A critical review of recent progress in analytical laser-induced breakdown spectroscopy

Enhancement mechanism and optimization analysis of resonant excitation Laser-induced Breakdown Spectroscopy (LIBS-RE) in gaseous ammonia element detection

The existing techniques for gas components detection, such as Tunable Diode Laser Absorption Spectroscopy (TDLAS) and Cavity Ring-Down Spectroscopy (CRDS), are constrained by their inability to discern the elemental composition of the gas molecules. As a supplementary technology for detecting the total amount of atoms of each component in a gas, Laser-Induced Breakdown Spectroscopy (LIBS) offers significant advantages in gas elemental composition detection. Nevertheless, there are still some challenges to be overcome, including weak spectral signals and instability. In a previous study, we proposed a novel approach for detecting gaseous ammonia molecules using LIBS. The experiment employs a wavelength-tunable, continuous wave (CW) CO2 laser resonant excitation LIBS (LIBS-RE) to improve signal intensity. A comprehensive examination of the experimental parameters still needs to be required. This paper optimizes the gas flow rate and the energy of the resonant excitation source by comparing the spectral intensities of analysis lines between LIBS and LIBS-RE. The enhancement mechanism of LIBS-RE is analyzed concerning ammonia characteristics, molecular motion, and absorption efficiency. Furthermore, we examined the temporal evolution of H and N ions and atoms. By comparing the spectra of LIBS and LIBS-RE, the maximum enhancement factor for H and N was determined to be approximately 6.59 ± 0.51 and 2.44 ± 0.12, respectively. Finally, the feasibility of using LIBS-RE for the detection of gaseous ammonia has been preliminarily verified. The results demonstrate that LIBS-RE can realize the detection of the atomic content of gas components and has the potential to improve the detection capability.

Low Signal-to-Noise Ratio Optoelectronic Signal Reconstruction Based on Zero-Phase Multi-Stage Collaborative Filtering

The Laser Light Screen System faces critical technical challenges in high-speed, long-range target detection: when a target passes through the light screen, weak light flux variations lead to significantly degraded signal-to-noise ratios (SNRs). Traditional signal processing algorithms fail to effectively suppress phase distortion and boundary effects under extremely low SNR conditions, creating a technical bottleneck that severely constrains system detection performance. To address this problem, this paper proposes a Multi-stage Collaborative Filtering Chain (MCFC) signal processing framework incorporating three key innovations: (1) the design of zero-phase FIR bandpass filtering with forward-backward processing and dynamic phase compensation mechanisms to effectively suppress phase distortion; (2) the implementation of a four-stage cascaded collaborative filtering strategy, combining adaptive sampling and anti-aliasing techniques to significantly enhance signal quality; and (3) the development of a multi-scale adaptive transform algorithm based on fourth-order Daubechies wavelets to achieve high-precision signal reconstruction. The experimental results demonstrate that under -20 dB conditions, the method achieves a 25 dB SNR improvement and boundary artifact suppression while reducing the processing time from 0.42 to 0.04 s. These results validate the proposed method's effectiveness in high-speed target detection under low SNR conditions.

Differentiating between normal and inflammatory blood serum samples using spectrochemical analytical techniques and chemometrics

Inflammation detection in blood serum samples is commonly performed using clinical analyzers, which are expensive and complex and require specific labels or markers. Spectrochemical analytical techniques, such as laser-induced breakdown spectroscopy (LIBS) and laser-induced fluorescence (LIF), have emerged as alternative methods for qualitative and non-destructive analysis in various fields. This study explores applying LIBS and LIF techniques for label-free discrimination between normal and inflammatory blood serum samples. In the LIBS analysis, the serum samples are deposited on ashless filter paper and exposed to a high-power Nd:YAG laser source to induce plasma emission. The emitted light is dispersed in a spectrometer and an ICCD camera that captures the spectral lines. The LIF technique utilizes a diode-pumped solid-state laser source to excite the blood serum sample placed in a quartz cuvette. The resulting emission spectra are collected and analyzed using a spectrometer equipped with a CCD detector. The obtained spectroscopic data from both techniques is subjected to principal component analysis (PCA) and graph theory for classification and clustering. The PCA classified the two classes with a data variance of 85.4% and 92.8% based on the first two principal components (PCs) for LIBS and LIF spectra. The graph theory clustered the two classes with an accuracy of 76% and 100% based on LIBS and LIF spectra. The statistical methods effectively discriminate between normal and inflammatory serum samples, providing satisfactory results. The proposed spectrochemical methods offer several advantages over traditional clinical analyzers. They are cost-effective and rapid, making them suitable for the fast and reliable identification of serum samples in laboratories. The non-destructive nature of these techniques eliminates the need for specific labels or markers, further streamlining the analysis process.

Laser-Induced Breakdown Spectroscopy as an Accurate Forensic Tool for Bone Classification and Individual Reassignment

This article provides a detailed discussion of the evidence available to date on the application of laser-induced breakdown spectroscopy (LIBS) and supervised classification methods for the individual reassignment of commingled bone remains. Specialized bone chemistry studies have demonstrated the suitability of bone elemental composition as a distinct individual identifier. Given the widely documented ability of the LIBS technique to provide elemental emission spectra that are considered elemental fingerprints of the samples analyzed, the analytical potential of this technique has been assessed for the investigation of the contexts of commingled bone remains for their individual reassignment. The LIBS bone analysis consists of the direct ablation of micrometric portions of bone samples, either on their surface or within their internal structure. To produce reliable, accurate, and robust bone classifications, however, the available evidence suggests that LIBS spectral information must be processed by appropriate methods. When comparing the performance of seven different supervised classification methods using spectrochemical LIBS data for individual reassociation, those employing artificial intelligence-based algorithms produce analytically conclusive results, concretely individual reassociations with 100% accuracy, sensitivity, and robustness. Compared to LIBS, other techniques used for the purpose of interest exhibit limited performance in terms of robustness, sensitivity, and accuracy, as well as variations in these results depending on the type of bones used in the classification. The available literature supports the suitability of the LIBS technique for reliable individual reassociation of bone remains in a fast, simple, and cost-effective manner without the need for complicated sample processing.

Advances and Challenges in Tracking Interactions Between Plants and Metal-Based Nanoparticles

Metal-based nanoparticles (MNPs) are increasingly prevalent in the environment due to both natural processes and human activities, leading to direct interactions with plants through soil, water, and air exposure that can have beneficial and detrimental effects on plant growth and health. Understanding the uptake, translocation, and transformation of MNPs in plants is crucial for assessing environmental risks and leveraging nanotechnology in agriculture. However, accurate analysis of MNPs in plant tissues poses significant challenges due to complex plant matrices and the dynamic nature of nanoparticles. This short review summarizes recent advances in analytical methods for determining MNP-plant interactions, focusing on pre-processing and quantitative nanoparticle analysis. It highlights the importance of selecting appropriate extraction and analytical techniques to preserve nanoparticle integrity and accurate quantification. Additionally, recent advances in mass spectrometry, microscopy, and other spectroscopic techniques that improve the characterization of MNPs within plant systems are discussed. Future perspectives highlight the need to develop real-time in situ monitoring techniques and sensitive tools for characterizing nanoparticle biotransformation.

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

BACKGROUND

Nanoparticle-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.

RESULTS

We 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 %).

SIGNIFICANCE

The 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.

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.

Monitoring of nanoplasmonics-assisted deuterium production in a polymer seeded with resonant Au nanorods using in situ femtosecond laser induced breakdown spectroscopy

In this brief report, we present laser induced breakdown spectroscopy (LIBS) evidence of deuterium (D) production in a 3:1 urethane dimethacrylate (UDMA) and triethylene glycol dimethacrylate (TEGDMA) polymer doped with resonant gold nanorods, induced by intense, 40 fs laser pulses. The in situ recorded LIBS spectra revealed that the D/(2D + H) increased to 4-8% in the polymer samples in selected events. The extent of transmutation was found to linearly increase with the laser pulse energy (intensity) between 2 and 25 mJ (up to 3 × 1017W/cm2). The observed effect is attributed only to the field enhancing effects due to excited localized surface plasmons on the gold nanoparticles.

Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

Modulate the laser phase to improve the ns-LIBS spectrum signal based on orbital angular momentum

Aiming to enhance the ns-LIBS signal, in this work, we introduced orbital angular momentum to modulate the laser phase of the Gaussian beam into the vortex beam. Under similar incident laser energy, the vortex beam promoted more uniform ablation and more ablation mass compared to the Gaussian beam, leading to elevated temperature and electron density in the laser-induced plasma. Consequently, the intensity of the ns-LIBS signal was improved. The enhancement effects based on the laser phase modulation were investigated on both metallic and non-metallic samples. The results showed that laser phase modulation resulted in a maximum 1.26-times increase in the peak intensities and a maximum 1.25-times increase in the signal-to-background ratio (SBR) of the Cu spectral lines of pure copper for a laser energy of 10 mJ. The peak intensities of Si atomic spectral lines were enhanced by 1.58-1.94 times using the vortex beam. Throughout the plasma evolution process, the plasma induced by the vortex beam exhibited prolonged duration and a longer continuous background, accompanied by a noticeable reduction in the relative standard deviation (RSD). The experimental results demonstrated that modulation the laser phase based on orbital angular momentum is a promising approach to enhancing the ns-LIBS signal.

Energy spectrum of selected diatomic molecules (H2, CO, I2, NO) by the resolution of Schrodinger equation for combined potentials via NUFA method

CONTEXT

To determine the properties of diatomic molecules, studying their chemical bond energy spectrum is essential since it enables the assessment of their characteristics. This research presents diatomic molecules spectroscopic characteristics and rovibrational energy (H2, CO, I2, NO). The Schrodinger equation is solved to determine these energies, considering the presence of a combination of two distinct potentials. the inverse quadratic Yukawa potential in combination with the screened modified Kratzer.

METHOD

This work used the Greene-Aldrich assumption and the Nikiforov-Uvarov functional analysis approach as analytical tools to solve the Schrodinger equation and determine the energy spectrum of diatomic molecules (H2, CO, I2, NO). The use of Mathematica software allows for the calculation of the eigenvalues of energy of the previously mentioned diatomic molecules (H2, CO, I2, NO) based on their rovibrational energies in the final equation. By comparing the eigenvalue findings with previous research, it was seen that the technique yielded the expected and desirable outcomes.

Photoelectric Measurement and Sensing: New Technology and Applications

Laser-based measurement and sensing technology has been paid more and more attention by academia and industry because of its incomparable advantages, such as high sensitivity, fast response, and no contact [...].

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

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

Laser Emission Spectroscopy of Graphene Oxide Deposited on 316 Steel and Ti6Al4V Titanium Alloy Suitable for Orthopedics

This paper presents the results of an analysis of carbon (in the form of graphene oxide) deposited on the surface of threads made from stainless steel 316 and titanium alloy Ti6Al4V used in orthopedics using Laser Induced Breakdown Spectroscopy (LIBS). The aim of the article is to indicate the possibility of using the LIBS spectra for the study of thin layers, including graphene derivatives and other elements. Stratigraphic measurements allowed the detection of differences in the spectra peaks of individual elements, not only in the surface layer itself and in the native material, but also in the intermediate layer connecting the two layers. Due to the clear difference in the outline of the spectrum of graphene oxide and the spectrum of the native material of the samples analyzed, a clear incorporation of carbon atoms into the surface layer was observed. A factor analysis was performed, which confirmed the incorporation of graphene oxide into the surface layer of the native material of the elements examined.

Qualitative Analysis of Glass Microfragments Using the Combination of Laser-Induced Breakdown Spectroscopy and Refractive Index Data

We have successfully demonstrated that although there are significant analytical challenges involved in the qualitative discrimination analysis of sub-mm sized (microfragment) glass samples, the task can be solved with very good accuracy and reliability with the multivariate chemometric evaluation of laser-induced breakdown spectroscopy (LIBS) data or in combination with pre-screening based on refractive index (RI) data. In total, 127 glass samples of four types (fused silica, flint, borosilicate and soda–lime) were involved in the tests. Four multivariate chemometric data evaluation methods (linear discrimination analysis, quadratic discrimination analysis, classification tree and random forest) for LIBS data were evaluated with and without data compression (principal component analysis). Classification tree and random forest methods were found to give the most consistent and most accurate results, with classifications/identifications correct in 92 to 99% of the cases for soda–lime glasses. The developed methods can be used in forensic analysis.

Improving Calibration Strategy for LIBS Heavy Metals Analysis in Agriculture Applications

A new calibration procedure, known as mapping conditional-calibration laser-induced breakdown spectroscopy (LIBS), has been suggested to improve analysis results for heterogeneous samples. The procedure is based on LIBS elemental mapping, followed by signal conditioning in every sampling spot to skip signal outliers, then by finalizing the calibration curve construction. The suggested mapping conditional calibration procedure was verified for zinc analysis in soybean grist samples. The laser parameters correspond to those of the hand-held LIBS instrument in order to estimate the influence of sample surface heterogeneity under on-site analysis conditions. The laser spot (60 μm) was equal to or smaller than the typical size of grist particles (40–500 μm) but laser crater dimensions were significantly greater and varied widely (150–450 μm). The LIBS mapping of different spectral signals (atomic and ionic lines for major and minor components) was achieved. Elemental maps were normalized to achieve signal maps that were conditionally spotted to skip signal outliers. It was demonstrated that the suggested mapping conditional-calibration LIBS provided 15 ppm RMSECV for zinc determination in heterogeneous samples, which is typical for agricultural products.

Review of the distribution and detection methods of heavy metals in the environment

Heavy metals can be enriched in living organisms and seriously endanger human health and the ecological environment, which has evolved into a significant global environmental problem. Based on summarizing the spatial distribution of heavy metals in the environment, this review introduces heavy metal detection technologies such as inductively coupled plasma mass spectrometry/atomic emission spectrometry, atomic absorption spectrometry, atomic fluorescence spectrometry, and laser-induced breakdown spectrometry. It summarizes their respective advantages, characteristics, and applicability. Besides, atmospheric pressure discharge plasma as a potential heavy metal detection technology is also introduced and discussed in this review. The current research mainly focuses on improving the analytical performance and optimizing the practical application. Furthermore, this review not only summarizes the advantages of atmospheric pressure discharge plasma in the field of element analysis but also summarizes the principal scientific and technical problems to be solved urgently.

Understanding the relationship between viral infections and trace elements from a metallomics perspective: implications for COVID-19

Recently, the World Health Organization (WHO) declared a pandemic situation due to a new viral infection (COVID-19) caused by a novel virus (Sars-CoV-2). COVID-19 is today the leading cause of death from viral infections in the world. It is known that many elements play important roles in viral infections, both in virus survival, and in the activation of the host's immune system, which depends on the presence of micronutrients to maintain the integrity of its functions. In this sense, the metallome can be an important object of study for understanding viral infections. Therefore, this work presents an overview of the role of trace elements in the immune system and the state of the art in metallomics, highlighting the challenges found in studies focusing on viral infections.

Rapid characterization of heavy metals in single microplastics by laser induced breakdown spectroscopy

Microplastics (MPs) in aquatic environment usually carry hazardous matters, including toxic heavy metals. Quantification of heavy metals in MPs is crucial for the comprehensive understanding of their ecotoxicology in field environment. However, conventional methods for heavy metal determination either are applicable only to bulk/collective samples or require strict operation environment. Here we demonstrated that laser induced breakdown spectroscopy (LIBS) is a robust tool for the characterization of heavy metals in single MPs. Single-particle LIBS selects individual MPs with specific sizes (down to tens of microns), shapes, and morphologies and analyzes simultaneously multiple elements in milliseconds without sample pretreatment. In addition to the elaborate optical design, we also used stretched thin polyethylene film as a substrate, which significantly suppress the matrix interference to the particles' spectra. The single particle LIBS was demonstrated to be a quantitative analytical method, and was applied to heavy metal analysis of the MPs collected in the seawaters of the Beibu Gulf of China. Positive correlation between the spectral intensities and the local marine pollutions as well as significant heterogeneity in the elemental compositions were observed. The results demonstrate that single-particle LIBS is a promising method for MPs characterization and is suitable for studying pollutant transportation by using MPs as vectors.

Detection of E. coli labeled with metal-conjugated antibodies using lateral-flow assay and laser-induced breakdown spectroscopy

This study explores the adoption of laser-induced breakdown spectroscopy (LIBS) for the analysis of lateral-flow immunoassays (LFIAs). Gold (Au) nanoparticles are standard biomolecular labels among LFIAs, typically detected via colorimetric means. A wide diversity of lanthanide-complexed polymers (LCPs) are also used as immunoassay labels but are inapt for LFIAs due to lab-bound detection instrumentation. This is the first study to show the capability of LIBS to transition LCPs into the realm of LFIAs, and one of the few to apply LIBS to biomolecular label detection in complete immunoassays. Initially, an in-house LIBS system was optimized to detect an Au standard through a process of line selection across acquisition delay times, followed by determining limit of detection (LOD). The optimized LIBS system was applied to Au-labeled Escherichia coli detection on a commercial LFIA; comparison with colorimetric detection yielded similar LODs (1.03E4 and 8.890E3 CFU/mL respectively). Optimization was repeated with lanthanide standards to determine if they were viable alternatives to Au labels. It was found that europium (Eu) and ytterbium (Yb) may be more favorable biomolecular labels than Au. To test whether Eu-complexed polymers conjugated to antibodies could be used as labels in LFIAs, the conjugates were successfully applied to E. coli detection in a modified commercial LFIA. The results suggest interesting opportunities for creating highly multiplexed LFIAs. Multiplexed, sensitive, portable, and rapid LIBS detection of biomolecules concentrated and labeled on LFIAs is highly relevant for applications like food safety, where in-field food contaminant detection is critical. Graphical abstract.

Detection improvement of laser-induced breakdown spectroscopy using the flame generated from alcohol-solution mixtures

It has been proved that the detection of laser-induced breakdown spectroscopy (LIBS) could be improved by the flame. In this work, we applied flame enhanced LIBS for the detection of elements in water, while the flame was generated from the mixture of alcohol and aqueous solution. In the measurements, the flame is functioned as an assistance to enhance the LIBS detection, and also worked as a sampling way for the solution. The obtained results indicate that the detection of manganese, calcium, lithium and magnesium were significantly improved by the proposed method. It is found that the flame actually forms an environment for the laser-induced plasma to have higher temperature and lower electron density, as comparing with the plasma underwater. With the method, the quantitative analysis was tried out for the element of manganese, and the internal reference of calcium was used. It is interesting to find that, when mixing with the calcium, the minimum detectable concentration of manganese could be lowered and the intensity was greatly increased. According to the result, it is suggested that the proposed method might be a practical way for liquid detection of LIBS because of the simplicity and the effectiveness.

Rapid determination of trace cadmium in drinking water using laser-induced breakdown spectroscopy coupled with chelating resin enrichment

The determination of heavy metals in drinking water is of great importance, but it is hard to realize rapid and in-situ measurement. Laser-induced breakdown spectroscopy is an effective method for both solid and liquid sample analysis with advantages of fast and micro-destructive. However, the concentrations of heavy metals in drinking water is too low to be directly detected using LIBS. In this study, we enhanced the sensitivity of LIBS by coupling with chelating resin, which is usually used for water purification. The resin provided a rapid enrichment of the heavy metal, so the limits of detection of common LIBS system was much enhanced. Using Cadmium as the representative heavy metal, PLSR model for predicting Cd were built based on the spectral intensity (Cd 214.4 nm) with concentrations from 0 to 100 µg/L, and resulted in correlation coefficient of 0.94433 and RMSE of 7.1517 µg/L. The LoD was 3.6 µg/L. Furthermore, the volume, resin mass, adsorption time, and LIBS system parameters were optimized for practical applications. We also demonstrated that the resin can be recycled without loss in sensing ability. The combination of chelating resin with LIBS provides inexpensive, rapid, and sensitive detection method of trace heavy metal contaminants in drinking water.

Characterization of Nanoparticles: Advances

Over the past two decades, the unique properties of engineered nanoparticles (NPs) have placed them at the centre of revolutionary advancements in many sectors of science, technology and commerce. Multi-technique and multi-disciplinary analytical approaches are required to identify, quantify, and characterize the chemical composition, size and size distribution, surface properties and the number and concentration of NPs. In this chapter, an overview of the recent advances in the characterization of NPs will be presented.

Discrimination Between Explosive Materials and Isomers Using a Human Color Vision-Inspired Sensing Method

This paper describes the application of a human color vision approach to infrared (IR) chemical sensing for the discrimination between multiple explosive materials deposited on aluminum substrates. This methodology classifies chemicals using the unique response of the chemical vibrational absorption bands to three broadband overlapping IR optical filters. For this effort, Fourier transform infrared (FT-IR) spectroscopy is first used to computationally examine the ability of the human color vision sensing approach to discriminate between three similar explosive materials, 1,3,5,-Trinitro-1,3,5-triazinane (RDX), 2,2-Bis[(nitrooxy)methyl]propane-1,3,-diyldinitrate (PETN), and 1,3,5,7-Tetranitro-1,3,5,7-tetrazocane (HMX). A description of a laboratory breadboard optical sensor designed for this approach is then provided, along with the discrimination results collected for these samples using this sensor. The results of these studies demonstrate that the human color vision approach is capable of high-confidence discrimination of the examined explosive materials.

Accuracy improvement of quantitative analysis for major elements in laser-induced breakdown spectroscopy using single-sample calibration

Accurate determination of major elements using limited standard samples is always a big challenge in laser-induced breakdown spectroscopy (LIBS). Based on a simple calculation process, we propose a new one-point calibration method called single-sample calibration LIBS (SSC-LIBS) to build the calibration and improve the accuracy of determination of major elements. In this work, several major elements (Fe, Cu, Zn, Ni, Cr, Nb, and Mo) in three sets of matrix-matched certified samples were determined without sample preparation. The results showed that compared with multipoint calibration LIBS (MPC-LIBS), the R², RMSECV, and ARE of Cu elements were improved from 0.40 to 0.97, 3.55 wt% to 0.76 wt%, and 5.19% to 1.05%, respectively, while the ARSD decreased from 16.22% to 1.15%. Furthermore, the AREs in the concentration ranges of 1-10, 10-20, 30-40, 50-60, 60-70, and 80-100 wt% were 5.16%, 2.55%, 1.75%, 1.69%, 1.05%, and 0.44%, respectively, with almost all less than 5%, as calculated by SSC-LIBS. These results demonstrated that SSC-LIBS can improve the accuracy and stability of detecting major elements using only one standard sample, which can greatly promote the application of LIBS.

On-line detection of radioactive and non-radioactive heavy metals in tobacco smoke using portable laser-induced breakdown spectroscopy

A portable laser-induced breakdown spectroscopy (PLIBS) device is proposed for on-line detection of radioactive and non-radioactive heavy metals in tobacco smoke with ultra-high sensitivity.

Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.

One-point and multi-line calibration method in laser-induced breakdown spectroscopy

The calibration-free laser-induced breakdown spectroscopy (CF-LIBS) and its variations are low cost, short time consumption, and high adaptability. However, seeking a more flexible and simple quantitative analysis method remains a challenge. A one-point and multi-line calibration (OP-MLC) was presented as a simple quantitative analysis method of LIBS. The results showed that OP-MLC-LIBS method can achieve quantitative analysis using only one standard sample, and the average relative errors (AREs) are 9, 22, 21 and 36% for Mn, Cr, Ni and Ti elements in six tested low-alloy steel samples, respectively. The method requires neither a large number of standard samples nor complicated calculations, which provides a flexible and low-cost quantitative analysis approach for development and application of LIBS.

Determination of Elemental Content in Solder Mask Samples Used in Printed Circuit Boards Using Different Spectroanalytical Techniques

Solder masks are essential materials used in the manufacture of printed circuit boards (PCB). This material protects PCBs against several types of damage and performance failure. In this study, the capabilities of laser-induced breakdown spectroscopy (LIBS) were investigated for the direct analysis of solder masks typically commercialized for homemade PCB production, and inductively coupled plasma-optical emission spectrometry (ICP-OES) was used to obtain a chemical profile for the target analytes Al, As, Ba, Cd, Co, Cr, Cu, Fe, Hg, Mg, Mn, Ni, Pb, Sb, Sn, and Zn. Inductively coupled plasma-mass spectrometry (ICP-MS) was also employed for the determination of potentially toxic elements, such as As, Cd, Cr, Pb, and Hg. In addition to the qualitative information that may be useful for obtaining the spectral profile related to the raw materials present in solder masks formulations, LIBS was also applied for major elements (Al, Ba, Cu, Fe, Mg, and Zn) determination, but due to the low sensitivity, the obtained results were only semi-quantitative for Ba. Regarding Cd, Cr, Hg, and Pb, the samples analyzed were following the restriction of hazardous substances (RoHS) directive of the European Union.

Comparative Study of the Detection of Chromium Content in Rice Leaves by 532 nm and 1064 nm Laser-Induced Breakdown Spectroscopy

Fast detection of toxic metals in crops is important for monitoring pollution and ensuring food safety. In this study, laser-induced breakdown spectroscopy (LIBS) was used to detect the chromium content in rice leaves. We investigated the influence of laser wavelength (532 nm and 1064 nm excitation), along with the variations of delay time, pulse energy, and lens-to-sample distance (LTSD), on the signal (sensitivity and stability) and plasma features (temperature and electron density). With the optimized experimental parameters, univariate analysis was used for quantifying the chromium content, and several preprocessing methods (including background normalization, area normalization, multiplicative scatter correction (MSC) transformation and standardized normal variate (SNV) transformation were used to further improve the analytical performance. The results indicated that 532 nm excitation showed better sensitivity than 1064 nm excitation, with a detection limit around two times lower. However, the prediction accuracy for both excitation wavelengths was similar. The best result, with a correlation coefficient of 0.9849, root-mean-square error of 3.89 mg/kg and detection limit of 2.72 mg/kg, was obtained using the SNV transformed signal (Cr I 425.43 nm) induced by 532 nm excitation. The results indicate the inspiring capability of LIBS for toxic metals detection in plant materials.

Identification of Coffee Varieties Using Laser-Induced Breakdown Spectroscopy and Chemometrics

We linked coffee quality to its different varieties. This is of interest because the identification of coffee varieties should help coffee trading and consumption. Laser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was used to identify coffee varieties. Wavelet transform (WT) was used to reduce LIBS spectra noise. Partial least squares-discriminant analysis (PLS-DA), radial basis function neural network (RBFNN), and support vector machine (SVM) were used to build classification models. Loadings of principal component analysis (PCA) were used to select the spectral variables contributing most to the identification of coffee varieties. Twenty wavelength variables corresponding to C I, Mg I, Mg II, Al II, CN, H, Ca II, Fe I, K I, Na I, N I, and O I were selected. PLS-DA, RBFNN, and SVM models on selected wavelength variables showed acceptable results. SVM and RBFNN models performed better with a classification accuracy of over 80% in the prediction set, for both full spectra and the selected variables. The overall results indicated that it was feasible to use LIBS and chemometric methods to identify coffee varieties. For further studies, more samples are needed to produce robust classification models, research should be conducted on which methods to use to select spectral peaks that correspond to the elements contributing most to identification, and the methods for acquiring stable spectra should also be studied.

Use of laser-induced breakdown spectroscopy for the determination of polycarbonate (PC) and acrylonitrile-butadiene-styrene (ABS) concentrations in PC/ABS plastics from e-waste

Due to the continual increase in waste generated from electronic devices, the management of plastics, which represents between 10 and 30% by weight of waste electrical and electronic equipment (WEEE or e-waste), becomes indispensable in terms of environmental and economic impacts. Considering the importance of acrylonitrile-butadiene-styrene (ABS), polycarbonate (PC), and their blends in the electronics and other industries, this study presents a new application of laser-induced breakdown spectroscopy (LIBS) for the fast and direct determination of PC and ABS concentrations in blends of these plastics obtained from samples of e-waste. From the LIBS spectra acquired for the PC/ABS blend, multivariate calibration models were built using partial least squares (PLS) regression. In general, it was possible to infer that the relative errors between the theoretical or reference and predicted values for the spiked samples were lower than 10%.

Combining Raman and laser induced breakdown spectroscopy by double pulse lasing

A new approach combining Raman spectrometry and laser induced breakdown spectrometry (LIBS) within a single laser event was suggested. A pulsed solid state Nd:YAG laser running in double pulse mode (two frequency-doubled sequential nanosecond laser pulses with dozens microseconds delay) was used to combine two spectrometry methods within a single instrument (Raman/LIBS spectrometer). First, a low-energy laser pulse (power density far below ablation threshold) was used for Raman measurements while a second powerful laser pulse created the plasma suitable for LIBS analysis. A short time delay between two successive pulses allows measuring LIBS and Raman spectra at different moments but within a single laser flash-lamp pumping. Principal advantages of the developed instrument include high quality Raman/LIBS spectra acquisition (due to optimal gating for Raman/LIBS independently) and absence of target thermal alteration during Raman measurements. A series of high quality Raman and LIBS spectra were acquired for inorganic salts (gypsum, anhydrite) as well as for pharmaceutical samples (acetylsalicylic acid). To the best of our knowledge, the quantitative analysis feasibility by combined Raman/LIBS instrument was demonstrated for the first time by calibration curves construction for acetylsalicylic acid (Raman) and copper (LIBS) in gypsum matrix. Combining ablation pulses and Raman measurements (LIBS/Raman measurements) within a single instrument makes it an efficient tool for identification of samples hidden by non-transparent covering or performing depth profiling analysis including remote sensing. Graphical abstract Combining Raman and laser induced breakdown spectroscopy by double pulse lasing.

Moisture Influence Reducing Method for Heavy Metals Detection in Plant Materials Using Laser-Induced Breakdown Spectroscopy: A Case Study for Chromium Content Detection in Rice Leaves

Fast detection of heavy metals in plant materials is crucial for environmental remediation and ensuring food safety. However, most plant materials contain high moisture content, the influence of which cannot be simply ignored. Hence, we proposed moisture influence reducing method for fast detection of heavy metals using laser-induced breakdown spectroscopy (LIBS). First, we investigated the effect of moisture content on signal intensity, stability, and plasma parameters (temperature and electron density) and determined the main influential factors (experimental parameters F and the change of analyte concentration) on the variations of signal. For chromium content detection, the rice leaves were performed with a quick drying procedure, and two strategies were further used to reduce the effect of moisture content and shot-to-shot fluctuation. An exponential model based on the intensity of background was used to correct the actual element concentration in analyte. Also, the ratio of signal-to-background for univariable calibration and partial least squared regression (PLSR) for multivariable calibration were used to compensate the prediction deviations. The PLSR calibration model obtained the best result, with the correlation coefficient of 0.9669 and root-mean-square error of 4.75 mg/kg in the prediction set. The preliminary results indicated that the proposed method allowed for the detection of heavy metals in plant materials using LIBS, and it could be possibly used for element mapping in future work.