Laser-induced Breakdown Spectroscopy Analysis Research Articles

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.

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.

Comparison of laser-induced breakdown spectroscopy (LIBS) and ICP analysis results for measuring Pb and Zn in soil

Context Laser-induced breakdown spectroscopy (LIBS) is a rapid, multielement analytical technique. It is particularly suitable for the qualitative and quantitative analyses of heavy metals in solid samples. Aims To validate the technique, the LIBS data were compared with the data obtained via conventional inductively coupled plasma (ICP) spectroscopy for the same soil samples. Methods In this study, standard and unknown soil samples from contaminated areas were prepared and fixed to an adhesive tape for LIBS analysis. The soils were also digested with acids for ICP analysis. The emission intensity of one selected line for each of the two analytes, i.e. lead (Pb) and zinc (Zn), was normalised to the background signal and plotted as a function of the concentration values previously determined via ICP analysis. Key results The data demonstrated good linearity for the calibration lines drawn, and the correlation between the ICP and LIBS data was confirmed by the satisfactory agreement between the corresponding values. Conclusions The concentration coefficient of determination (R2) between LIBS and ICP-aqua regia digestion analysis or ICP-total digestion analysis were >0.86 and >0.89 for Pb and Zn, respectively. The total analysis time for the LIBS method was 310 min, which was 54.40% shorter than that for the ICP method (680 min). Implications Consequently, LIBS can be used to measure Pb and Zn in soils without any chemical preparation.

A strategy to reduce spectral intensity uncertainty and predicted content uncertainty of low and medium alloy steel elements

This work presents a correction method for reducing the uncertainty in the spectral intensity and quantitative prediction model by incorporating plasma acoustic pressure parameters (First peak value and First attenuation slope) and validated it in order to reduce the uncertainty in the quantitative analysis of laser-induced breakdown spectroscopy (LIBS). Mo, Al, Mn, and V elements in low and medium alloy steels were analyzed using a simultaneous plasma spectroscopy and acoustic pressure signal acquisition system. The relationship between the spectra and the acoustic pressure parameters was then examined in order to analyze the spectral intensities and the uncertainty of the quantitative prediction model after incorporating the acoustic pressure parameters. The findings indicate that, in comparison to the original spectra, the average relative standard deviation (ARSD%) of the spectral intensities of the elements Mo, Al, Mn, and V incorporated in the first peak of the sound pressure were reduced by approximately 50.32%, 44.33%, 52.74%, and 41.57%, respectively, and that of the elements incorporated in the first attenuation slopes were reduced by approximately 35.82%, 29.99%, 38.38%, and 24.66%, respectively. We discovered that the quantitative laser-induced breakdown spectroscopy model based on the plasma acoustic pressure parameter correction may also successfully lower the uncertainty of the projected elemental content values through prediction model cross-validation studies.

Self-absorption of emission lines in picosecond-laser-produced gold plasmas

Ultrashort laser ablation offers several advantages in various applications compared to traditional nanosecond laser ablation techniques. Despite providing a lower damage threshold, cold ablation with high precision, the impact of self-absorption effects in ultrashort laser-produced plasmas (LPPs) significantly affects the assessment of plasma parameters and analytical outcomes in Laser-Induced Breakdown Spectroscopy (LIBS) analysis. This study investigated the impact of laser energy, analyte concentration, and acquisition gate delay on the self-absorption of emission lines from picosecond LPPs (ps-LPPs) of gold targets at atmospheric pressure. We used four gold targets (24 carat gold, 22 carat gold, 18 carat gold, and Hepatizon) with varying concentrations of gold and copper. To analyze the self-absorption effects in ps-LPP, we examined four neutral emission lines of ps-LIBS spectra (Au I: 267.59 and 627.81 nm; Cu I: 327.98 and 510.55 nm). We observed that with an increase in laser pulse energy, there is a corresponding rise in the self-absorption of emission lines under ambient pressure. This increase in analyte species concentration leads to an elevation in the self-absorption of emission lines. Additionally, as the temporal delay of acquisition extends, self-absorption intensifies.

A rapid in-situ hardness detection method for steel rails based on LIBS and machine learning

The railway, as a public transportation system, has played a significant role in global economic development. However, the pursuit of high speed and heavy load in trains has brought significant bending and shear stresses on the rails, making the performance of steel rails a notable focal point. The properties of steel rails are crucial factors determining the operational safety of high-speed trains, while the surface hardness is regarded as a key mechanical characteristic. This study is to develop a new rapid in-situ method to measure the hardness of steel rails, by employing Laser-Induced Breakdown Spectroscopy (LIBS) and specified analysis technology. Three distinct methods, including spectral line intensity ratios, plasma excitation temperature and machine learning, have been compared and analysed. Particularly, a multivariate model is established and optimized using the machine learning methods for U71Mn steel rail hardness analysis. In the machine learning algorithms, variance normalization is utilized, resulted in a significant improvement for the information retention during the data dimensionality reduction process. Subsequently, twelve algorithm combinations were explored, revealing that the Particle Swarm Optimization employed in Support Vector Regression (PSO-SVR) yielded the lowest mean squared error (MSE). Further refinement of the PSO-SVR was achieved through the incorporation of adaptive stochastic weights, resulting in an elevated coefficient of determination (R2) to 0.9876. Finally, the performance of the model on the new five samples was validated with an R2 of 0.9864. Otherwise, its potential applicability may be extended to broader domains, providing robust support for enhancing the precision and reliability of LIBS technology in surface hardness quantitative analysis.

Rapid detection and recognition of phosphors using laser-induced breakdown spectroscopy and principal component analysis method—back propagation neural network algorithm

Rapid detection and quality monitoring of phosphor materials have always been a difficult problem in phosphor materials market. In this work, an independently proposed method based on principal component analysis method—error back propagation neural network algorithm—laser induced breakdown spectroscopy (PCA-BPNN-LIBS) was used for the detection and recognition of phosphors. Firstly, spectroscopic study was carried out on phosphor material samples, and the composition of phosphor elements was analyzed according to the full emission spectrum. Spectral data with different element characteristics detected by LIBS were used as training data sets for further identification. Then PCA method and BPNN algorithm were applied to identify 4 types phosphor samples (P11, P20, P43, P46). A very clear distinction graph was obtained, and the classification accuracy of 99.93% was verified. Allresults show that the proposed PCA-BPNN-LIBS method is an effective method for rapid analysis and recognition of phosphors.

Multielement simultaneous quantitative analysis of trace elements in stainless steel via full spectrum laser-induced breakdown spectroscopy

Laser-Induced Breakdown Spectroscopy (LIBS) instruments are increasingly recognized as valuable tools for detecting trace metal elements due to their simplicity, rapid detection, and ability to perform simultaneous multi-element analysis. Traditional LIBS modeling often relies on empirical or machine learning-based feature band selection to establish quantitative models. In this study, we introduce a novel approach—simultaneous multi-element quantitative analysis based on the entire spectrum, which enhances model establishment efficiency and leverages the advantages of LIBS. By logarithmically processing the spectra and quantifying the cognitive uncertainty of the model, we achieved remarkable predictive performance (R2) for trace elements Mn, Mo, Cr, and Cu (0.9876, 0.9879, 0.9891, and 0.9841, respectively) in stainless steel. Our multi-element model shares features and parameters during the learning process, effectively mitigating the impact of matrix effects and self-absorption. Additionally, we introduce a cognitive error term to quantify the cognitive uncertainty of the model. The results suggest that our approach has significant potential in the quantitative analysis of trace elements, providing a reliable data processing method for efficient and accurate multi-task analysis in LIBS. This methodology holds promising applications in the field of LIBS quantitative analysis.

Elemental composition and structural characteristics of Bio-active™ orthodontic archwire

The Bio-active™ archwires are the latest generation multi-force orthodontic archwires made of a Ni-Ti alloy. It is of particular importance to orthodontists to know what their composition and structural characteristics are so that they can determine which one is suitable for a given stage of orthodontic treatment. The aim of this work is to characterize as-received Bio-active™ archwires, consisting of three segments (anterior, bicuspid and posterior), by determining their physicochemical properties. Laser-induced breakdown spectroscopy (LIBS) was used to determine the elemental composition in the three different segments of the archwires, along with X-ray diffraction analysis (XRD), scanning electronic microscopy (SEM), energy dispersive X-ray spectroscopy (EDX) and differential scanning calorimetry (DSC). A LIBS and EDX analysis of the elemental composition showed that nickel (55wt.%) and titanium (45wt.%) are the main elements, and in some segments Fe and Cr registered as trace elements. A XRD analysis, at room temperature, showed two similar peaks, characteristic of a Ni-Ti alloy, proof that the archwire is an austenite phase. The DSC data was obtained by measuring the Af temperatures for each segment (heated up to +80°C and cooled down to -80°C), showing that they can be classified as martensite-active wires (heat-activated). Based on that a recommendation can be made to cool down the unused, as-received archwires before clinical use to ensure that they will fit in the brackets easier. On the surface of the as-received archwires small grains can be seen from the SEM micrographs. The obtained results provide orthodontists important information regarding the physicochemical properties of the as-received Bio-active™ archwires. The results can also serve as a foundation for future research on the elemental composition and morphology of clinically applied Bio-active™ archwires.

Substrate-Assisted Laser-Induced Breakdown Spectroscopy Combined with Variable Selection and Extreme Learning Machine for Quantitative Determination of Fenthion in Soybean Oil

The quality and safety of edible vegetable oils are closely related to human life and health, meaning it is of great significance to explore the rapid detection methods of pesticide residues in edible vegetable oils. This study explored the applicability potential of substrate-assisted laser-induced breakdown spectroscopy (LIBS) for quantitatively determining fenthion in soybean oils. First, we explored the impact of laser energy, delay time, and average oil film thickness on the spectral signals to identify the best experimental parameters. Afterward, we quantitatively analyzed soybean oil samples using these optimized conditions and developed a full-spectrum extreme learning machine (ELM) model. The model achieved a prediction correlation coefficient (RP2) of 0.8417, a root mean square error of prediction (RMSEP) of 167.2986, and a mean absolute percentage error of prediction (MAPEP) of 26.46%. In order to enhance the prediction performance of the model, a modeling method using the Boruta algorithm combined with the ELM was proposed. The Boruta algorithm was employed to identify the feature variables that exhibit a strong correlation with the fenthion content. These selected variables were utilized as inputs for the ELM model, with the RP2, RMSEP, and MAPEP of Boruta-ELM being 0.9631, 71.4423, and 10.06%, respectively. Then, the genetic algorithm (GA) was used to optimize the parameters of the Boruta-ELM model, with the RP2, RMSEP, and MAPEP of GA-Boruta-ELM being 0.9962, 11.005, and 1.66%, respectively. The findings demonstrate that the GA-Boruta-ELM model exhibits excellent prediction capability and effectively predicts the fenthion contents in soybean oil samples. It will be valuable for the LIBS quantitative detection and analysis of pesticide residues in edible vegetable oils.

Rapid Assessment of Extractability of Macronutrients from Yerba Mate (Illex paraguariensis) Leaves Based on Laser-Induced Breakdown Spectroscopy

Leaves of yerba mate plant (Ilex paraguariensis) have a wealth of nutrients, ingested by people who drink them in the hot water infusion popularly known as mate. In the present work, the laser-induced breakdown spectroscopy (LIBS) technique was applied for the first time to analysis of the extractability of macronutrients, including Mg, Ca, Na, and K, in commercial samples of yerba mate. Powdered samples from leaves’ material were used to simulate the infusion process in the laboratory. To carry out LIBS analysis, the emission spectra were measured before and after the infusion from the samples prepared in pellets. The spectral data were processed and analyzed by a specially designed algorithm. A coefficient of extractability was calculated for each of the investigated macronutrients in the range 34–76%, showing a good correlation with the corresponding elemental concentrations leached into the water infusion, determined by Atomic Absorption Spectroscopy. The obtained results demonstrated the feasibility of our approach for the rapid analysis of extractable macronutrients present in yerba mate leaves.

Laser-Induced Breakdown Spectroscopy Optimization Using Response Surface Methodology

Laser-induced breakdown spectroscopy (LIBS) is a modern analytical technique that is capable of fast, multi-elemental, lowcost and environmental friendly analysis, which does not require complex sample preparation. Albeit its potential, LIBS analysis still presents many limitations in terms of sensitivity and reproducibility, especially when analyzing complex matrices such as of sediments and soil samples. In order to reduce these matrix related effects, it is highly recommended that the system temporal parameters, which are responsible for controlling plasma evolution and signal collection, are optimized beforehand. In this work, we proposed the design of experiments (DOE) tool – specifically, the response surface methodology (RSM) – as an approach to optimize LIBS’s most important parameters (delay-time, interpulse delay, gate width and accumulated pulse). Signal-to-noise ratio (SNR) of the emission lines for Zn, Cd, Mg, Al, Ni, Cu, Ca, Cr, Sr, Fe were the response variable assessed during the procedure. The results showed that the RSM was an effective optimization tool for LIBS parameters and the final condition improved SNR ratios by up to a 48 ratio, when comparing to the not-optimal conditions.

CF-LIBS based elemental analysis of Saussurea simpsoniana medicinal plant: a study on roots, seeds, and leaves.

The plant Saussurea Simpsoniana, which has been used in traditional medicine for its biocompatibility and abundant nutrients, offers a wide range of remedies. Local communities effectively utilize medicines derived from the plant's roots to treat various ailments such as bronchitis, rheumatic pain, and abdominal and nervous disorders. In this study, we present an elemental analysis of the chemical composition (wt%) of this medicinal plant using the laser-induced breakdown spectroscopy (LIBS) technique. In the air atmosphere, an Nd:YAG (Q-switched) laser operating at a wavelength of 532nm is utilized to create plasma on the sample's surface. This laser has a maximum pulse energy of approximately 400mJ and a pulse duration of 5ns. A set of six miniature spectrometers, covering the wavelength range of 220-970nm, was utilized to capture and record the optical emissions emitted by the plasma. The qualitative analysis of LIBS revealed the presence of 13 major and minor elements, including Al, Ba, C, Ca, Fe, H, K, Li, Mg, Na, Si, Sr, and Ti. Quantitative analysis was performed using calibration-free laser-induced breakdown spectroscopy (CF-LIBS), ensuring local thermodynamical equilibrium (LTE) and optically thin plasma condition by considering plasma excitation temperature and electron number density. In addition, a comparison was made between the results obtained from CF-LIBS and those acquired through energy-dispersive X-ray spectroscopy (EDX) analysis.

Identification of wood specimens utilizing fs-LIBS and machine learning techniques

We report on the ability to identify wood specimens by utilizing 30 fs Laser Induced Breakdown Spectroscopy (LIBS) in conjunction with machine learning techniques. Ten different wood specimens have been studied. The spectral features were assigned to atomic/ionic and diatomic molecular transitions. The origin of the latter has been explored by investigating the dynamics of the created plume in ambient and argon atmosphere. Principal Component Analysis (PCA) was employed for dimensionality reduction based on the primary LIBS analysis. The principal components formation is grounded on the CN, Ca II, Ca I, and Na, LIBS data. Furthermore, applying the weighted k nearest neighbor (kNN) algorithm led to an accurate identification of the investigated specimens, since the evaluation metrics of specificity value were found to be in the range of 0.96–1.00, while that of accuracy was within 0.93–1.00.

Polluted soil-plant interaction analysis and soil classification based on laser-induced breakdown spectroscopy and machine learning.

A new method is introduced for the swift and precise detection of soil pollution and its effects on crops. Soil quality is essential for human well-being, with heavy metal pollution presenting considerable risks to both the ecological environment and human health. In crops, heavy metal contamination primarily occurs through mediums such as soil and water sources. This study introduces a system combining Laser-Induced Breakdown Spectroscopy (LIBS) with machine learning (ML) to analyze garlic contaminated by soil and the soil used for its cultivation. The simulation conducted in this study focuses on the impact of heavy metal-contaminated soil on garlic. Detection results indicate a significant influence of soil on garlic, resulting in heavy metal accumulation. Further analysis shows that metals from contaminated soil accumulate differently in various garlic plant parts, as per spectral data, underscoring the need for targeted detection methods to assess crop contamination. Conducting LIBS analysis on various soil samples enables the classification of different soil types. This indicates that tracing the origin of contaminated garlic through its residual soil is feasible. These findings imply the feasibility of tracing contaminated garlic's origin through its residual soil.

Investigation on plasma morphology fluctuation in laser-induced breakdown spectroscopy analysis of particle flow due to stochastic particle ablation

Plasma morphology fluctuation is the primary source for signal uncertainty of laser-induced breakdown spectroscopy (LIBS). This study has investigated the plasma morphology fluctuation due to stochastic particle ablation in LIBS particle flow analysis. K-means clustering was employed to analyze a large set of spectral data, identifying four plasma patterns: weak, moderate, air-prominent, and extreme plasma. As the excitation probabilities of four plasma patterns differed, the analytical signal statistic concerning the particle ablation deviated from the Gaussian distribution but aligned with the generalized extreme value distribution. For each plasma pattern, the pulse-to-pulse plasma morphology fluctuation in terms of plasma length and center position was investigated by time-resolved imaging. The dominant particle ablation process was evaluated through particle distribution images. Weak plasma with the most fluctuated plasma length and moderate plasma with the most fluctuated center position contributed to considerable signal uncertainty. Air-prominent and extreme plasma presented more repeatable signals due to less plasma morphology fluctuation. The pulse-to-pulse relative standard deviations of particle emission for the weak, moderate, air-prominent, and extreme plasma were 60.68%, 41.75%, 38.62%, and 22.20%, respectively. All these results suggest a plasma modulation pathway for particle flow analysis.

Historical pyrite “mirrors” from the Ecuadorian Cañari culture, digital microscopy observations, mineralogical and elemental analysis

This research relies on microscopy and digital microscopy observations and spectral analysis techniques: Raman spectroscopy, laser-induced breakdown spectroscopy (LIBS) and particle-induced X-ray emission (PIXE), to recover part of the lost identity of two pyrite (FeS2) cones. Conserved in Paris, at the Muséum National d'Histoire Naturelle (MNHN), the first one is registered MIN000-3519 in the collection of the mineralogist René-Just Haüy, the second one, registered 105.504, is a stray object. All information related to its origin would have been lost during the move (1837–1841) of the collections from the former Cabinet du Jardin du Roy to the new earth science gallery of the MNHN. Our historical research (Gendron, 2022) revealed that the first one, described as an “Inca mirror”, was shipped from Peru around 1760 by the botanist Joseph de Jussieu to his brothers Antoine and Bernard. They also confirmed that these two pyrite cones are archaeological objects of the Ecuadorian Cañari culture (500–1500 AD).Raman analyses conducted on the MIN000-3519 specimen confirm that it was developed on a quartz paragenesis. While the faces observation of no.105.504 reveals that the crystal used for its cut was triglyph twinned, a crystal which must have been cubic or dodecahedral pentagonal. The observation of the technical traces in digital microscopy makes it possible to reconstruct a process of similar cut for the two “mirrors” and help to discover red mineral clusters in the bottom of the crystalline gaps. LIBS analyses were also applied to get complementary information on in-depth element distribution, such as Fe and Al, in order to understand the stratigraphy inside the cracks. Finally, PIXE measurements do not confirm that these two pyrites come from the same deposit. But, this analytical technique is hampered by the nature of the element Fe which offers multiple and random bonding possibilities during the crystallization of iron minerals and by the lack of a comparative sample in the Parisian mineralogic collections.

Adsorption of Nickel Ions on the γ-Alumina Surface: Competitive Effect of Protons and Its Impact on Concentration Profiles.

Mesoporous γ-alumina is used as an adsorbent in the decontamination of water from heavy metals (e.g., nickel and cobalt) and as a support for heterogeneous catalysts prepared by impregnation. In these cases, alumina extrudates are in contact with aqueous solutions containing precursors of the active metal phase to be deposited. The proton concentration (or pH) in the metal solution in contact with alumina can impact the adsorption efficiency of decontamination processes and the activities of catalysts. Yet, it is difficult to quantify the effect of the pH inside the pores since protons are not detected by classical imaging techniques. In this article, the effect of protons on nickel adsorption on alumina is evaluated using a novel technique coupling liquid analysis (pH, conductivity, and UV/vis) and laser-induced breakdown spectroscopy (or LIBS) analysis of concentration gradients inside the solid. Both methods are in excellent agreement. The results show a slow diffusion of protons inside alumina pores (diffusion continues even after 940 min), yielding high proton concentration gradients. On the other hand, the nickel species penetrate the extrudates faster but are slowly displaced by protons under certain operating conditions. As a result, different metal concentration profiles are obtained, depending on the initial pH and contact time. These findings are interesting in catalysis since they prove the possibility of controlling the deposition of the active metal on catalysts by regulating the operating conditions of impregnation. For typical industrial impregnation times (a few minutes to 1 to 2 h), protons do not have enough time to deeply penetrate inside extrudates, so the initial pH of the metal solution will have nearly no effect on the metal distribution. Conversely, decontamination processes have much longer contact times; therefore, lower initial pH values should have negative impacts on the adsorption efficiency due to the protons displacing the adsorbed nickel.

Research on improving the accuracy of laser-induced breakdown spectroscopy analysis by considering plasma attenuation rate characteristics

BackgroundLaser-induced breakdown spectroscopy (LIBS) is widely applied in various fields, but accuracy issues limit its further development. Signal uncertainty is the main reason that affects the accuracy of LIBS measurements, but the signal uncertainty caused by different plasmas exhibiting different radiation attenuation rates during the integration time is often neglected. There is a need for a method to correct LIBS signals by quantifying the radiation attenuation rate. ResultsIn order to reduce the uncertainty due to different plasma attenuation rates, the attenuation rates of the energy level radiation emitted by plasma are described as attenuation coefficients, which are obtained by linearly fitting the logarithm of the time series of line intensities. The calibration curve was corrected by attenuation coefficients for 4 major elements in 7 standard samples. The results showed that the line intensities corrected by attenuation coefficients showed better linearity with elemental concentrations. SignificanceThis study is important for improving the accuracy of LIBS measurements, and is also significant for modeling the plasma radiative attenuation of laser-induced plasma, and is expected to be applied to spectrometers that can obtain time series spectra of the same plasma to improve the accuracy of in-situ fast LIBS analysis.

A review of intelligent coal gangue separation technology and equipment development

ABSTRACT One of the necessary ways to achieve the efficient, clean utilization of coal resources is to reduce the amount of coal gangue in raw coal to ensure the development of clean, low-carbon, and green energy in China. The application of conventional coal gangue separation equipment is severely limited due to the large investment in early construction, high production costs, and large water consumption and waste in production. Intelligent coal gangue separation equipment has the advantages of fast speed, high precision, strong modularity, and integration scalability. It also has the advantages of low operation cost and energy consumption, convenient operation and maintenance, and no water use. All of that could effectively improve the utilization rate of coal resources and expand the economic benefits of enterprises. The application of high-speed, high-precision, and anti-interference online element rapid analysis technology in intelligent coal gangue separation equipment will become an inevitable trend of equipment development in the future with the continuous improvement of coal product demand. Online rapid element analysis technology includes laser-induced breakdown spectroscopy, dual-energy X-ray, and transient neutron activation analysis. Improving and applying more high-speed, high-precision algorithms such as partial least squares and Monte Carlo library least squares in the future are important.