–ore matrix. The high-resolution microscale elemental mapping of REEs was accomplished using the emission lines Ce (II) at 446.0 nm, La (II) at 492.1 nm, and Nd (II) at 388.8 nm. By integrating multiple analytical techniques, our study enabled the construction of a complete elemental distribution map, providing new insights into the geochemical processes and mineral composition of rare earth ores, while advancing geochemistry and contributing valuable data for rare earth resource exploration.

Helium retention feature in the boron deposited layer on tungsten substrate by laser-induced breakdown spectroscopy and machine learning approach

ITER is designed for a burning plasma operation in which Tungsten (W) tiles are used as the first wall and diverter materials. Studying He dynamics in B-coated W wall is essential to understanding the effect of boronized plasma-facing components in fusion reactors, as these post-conditioning materials significantly influence helium retention and release. Plasma-wall interactions (PWI) are an important issue in ITER fusion reactors. PWI would lead to wall erosion and impurity redeposition. As a product of the D-T burning plasma, helium (He) ash would be retained or co-deposited on plasma-facing components (PFCs), affecting the stable operation of burning plasma. Laser-induced breakdown spectroscopy (LIBS) is proposed as a promising in-situ diagnostic approach for monitoring D/T and He retention and impurity deposition on PFCs of tokamak devices. In this study, the LIBS technique was used to investigate the helium retention feature in the boron (B) layer on tungsten substrate in 10−5 mbar. Five He-retention samples on the boron deposition layer on tungsten substrates were prepared by pulsed laser deposition (PLD) method at different ambient pressures in our lab. The investigations indicate that the atomic spectral line of helium (He-I 587.56 nm) was observed in the spectra of the first three laser shots. The depth profiles of He, B, and W in the boron-deposited layer on tungsten substrate were performed by LIBS to determine the co-deposition layer thickness. The concentration of He in the co-deposition layer samples measured by TDS is 7 × 1020 He/m2. The plasma parameters, such as plasma electron temperature and electron number density, were calculated to validate the local thermodynamic equilibrium. Machine learning (ML) algorithms are used to classify the co-deposits and substrates. The first three principal components (PC1, PC2 & PC3) of the unsupervised ML algorithm (PCA) give a classification accuracy of 91.7 %. The supervised ML algorithm neural network achieved training and testing accuracy of 100 % and 96.7 %, respectively.

A combination of XGBoost and neural network in LIBS spectrum processing for precise determination of critical elements in 620 iron ore samples of various origins

China is the worldwide largest importer of iron ores for supplying its iron and steel industries. The necessary inspections of iron ores not only concern total iron content but also involve minor elements, such as silicium, aluminum, magnesium, and calcium. Laser-induced breakdown spectroscopy (LIBS) promises in situ and on-site detection and analysis capabilities, which is required for efficient, reliable, and large-scale impartation inspections. Such opportunity represents at the same time, a challenge for the technique in terms of the quantitative analysis ability. A fundamental issue corresponds to the matrix effect in LIBS analysis due to a large diversity of iron ores in terms of mineralogical and chemical compositions. Demonstrated as effective to deal with the matrix effect in LIBS analyses of geological materials, the machine learning approach needs to be implemented within an optimized data processing pipeline, combining algorithms carefully chosen and tested. In this work, we developed a sequentially connected combination of an extreme gradient boosting (XGBoost) model and a back propagation neural network (BPNN) model, to effectively fit the functional relation between the concentrations and the spectra. The specificity of the approach consists in fitting the linear, low-order nonlinear, and high-order nonlinear terms in a sequential way to substantially improve the model prediction performances. The method has been developed and tested with a significant set of 620 collected iron ore samples of eight production countries and 35 different commercial brands, with a wide range of matrices in terms of mineralogical and chemical compositions. The samples underwent prior characterization using conventional laboratory analysis methods to establish the reference elemental concentrations. The results from this necessary step of laboratory investigation, with root mean square error for prediction (RMSEP) of 0.407 wt%, 0.372 wt%, 0.085 wt%, 0.131 wt%, and 0.114 wt%, respectively for total iron (TFe), SiO2, Al2O3, MgO, and CaO, show the potential for on-site inspections.

Manganese diluted TlInS2 layered semiconductor: Optical, electronic and magnetic properties

Manganese–doped TlInS2 (TlInS2+Mn) layered semiconductors with different doping concentrations of about 0.1 and 0.3 at. percent were investigated. Photo–induced current transient spectroscopy (PICTS) measurements in the temperature range of 80 and 300 K have been made for identifying energy levels related to Mn dopant within the electronic bandgap of TlInS2+Mn crystal. Optical absorption spectra in the photon energy range of ∼1.8–3.1 eV have been extracted by fitting the transmittance spectra of TlInS2+Mn recorded at the temperatures varied from ∼40–300 K. A redshift trend of the absorption edge with increasing of temperature was observed. On the other hand, a blue shift in the absorption edge with Mn doping was observed in the absorption spectra. The Mn dopant induced photoluminescence (PL) was also investigated in the visible region ranging from 320 to 960 nm. Seven peaks were observed in the PL spectra of Mn doped TlInS2 crystal in the regions both ∼960–620 nm (∼1.3–2 eV) and ∼620–320 nm (∼2–3.8 eV) due to the electronic states of Mn ions. Laser–induced breakdown spectroscopy (LIBS) technique was applied to establish of the elemental chemical composition and to confirm presence of each constituent element in TlInS2:Mn. The dc magnetization measurements of TlInS2+Mn performed in a wide temperature range of ∼5 and ∼300 K revealed different (diamagnetic and paramagnetic states) magnetic phases inside the studied temperature range. The X–band electron paramagnetic resonance (ESR) experiments were carried out in the low–temperature phase of TlInS2+Mn to probe the structure of Mn centers in TlInS2. The measurements revealed that the ESR spectrum changes drastically in the low–temperature phase of TlInS2+Mn bulk sample. Finally, a detailed density functional theory (DFT) based computational investigation of electronic band structures, density of states and optical properties of TlInS2+Mn compound has been performed. The results of ab–initio calculations are discussed for three possible states when the manganese atom was doped by replacing either the Tl, In or S atoms in the unit cell of TlInS2. Additionally, the effect of interstitial Mn dopant on the electronic structure and optical properties of TlInS2 material has been simulated.

Surface matters: Decarburising wootz crucible steel ingots

Wootz, the Indian crucible steel, is a hypereutectoid iron–carbon alloy and famous for its outstanding qualities. Due to the paucity of archaeological and historical ingot finds and conservative sampling strategies, discussions of the homogeneity of such ingots and the microstructural representativeness of samples have remained generic and assumptive. Thus two major shortcomings in the study of crucible steel ingots include the determination of their absolute carbon content and its relative distribution across the ingots. The recent discovery of a large hoard of wootz ingots from Telangana (Jaikishan et al. 2021) offered a unique opportunity to study their microstructure and determine their carbon content.Reports based on traditional metallography suggest a wide carbon range, from 1 to 2 wt% carbon, for similar ingots (Scott 2013). Recent work based on image analysis (Desai and Rehren 2023) offered narrower carbon estimates (about 1.8 wt%) for several of the recently discovered ingots, with some variation in concentration towards the edge of the samples. As a collaborative effort to determine absolute carbon values and potential uneven distribution of the carbon in the Telangana ingots, traditional metallography was coupled with laser-induced breakdown spectroscopy (LIBS). Beyond documenting the microstructure across several ingots, the study provides macrostructural evidence of rim decarburisation, which we believe to be intentional. This study presents the micro- and macrostructure of two of the hypereutectoid Telangana ingots, highlighting the skill of the craftsmen in decarburising the outer surfaces of their ingots, potentially for ease of subsequent forging.

Large-Eddy Simulation of Supersonic Combustion in a Mach 2 Cavity Model Scramjet Combustor

In this study, we report on reactive large-eddy simulations (LESs) of flow and combustion in the U.S. Air Force Research Laboratory Research Cell 19 cavity-based scramjet combustor. This case involves the combustion of ethylene that is injected into a cavity. It has previously been studied experimentally with multiple techniques, including particle image velocimetry, hyperspectral imagining, and laser-induced breakdown spectroscopy, as well as numerically using hybrid Reynolds-averaged Navier–Stokes/LES. Here, we build on the existing knowledge and provide an in-depth investigation of the flow and combustion using a pure LES approach. A range of fuel injection rates are studied, as well as the nonreacting case. This way we can disseminate how different amounts of combustion and the associated volumetric expansion affect the shear layer above the cavity, the recirculation zone, and the downstream shock train. For example, an additional shock train emerges from the start of the cavity when combustion is present, and it grows in intensity with increasing fueling rate from 50 to 110 slpm. For the cases studied, good agreement is found between the LES and experiments. The primary flow features, such as the shock train, primary and secondary cavity recirculation regions, and shear-layer lift due to exothermicity, are found to evolve similarly with increasing fueling rate in the experiments and the LES. This enables the use of the more complete LES results to further investigate the flow and combustion physics.

Rapid quantitative analysis of three elements (Al, Mg and Fe) in molten zinc based on laser-induced breakdown spectroscopy combined with machine learning algorithm

Hot-dip galvanizing represents one of the most cost-effective methods for the prevention of metal corrosion, and is therefore employed extensively across a range of fields. Carrying out the research and development of technology and devices for quantitative analysis of chemical elements in hot-dip galvanising process can provide theoretical basis and technical support for the efficiency of hot-dip galvanising process and reduction of energy consumption. A machine learning-assisted LIBS combined with a programmable logic controller (PLC) for simultaneous on-line/in-site monitoring of multiple elements in hot-dip galvanising solution (molten zinc) was developed in the current study. The LIBS spectral data of the on-site hot-dip galvanising solution was collected under optimised experimental conditions. In order to further reduce the influence of experimental noise on the analysis performance, the on-site LIBS spectral data were preprocessed and anomalous spectral data were screened based on normalisation and principal component analysis-mahalanobis distance (PCA-MD). On the basis of the optimised data, component prediction models for three key elements of on-site hot-dip galvanising solution were constructed. The storage and re-call of the model was achieved based on python combined with LabVIEW, thus real-time prediction of the on-site component content of hot-dip galvanising solution was achieved. The results show that the random forest model presents the best prediction results, in which the R2 is 0.9978 and the RMSE is 0.0013% for Al, the R2 is 0.9984 and the RMSE is 0.0011% for Mg, and the R2 is 0.9932 and the RMSE is 0.0001% for Fe. From the on-site analysis results of the constructed model, its MRE of Al, Mg and Fe is 0.0098, 0.0236, and 0.2102, respectively. In summary, the in-situ/on-line analysis system of hot-dip galvanising solution combined with machine learning constructed in this study shows excellent performance, which can satisfy the needs of hot-dip galvanising solution production site. This study is expected to provide theoretical basis and technical reference for quality control and process optimisation in other production sites in the metallurgical field.

Standoff Identification of Plastic Waste Using a Low-Cost Compact Laser-Induced Breakdown Spectroscopy (LIBS) Detection System.

We report the standoff/remote identification of post-consumer plastic waste by utilizing a low-cost and compact standoff laser-induced breakdown spectroscopy (ST-LIBS) detection system. A single plano-convex lens is used for collecting the optical emissions from the plasma at a standoff distance of 6.5 m. A compact non-gated Czerny-Turner charge-coupled device (CCD) spectrometer (CT-CCD) is utilized to analyze the optical response. The single lens and CT-CCD combination not only reduces the cost of the detection system by tenfold, but also decreases the collection system size and weight compared to heavy telescopic-based intensified CCD systems. All the samples investigated in this study were collected from a local recycling plant. All the measurements were performed with only a single laser shot which enables rapid identification while probing a large number of samples in real time. Furthermore, principal component analysis has shown excellent separation among the samples and an artificial neural network analysis has revealed that plastic waste can be identified within ∼10 ms only (testing time) with accuracies up to ∼99%. Finally, these results have the potential to build a compact and low-cost ST-LIBS detection system for the rapid identification of plastic waste for real-time waste management applications.

Correlation between the Mechanical Properties and Laser-Induced Breakdown Spectroscopy for Human Teeth

The present study investigates the correlation between mechanical properties and laser-induced breakdown spectroscopy (LIBS) for different tooth samples. A set of Fourteen samples collected from the Al-Kut Specialized Dental Centre in Wasit Governorate was analysed. Tooth samples were divided into groups depending on age and gender (men, women). The optical emission of the ionic-to- atomic intensity ratio of the Ca spectral line has been correlated with Leeb hardness test (LHT) values, compressive strength, and Young's modulus measured by standard tools. The ratio ionic line to atomic line CaII / CaI between the ionic calcium line at 396.84 nm and the atomic line at 558.87 nm for teeth is obtained using LIBS technique employing Nd: YAG laser with a wavelength of 1064 nm, an energy of 200 Mj, and a pulse duration of 10 ns. A linear relationship was observed between compressive strength, Young's modulus and hardness, and the concentrations of the elements (Ca). The small relative errors for hardness, compressive strength, and Young's modulus ranged from (1.44-5.57) %, (1.94-6.07) %, and (0.243-4.01) %, respectively. These findings validate applying LIBS to check out some important mechanical properties.

Characterization of Traditional Pottery Artifacts from Yucatán Peninsula, México: Implications for Manufacturing Process Based on Elemental Analyses

The present work is focused on developing and implementing a minimally invasive methodology for material characterization of traditional pottery from Yucatan, México. The developed methodology, which combines elemental (X-ray fluorescence spectroscopy (XRF), inductively coupled plasma-optical emission spectroscopy (ICP-OES), and Laser-Induced Breakdown Spectroscopy (LIBS)) and molecular (fiber optic reflectance spectroscopy (FORS)) spectroscopic analytical techniques, allowed for the characterization of contemporary pottery objects manufactured following traditional recipes in the town of Uayma, Yucatán, México and raw materials associated with the pottery manufacturing process. The results allowed us to detect and estimate the number of selected elements and helped to infer the presence of complex materials such as iron oxides, aluminosilicates, and calcium carbonate. Additionally, the analysis indicated two pottery groups separated by their elemental and molecular composition, corresponding to the sources of raw materials employed by the potters. It confirmed the absence of toxic compounds in ceramic objects, a significant concern for potters, as some objects are intended for domestic use. The research findings provide reassurance about the safety of these products.

Study on the Impact of Air Pressure on the Laser-Induced Breakdown Spectroscopy of Intumescent Fireproof Coatings

Intumescent fireproof coatings protect steel structures and cables by forming a thick, fire-resistant layer under high temperatures. These coatings can deteriorate over time, impacting their fire resistance. Current testing methods are largely lab-based, lacking in-service evaluation platforms. Laser-Induced Breakdown Spectroscopy (LIBS) is emerging as a promising in situ detection technology but is influenced by low air pressure in high-altitude areas. This study investigates how air pressure affects LIBS signals in intumescent coatings on galvanized steel. Using pressures between 35 and 101 kPa, a linear model was developed to correlate laser pulses to ablation depth for characterizing coating thickness. Results show that spectral intensity decreases with lower air pressure. However, a strong linear relationship persists between laser pulses and ablation depth, with a fitting accuracy above 0.9. The coating thickness is identified by the number of laser pulses required to detect the Zn spectral line from the underlying galvanized steel. As air pressure decreases, the ablation depth increases. The study effectively models and corrects for air pressure effects on LIBS data, enabling its application for field detection of fireproof coatings. This advancement enhances the reliability of LIBS technology in assessing the fire performance of these materials, providing a reference for their in situ evaluation and ensuring better fire safety standards for building steel structures and cables.

Free-standing cubic gauche nitrogen stable at 760 K under ambient pressure.

Cubic gauche nitrogen (cg-N) has received wide attention for its exceptionally high energy density and environmental friendliness. However, traditional synthesis methods for cg-N predominantly rely on high-pressure techniques or the utilization of nanoconfined effects using highly toxic and sensitive sodium azide as precursor, which substantially restrict its practical application. On the basis of the first-principles simulations, we found that adsorption of potassium on the cg-N surface exhibits superior stabilization compared to sodium. Then, we chose safer potassium azide as precursor for synthesizing cg-N. Through plasma-enhanced chemical vapor deposition treatment, the free-standing cg-N was successfully synthesized without the need for high-pressure and nanoconfined effects. It demonstrated excellent thermal stability up to 760 K, and then rapid and intense thermal decomposition occurred, exhibiting typical thermal decomposition behaviors of high-energy-density materials. The explosion parameters were also measured using laser-induced plasma spectroscopy. Our work has substantially promoted the practical application of cg-N as HEDMs.

BASEPROD: The Bardenas Semi-Desert Planetary Rover Dataset

Dataset acquisitions devised specifically for robotic planetary exploration are key for the advancement, evaluation, and validation of novel perception, localization, and navigation methods in representative environments. Originating in the Bardenas semi-desert in July 2023, the data presented in this Data Descriptor is primarily aimed at Martian exploration and contains relevant rover sensor data from approximately 1.7km of traverses, a high-resolution 3D map of the test area, laser-induced breakdown spectroscopy recordings of rock samples along the rover path, as well as local weather data. In addition to optical cameras and inertial sensors, the rover features a thermal camera and six force-torque sensors. This setup enables, for example, the study of future localization, mapping, and navigation techniques in unstructured terrains for improved Guidance, Navigation, and Control (GNC). The main features of this dataset are the combination of scientific and engineering instrument data, as well as the inclusion of the thermal camera and force-torque sensors in particular.

Deep learning assisted femtosecond laser-ablation spark-induced breakdown spectroscopy employed for rapid and accurate identification of bismuth brass

BackgroundOwing to its excellent machinability and less toxicity, bismuth brass has been widely used in manufacturing various industrial products. Thus, it is of significance to perform rapid and accurate identification of bismuth brass to reveal the alloying properties. However, the analytical lines of various elements in bismuth brass alloy products based on conventional laser-induced breakdown spectroscopy (LIBS) are usually weak. Moreover, the analytical lines of various elements are often overlaped, seriously interfering with the identification of bismuth brass alloys. To address these challenges, developing an advanced strategy enabling to achieve ultra-high accuracy identification of bismuth brass alloys is highly desirable. ResultsThis work proposed a novel method for rapidly and accurately identifying bismuth brass samples using deep learning assisted femtosecond laser-ablation spark-induced breakdown spectroscopy (fs-LA-SIBS). With the help of fs-LA-SIBS, a spectral database containing high quality LIBS spectra on element components were constructed. Then, one-dimensional convolutional neural network (CNN) was introduced to distinguish five species of bismuth brass alloy. Amazingly, the optimal CNN model can provide an identification accuracy of 100 % for specie identification. To figure out the spectral features, we proposed a novel approach named “segmented fs-LA-SIBS wavelength”. The identification contribution from various wavelength intervals were extracted by optimal CNN model. It clearly showed that, the differences of spectra feature in the wavelength interval from 336.05 to 364.66 nm can produce the largest identification contribution for an identification accuracy of 100 %. More importantly, the feature differences in the four elements such as Ni, Cu, Sn, and Zn, were verified to mostly contribute to identification accuracy of 100 %. SignificanceTo the best of our knowledge, it is the first study on one-dimensional CNN configuration assisted with fs-LA-SIBS successfully employed for performing identification of bismuth brass. Compared with conventional machine learning methods, CNN has shown significant more superiority. To reveal the tiny spectra differences, the classification contribution from spectra features were accurately defined by our proposed “segmented fs-LA-SIBS wavelength” method. It can be expected that, CNN assisted with fs-LA-SIBS has great promising for identifying the differences from various element components in metallurgical field.

Quantitative analysis of copper-molybdenum slurries based on low energy pulse laser, combined with artificial neural network and principal component analysis

In the mining industry, flotation serves as a critical step in the on-stream processing of slurry. A prompt and accurate acquisition of target elemental concentrations is fundamental for evaluating the efficiency of the flotation process. In this study, an algorithm of principal component analysis (PCA) combined with an artificial neural network (ANN) was proposed. This algorithm was based on a low energy pulse laser and a micro-spectrometer, incorporating the principle of laser-induced breakdown spectroscopy (LIBS). The method optimized the number of features for training the models, assisted by cross-validation. The copper concentrations of 36 slurries containing mixed copper-molybdenum powders were quantified. Compared to the full-spectrum model, the full-spectrum PCA model reduced the number of features from 271 to 5 while maintaining an appropriate fit. Meanwhile, the effect of the continuum on prediction accuracy was also analyzed, revealing that the continuum improved the prediction accuracy. This improvement can be explained by the fact that the continuum contains essential information, which serves as a correction for the intensity of the spectral lines. The results indicated that the prediction accuracy of the full-spectrum PCA model was significantly enhanced in comparison to the peak intensity and full-spectrum models, with the correlation coefficient (R2) of the test set improving from 0.894 to 0.985 and the root mean square error (RMSE) decreasing from 0.18% to 0.08%.

A Stand-Off Laser-Induced Breakdown Spectroscopy (LIBS) System for Remote Bacteria Identification.

Bacteria are the primary cause of infectious diseases, making rapid and accurate identification crucial for timely pathogen diagnosis and disease control. However, traditional identification techniques such as polymerase chain reaction and loop-mediated isothermal amplification are complex, time-consuming, and pose infection risks. This study explores remote (~3 m) bacterial identification using laser-induced breakdown spectroscopy (LIBS) with a Cassegrain reflective telescope. Principal component analysis (PCA) was employed to reduce the dimensionality of the LIBS spectral data, and the accuracy of support vector machine (SVM) and Random Forest (RF) algorithms was compared. Multiple repeated experiments showed that the RF model achieved a classification accuracy, recall, precision, and F1-score of 99.81%, 99.80%, 99.79%, and 0.9979, respectively, outperforming the SVM model and providing more accurate remote bacterial identification. The method based on laser-induced plasma spectroscopy and machine learning has broad application prospects, supporting noncontact disease diagnosis, improving public health, and advancing medical research and technological development.

Compositional analysis of Swertia chirayita medicinal plant using laser-induced breakdown spectroscopy and ICP-MS

Compositional analysis of Swertia chirayita medicinal plant using laser-induced breakdown spectroscopy and ICP-MS

Optimization of nanoparticle-enhanced laser-induced breakdown spectroscopy for the hyperspectral chemical mapping of solid samples

BackgroundNanoparticle-enhanced laser-induced breakdown spectroscopy (NE-LIBS) uses the plasmonic effect of metallic nanoparticles deposited on solid sample surfaces to achieve a significant signal enhancement by lowering the breakdown threshold and elevating plasma temperature. NE-LIBS has been used for localized analysis, but NE-LIBS mapping of solids has rarely been done, due to several challenges. In this study, we scrutinized the performance of NE-LIBS hyperspectral mapping of solid samples, when the controlled deposition of nanoparticles was done using magnetron sputtering. ResultsWe performed a detailed optimization of the nanoparticle-related signal enhancement involving the laser wavelength, laser fluence and detector gating. It was confirmed that while the laser wavelength has only a small influence in the studied range (at 266 nm, 532 nm and 1064 nm), but there is an optimum for laser fluence and detection gate delay. The best signal enhancement achieved for a glass sample was 25–30. The applicability of the approach was demonstrated by hyperspectral NE-LIBS mapping of a granite rock sample, which provided an improved sensitivity in the study of the elemental distribution (exemplified for Li and Mg), and by paint linear discriminant analysis, in which NE-LIBS gave rise to a significantly improved accuracy (98 %, as opposed to the LIBS accuracy of only 84 %). SignificanceThe analytical benefits of NE-LIBS hyperspectral mapping was demonstrated in two applications involving industrially relevant sample types. For example, the enhanced signals in rock elemental mapping can improve the localization of mineral grains viable for economic mining. The qualitative discrimination application involving paints demonstrates that the NE-LIBS approach can be beneficial in the spatial classification or identification of the local quality of a solid sample surface.