Laser-induced Breakdown Spectroscopy Analysis Research Articles

Probabilistic-based identification of gunshot residues (GSR) using Laser-Induced Breakdown Spectroscopy (LIBS) and Support Vector Machine (SVM) algorithm

Firearm violence results in thousands of victims annually in Brazil, which is ascribed, among other factors, to the low homicide resolution rates. One method for generating forensic evidence in firearm discharges is the identification of gunshot residues (GSR), typically composed of inorganic particles containing primarily lead (Pb), barium (Ba), and antimony (Sb). The “gold standard” technique for GSR identification is Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS) which is highly accurate but demands significant time and labor for each sample analysis. Other tests to detect GSR include the colorimetric rhodizonate test and spectroscopic techniques such as Laser-Induced Breakdown Spectroscopy (LIBS), which are quicker, but exhibit lower reliability and a higher potential for false positives and potentially compromise the investigation. This study introduces a novel GSR analytical protocol as a potential alternative to SEM-EDS and colorimetric tests, which is based on LIBS analysis combined with dimensionality reduction performed by PCA and probabilistic Support Vector Machine (SVM) algorithm. The developed protocol accurately predicts both positive and negative samples, and establishes the probability of each prediction, allowing for the exclusion of questionable samples from the analysis. The initial tests show the protocol achieved 100 % accuracy in both training and external validation. As a stress test, the protocol is able to exclude questionable samples from the analysis and accurately predict true samples with 100 % correct classifications from a set of artificially contaminated samples containing elements characteristic of GSR, highlighting the protocol’s significant reduction in the likelihood of false positives.

GGR Handbook of Rock and Mineral Analysis Chapter 13 Laser‐Induced Breakdown Spectroscopy (LIBS)

This chapter (Laser‐Induced Breakdown Spectroscopy (LIBS)) is a contribution to the Geostandards and Geoanalytical Research Handbook of Rock and Mineral Analysis – an online textbook that is a fully revised and updated edition of A Handbook of Silicate Rock Analysis (P. J. Potts, 1987, Blackie, Glasgow).Chapter 13 (from Section 3 of the handbook dealing with microbeam techniques) provides first a history of the development of laser‐induced breakdown spectroscopy, and of the LIBS process, followed by an examination of the fundamental principles of LIBS and its instrumentation. Discussion is then provided on the preparation of sample material, LIBS matrix effects and signal processing. Different modes of compositional analysis that can be tackled by LIBS are described, including quantitative measurement (covering isotope measurements), compositional mapping, depth profiling and the determination of physical properties of geological materials. The recent tandem coupling of LIBS with laser ablation ICP‐MS instrumentation is explored. Finally, a suite of examples of LIBS analyses of silicate rocks and minerals is provided, demonstrating the utility of this measurement principle in rapid compositional assessment, detailed petrological studies and microgeochemical mapping.

Enhancing silicon spectral emission in LIBS using Tesla coil discharge

Laser-induced breakdown spectroscopy (LIBS) is a powerful technique for elemental analysis, offering rapid analysis, minimal sample preparation, wide elemental coverage, and portability. To enhance the detection sensitivity of LIBS, increasing the spectral emission intensity is crucial. This paper explores the use of Tesla coil (TC) discharge as an alternative to spark discharge in silicon LIBS. The study examines the influence of TC discharge on both time-integrated and time-resolved spectra, with and without TC discharge; the corresponding electron temperature and density are obtained. The results show that TC discharge significantly amplifies the spectral intensity, improving signal sensitivity in LIBS analysis. Specifically, in the laser energy range from 7.4 to 24.0 mJ, TC discharge increased the average spectral line intensities of Si (II) 385.60 nm and Si (I) 390.55 nm by factors of 8.4 and 5.1, respectively. Additionally, the average electron temperature and density were enhanced by approximately 3.2% and 4.2%, respectively, under TC discharge. The advantages of TC discharge include higher energy deposition, extended discharge duration, reduced electrode erosion, and enhanced safety. This research contributes to advancing LIBS technology and expanding its applications in various fields.

Detection of organic carbon in shale by laser induced breakdown spectroscopy and Raman spectroscopy combined with partial least squares methods

In this study, data fusion of laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy was used to characterize the total organic carbon content (TOC) of shale samples in Ordos Basin. The powder samples are pressed into pellets for LIBS and Raman analysis. Partial least squares calibration method was used for quantitative analysis. By comparing the quantitative results of the two techniques and the quantitative results of different spectral fusion strategies, it is found that the fusion strategy of LIBS and Raman spectral data can improve the prediction ability of the model. The model based on PLS latent variable fusion strategy has better fitting effect, its coefficient of determination (R2) for the calibration sets is 0.9915, the coefficient of determination for the prediction sets is 0.9574 and the root mean square error of the prediction set is 0.5580 wt%. The model has better generalization ability, and the root-mean-square-error-of-cross-validation (RMSECV) of the model is 0.5252 wt%. This study shows that data fusion of LIBS and Raman spectra can achieve rapid analysis of total organic carbon content in shale samples, and may be beneficial to carbon storage research in depleted gas shale.

On-The-Flight trapping, LIBS analysis and discrimination of single meteorite particles generated by laser ablation

BackgroundThousands of micrometeorites fall to the Earth on a daily basis. Most of these meteorites have a rocky composition, but others are mainly composed of iron and nickel. Due to their small size, often ca. 100 μm in diameter, the process of searching for, collecting, and identifying these samples is remarkably tedious. In this work, we introduce a minimally invasive methodology for evaluating the full elemental composition of micrometeorites using optical emission spectroscopy of single particles produced by laser ablation of bulk targets. ResultsBulk meteorite samples were directly ablated within an ablation cell. From few micrograms of ablated matrix, we originated dry aerosols consisting of multielemental particles which were representative of the sample chemistry. SEM images confirmed that the generated particles exhibited spherical geometry. Particles were first optically trapped in air and, then, analyzed by laser-induced breakdown spectroscopy (LIBS). LIBS spectra evidenced compositional differences among samples. For example, Campo de Cielo meteorite featured a high iron content due to its metallic nature whereas LIBS results for Jbilet Winselwan and NWA 869 suggested that pyroxene components dominated the composition of the samples. In contrast, NWA 13739 and Vaca Muerta contained high aluminum intensity, thus indicating that the feldspathic component was dominant, as then verified by XRD. The intra-sample compositional variability were quite satisfactory, as revealed by the RSD data, below 45 %. For quantitative analysis, the percentages of FeO, SiO2, Al2O3, Na2O, MgO, TiO2, CaO, K2O, MnO, SrO, Cr2O3, and Li2O were calculated using CF-LIBS. SignificanceThis work demonstrates the applicability of laser excitation of individual particles in an optical trap for the multielemental analysis of meteorites. The methodology provides a complete overview of the samples, is capable of classificating them according to their main phases and yield preliminary quantitative information about those phases. Therefore, we present a minimally destructive pathway to be used as the first step in the inspection of these delicate samples. If the particles represent nanostructures inherent to the meteorites, it would constitute a major step in the analysis of extraterrestrial material that may provide fundamental insights into the structure and composition of the original materials at the microscale, a topic that remains an active area of research worldwide.

Laser-induced Breakdown Spectroscopy and Multivariate Data Analysis Applied to in-line Sorting of Brominated Plastics from Waste Electric and Electronic Equipment

Laser-induced Breakdown Spectroscopy and Multivariate Data Analysis Applied to in-line Sorting of Brominated Plastics from Waste Electric and Electronic Equipment

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.

Rapid quality control for recycled coarse aggregates (RCA) streams: Multi-sensor integration for advanced contaminant detection

Recycling coarse aggregates from construction and demolition waste is essential for sustainable construction practices. However, the quality of recycled coarse aggregates (RCA) often fluctuates significantly, in contrast to the more stable quality of natural aggregates. Contaminants in RCA notably compromise its quality and usability. Therefore, automating the quality control of RCA is necessary for the recycling industry. This study introduces an industry-focused, innovative, and rapid quality control system that combines Laser-Induced Breakdown Spectroscopy (LIBS) with 3D scanning technologies to enhance the detection of contaminants in RCA streams. The system involves a synchronized application of LIBS for spectral analysis and 3D scanning for the physical characterization of different materials. Results reveal that the dependability of single-shot LIBS analysis has been enhanced, thus elevating the precision of contaminant detection. This improvement is achieved by accounting for the laser shot's angle of incidence and focal length adjustments. The introduced technology holds potential for application in the real-time examination of substantial volumes of RCA, facilitating a rapid and reliable quality control method. This rapid assessment technique delivers online data about the concentration of contaminants in RCA, including recycled fine aggregates, cement paste, bricks, foam, glass, gypsum, mineral fibers, plastics, and wood. This data is both essential and sufficient for choosing a cost-effective mortar recipe and guaranteeing the performance of the final concrete product in terms of strength and durability in construction projects. The system can monitor the quality of RCA flows at throughputs of 50 tons per hour per conveyor, characterizing approximately 4000 particles in every ton of RCA, in this way signaling the most critical contaminants at levels of less than 50 parts per million. With these characteristics, the system could also become relevant for other applications, such as characterizing mining waste or solid biofuels for power plants.

Effect of IR Laser Energy on Several Polymers Using LIBS Analysis

ABSTRACTThe focus of this research is to use the thermal ablation properties of a Nd:YAG infrared laser to highlight the thermal damage caused by the extinction of the plasma, which leads to the disappearance of the spectra of certain polymers as the laser energy increases. The study involved testing five commonly used polymers: polytetrafluoroethylene (PTFE) also known as Teflon, polyoxymethylene (POM), polyvinyl chloride (PVC), Bakelite, and polyamide. Laser induced breakdown spectroscopy (LIBS) analysis was used to qualitatively analyze the plasma generated from the polymer samples, identifying the excited species present in each of the five polymers. Wavelength dispersive X‐ray fluorescence (WDXRF) analysis of the polymer samples further confirmed the identification of these excited species. The results obtained from the spectra recorded at different laser energies in an air environment showed that the saturation observed in the plasma, induced by increasing laser energy, is not consistently observed for all polymers. This plasma extinction phenomenon in certain polymers is attributed to thermal effects when using an infrared (IR) laser as a heating source.

Resolving studies of Balmer alpha lines relevant to the LIBS analysis of hydrogen isotope retention

Utilizing Laser-Induced Breakdown Spectroscopy (LIBS) for detecting deuterium and tritium retention in fusion devices poses a significant challenge due to the experimental limitations in resolving hydrogen isotope Balmer alpha lines (Hα, Dα, and Tα). This study utilizes the Rayleigh criterion to distinguish Tα and Dα lines by determining the maximum line widths and corresponding plasma parameters. Experimental validation was performed through LIBS analysis of heavy water-doped graphite/silica gel targets in both argon and helium atmospheres to assess the predicted plasma parameters and line profile shapes. The optimization of laser pulse energy, gas pressure, delay, and gate times aimed at achieving fully resolved lines based on the intensity, width, and the dip between deuterium and hydrogen Balmer alpha lines. By fine-tuning these experimental parameters, the study successfully achieved a dip of less than 10 % between the Hα and Dα lines with a satisfactory signal-to-noise ratio, demonstrating the feasibility of fully resolving the Tα and Dα lines. These findings underscore the potential of LIBS in enhancing the detection of deuterium and tritium retention in fusion devices.

Significant enhancment of the accuracy of impurity determination in vacuums using classification one-point laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy (LIBS) is a highly promising technique for the in-situ, real-time diagnosis of impurity deposits on the inner walls of tokamak devices. The deposited impurity on plasma-facing materials (PFCs) pose a significant risk to the steady-state operation of the tokamak. Under vacuum conditions, an accurate quantitative analysis of the thin co-deposition layers is a technical challenge. In this study, 30 co-deposited layer samples of tungsten (10.0–92.3 a.t.%), molybdenum (2.0–77.8 a.t.%), iron (2.9–12.1 a.t.%) and copper (1.2–18.7 a.t.%) were prepared to simulate the co-deposition layers found on PFCs in Experimental Advanced Superconducting Tokamak (EAST). A variation of the CF-LIBS algorithm, the so-called One Point Calibrated LIBS (OPC-LIBS), was employed to analyze these co-deposited layer samples under conditions of 5 × 10−5 mbar. It was found that the matrix matching degree among the measured samples and the selection of standard samples play a decisive role in the quantitative analysis capability of OPC-LIBS. In actual situations, the composition of the co-deposited impurity layers at different locations in the Tokamak will be quite different. We addressed this challenge by developing the Classified OPC-LIBS (COPC-LIBS) model, an enhanced version of OPC-LIBS with pre-classification to offset matrix effects in LIBS analysis. For tungsten in the co-deposition layers, the root mean square (RMSE) calculated by the CF-LIBS method was 14.7, the OPC-LIBS method was 11.5, and the newly invented COPC-LIBS was reduced to only 5.1. The COPC-LIBS method is a highly efficient technique that can precisely measure the distribution of co-deposited layers on the surface of inner wall materials. The diagnostic data obtained from this method will provide valuable insights into the interaction between plasma and wall materials during the operation of fusion devices.

Single-shot LIBS: A rapid method for in situ and precise nutritional evaluation of hydroponic lettuce

Hydroponic farming has emerged as a promising method that can enable year around crop production, particularly in regions with non-arable land. Ensuring precise control over nutrient levels and growing conditions is imperative for optimizing crop quality and nutritional value. However, the existing state-of-the-art nutrient assessment methods demand tedious sample preparation and often prove to be either destructive or offline, lacking in options for in situ monitoring. Previous approaches to nutritional evaluation using laser-induced breakdown spectroscopy (LIBS) utilized multiple laser shots or labor-intensive sample preparation to achieve enhanced sensitivity. In this context, we propose a single-shot LIBS system with a custom-made optical collection unit coupled to spectrograph to improve sensitivity and reduce sample damage by employing low excitation energy levels (~ 1.5 mJ). This study demonstrates in situ nutrient monitoring of hydroponically grown lettuce leaves and roots using single-shot LIBS analysis, paving the way for enhanced crop cultivation practices and improved agricultural productivity. Additionally, we discuss energy optimization strategies aimed at improving sensitivity and achieving a high signal-to-background ratio, which are essential for effective and safe nutrient monitoring and analysis in hydroponic farming systems. The results and analysis reveal that highly reproducible and sensitive LIBS spectra can be obtained directly from lettuce plants without any prior sample preparation.Graphical

Classification of kimchi using laser-induced breakdown spectroscopy and k-nearest neighbors modeling

This study introduces a novel application of Laser-Induced Breakdown Spectroscopy (LIBS) combined with k-nearest neighbors (KNN) modeling to classify the origin of kimchi. Using the spectral intensities of Mg II at 279 nm and K I at 766 nm, we achieved a classification accuracy of 92.8 %. This method effectively leverages regional differences in salt supply chains impacting kimchi's elemental composition. The innovation lies in applying the interclass distance method for variable selection in LIBS analysis, enhancing the interpretability and accuracy of food classification. Compared to traditional elemental analysis techniques, LIBS offers a practical, cost-effective solution for rapid field analysis with minimal sample preparation. This study not only demonstrates the potential of LIBS for food authenticity but also provides insights for developing accurate methods for detecting Mg and K in various food products, contributing to advancements in food quality control.

Application of KF/waste glass catalyst in the synthesis of fatty acid esters under pressurized conditions without glycerol generation

In this study, the efficiency of applying the KF/waste glass catalyst was tested in the transesterification of crambe oil under pressurized conditions for the synthesis of esters and glycerol carbonate (GLC). For this, a miscella pressurized extraction was used. The influence of temperature on different residence times was evaluated, and to verify the effect of the catalyst on the process, non-catalytic reactions were conducted. In reactions without the use of catalyst, a temperature of 250 °C was required to obtain >90 % esters yield. In the catalytic reactions, the operating conditions were reduced to 225 °C, to obtain ∼96 % esters yield, also providing the synthesis of a greater quantity of GLC, and a glycerol-free sample. The wet modification of powdered glass waste with KF results in the K2SiF6, NaF, CaF2, and KCaF3 phases. The laser-induced breakdown spectroscopy analyses showed the presence of fluorinated compounds in the catalyst. Furthermore, calcium and sodium ions from the glass waste matrix support were the source for the crystalization of K2SiF6/CaF2/KCaF3 and NaF phases, respectively. The catalyst showed a good catalytic activity, promoting high ester and GLC formation at 225 °C, 10 min and 10 MPa, remaining with good performance even after 8h of reaction, but it is important to mention that the stability of the catalyst needs to be improved due to the leaching of K to Ca.

Scalable solution for agricultural soil organic carbon measurements using laser-induced breakdown spectroscopy

Effective verification of soil organic carbon (SOC) improvement interventions through soil carbon sequestration (SCS) requires robust methodologies to measure, report, and verify changes in soil carbon (C) levels. Furthermore, soil C must be monitored over time to ensure that sequestered C is not being re-emitted, thus ensuring the permanence of C removals. The traditional methods for soil C measurement are time-consuming, labor-intensive, and energy-intensive, increasing analysis costs. In this article, we verify the use of a commercially available laser-induced breakdown spectroscopy analyzer, the LaserAg-Quantum, coupled with the recursive feature addition, the gradient-boosted decision trees regression model, and the novelty detection model to predict C in soils. The developed method shows promising performance with an average limit of quantification of 0.75% of C and a precision of 4.10%. Accuracy metrics, including R2, mean absolute error, and root mean square error, yielded values of 0.81, 0.27%, and 0.37% for the validation dataset. Additionally, around 10% of validation samples after the novelty detection model exhibited relative error greater than 30%. Finally, our findings demonstrate the potential of the LaserAg-Quantum process to support measuring SOC in agricultural soils on a large scale.

Elemental analysis and metabolic profiling of medicinally potent members of Zingiberaceae family using FT-IR and LIBS coupled with PLS-DA

The role of organic and inorganic elemental profiles in the growth, development, and secondary metabolite synthesis of plants is crucial, particularly concerning their medicinal value. However, comprehensive studies addressing both aspects are scarce. Hence, the present manuscript aims to investigate the potential use of Fourier transform infrared spectroscopy (FT-IR) and laser-induced breakdown spectroscopy (LIBS) techniques to obtain the functional groups and organic and inorganic elemental profiles of significant medicinal plants belonging to the Zingiberaceae family collected from two different geographic regions in India. The FT-IR analysis of the methanolic extracts shows the presence of aliphatic and aromatic alcohols, esters, ethers, carboxyl compounds, and their derivatives. In LIBS analysis, the spectral characteristics of atomic and molecular species present in the samples were observed, encompassing both organic and inorganic elements. The presence of heavy metals and trace elements have also been observed in the LIBS spectra of the samples. Furthermore, partial least squares discriminant analysis (PLS-DA) has been used to obtain classification pattern of the samples based on their spectral fingerprints. This study not only helps in reflecting the significance of micronutrients in aiding secondary metabolism thus enhancing the medicinal properties of plants, but also enables the identification of trace elements within plants. This facilitates the determination of the suitable usage and dosage of particular plant components, contributing to the research goal of establishing pharmacological and nutraceutical significance. This study is imperative as it fills a critical gap in research, although further work in this direction is warranted.

Laser-Induced Breakdown Spectroscopy Analysis of Sheet Molding Compound Materials

Sheet Molding Compound (SMC) materials are extensively utilized as high-voltage insulation materials in electrical equipment. SMC materials are prone to aging after long-term operation. Conducting non-destructive testing to assess their electrical and physicochemical properties is crucial for the safe operation of electrical equipment. This study identifies the optimal equipment parameters for testing SMC materials using Laser-Induced Breakdown Spectroscopy (LIBS) technology through experimental investigation and also explores the ablation characteristics of SMC under various laser parameters. The results indicate a significant positive correlation between the ablation depth and laser pulse number, while there is no correlation with single laser pulse energy. However, the ablation area demonstrates a strong positive correlation with both single laser pulse energy and laser pulse number. Additionally, LIBS spectral analysis provides elemental results comparable to Energy Dispersive Spectroscopy (EDS), facilitating the examination of variations in Na, Ti, Fe, Mg, Ca, and C elemental contents with depth. Moreover, an enhanced iterative Boltzmann plot method is suggested for calculating the plasma temperature using 21 Fe I spectral lines and the electron density using the Fe II 422.608 nm line. The variations of these plasma parameters with laser pulse number are documented, and the results show consistent trends, confirming that the laser-induced SMC plasma adheres to local thermodynamic equilibrium.

Quasi-Non-Destructive Estimation of Concrete Compression Strength Using Laser-Induced Breakdown Spectroscopy and Multivariate Analysis.

Micro- and non-destructive methods of estimating compressive strength are useful for diagnosing the degradation of reinforced structures. The velocity of waves propagating through concrete can be measured using conventional non-destructive methods; however, the propagation path of waves varies depending on the distribution of coarse aggregate, resulting in variations in velocity at different measurement points. To address this issue, a method based on laser-induced breakdown spectroscopy and multivariate analysis was developed in this study for estimating the compressive strength of concrete non-destructively, ensuring the non-influence of the coarse aggregate spatial distribution. The method is based on the correlation between the emission intensity of the spectrum and the hardness of the object to be measured. Principal component analysis and partial least squares regression (PLSR) were used to extract the mortar spectrum, which determines the compressive strength of concrete, from a mixture of aggregate and mortar spectra. The compressive strength estimated based on the proposed method was consistent with the values obtained from the compressive strength test, which indicates the possibility of using multivariable analysis to estimate the compressive strength of concrete. Furthermore, the proposed method enabled on-site measurements through a simple experimental setup and insensitivity to spectral noise offered by PLSR.

Multi-component quantitative analysis of LIBS using adaptively optimized multi-branch CNN

Multi-component quantitative analysis of laser-induced breakdown spectroscopy (LIBS) is very important for industrial process control, food safety and space exploration. Due to the strong background interference and complex matrix effect, traditional quantitative analysis methods are difficult to realize accurate multi-component regression. Deep learning has the advantages of automatically extracting full-spectrum features and strong nonlinear modeling capabilities, which provides new opportunities for advancing the progress of LIBS quantitative analysis. However, due to the large difference of spectral line intensity and spectral cross-interference, existing deep learning models have limited performance for simultaneous multi-component regression. To this end, a novel adaptively optimized multi-branch convolution neural network (AOMB-CNN) framework is proposed for multi-component quantitative analysis of LIBS. Multi-branch structure is adopted to ensure the accuracy of multi-component content simultaneously. Dynamic weighted loss function and Bayesian optimization algorithm are used to adaptively optimize the deep learning model. The results demonstrate that the AOMB-CNN can effectively improve the performance of multi-component regression on 2 LIBS datasets compared with the traditional quantitative analysis methods. The root mean square error (RMSE) of 8 components of the ChemCam test set are 2.4465, 0.3486, 1.2481, 1.3909, 0.9010, 0.8892, 0.6700, and 0.5498 respectively, and the RMSE of 4 components of the steel test sets are 1.9787, 0.3455, 0.6025 and 1.6631 respectively. The proposed method provides an end-to-end, adaptively optimized, full-spectrum deep learning solution for LIBS multi-component content measurement without human intervention.

Quantification of regulated metals in recycled post-consumer polypropylene through comparative ICP-MS, AAS and LIBS analyses

Knowing the exact heavy metal load of recycled plastics is important for their risk assessment. We therefore established a novel strategic hierarchy for testing the heavy metal load of recycled plastics. For preliminary screening for unusually high elemental loads, laser-induced breakdown spectroscopy (LIBS) is suggested, while inductively coupled plasma-mass spectrometry (ICP-MS) or electrothermal atomic absorption spectroscopy (ETAAS) are used for exact quantification. The contents of ten regulated elements (Hg, As, Cd, Cr, Co, Cu, Se, Pb, Sn and Ni) in post-consumer (PCR), post-industrial (PIR), and virgin polypropylene (PP) were thus determined. Concentrations in the PCR were mostly found to be two orders of magnitude smaller than their threshold in non-food applications. Cu was most abundant in PCR with 18.7 ± 6.1 mg/kg. Concentration variations of approx. 30 %. were found within a PCR batch, while between batches, slightly higher variations of 30–40 % were observed. Moreover, the heavy metal concentration patterns of PCR and PIR differ significantly. This strategy may enhance the applicational possibilities for PCR.