A Review of Calibration-Free Laser-Induced Breakdown Spectroscopy

Heavy metals high-sensitive detection by laser-induced breakdown spectroscopy based on radial electroosmotic flow-driven enrichment

Heavy metal detection is imperative for human health and environmental sustainability. However, the commonly used liquid sample pretreatment, drying liquid droplet to solid, encounters solute diffusion and nonuniform distribution, thus causing unpromising detection results. Here, we developed a radial electroosmotic flow-driven (REOF) platform to enrich heavy metals in water for high-sensitive detection using laser-induced breakdown spectroscopy (LIBS). Firstly, the electrodes in the substate for REOF were designed and produced by the printed circuit board manufacturer. Different particle deposition patterns were observed by modifying the direction and magnitude of voltage in the evaporated droplets of Cadmium Chloride (CdCl2) on the substrate. Then, the two-dimensional model of the evaporating droplets with REOF was established to verify the experimental phenomenon. The CdCl2 (10-50 mg/L) and Manganese Chloride (MnCl2, 1-8 mg/L) solutions were quantitatively analyzed with the optimized parameter on the substrate by LIBS. The detection limits of Ca and Mn can be reduced by approximately 42 times with REOF substrates by LIBS. Finally, the Mn in the real underground water sample was tested with the REOF substrate by LIBS, and the relative error was 5.5% compared with the results of ICP-MS. The results demonstrated that the REOF can enrich and uniformly distribute the solute on the substrate, and be helpful for the analysis of heavy metals in solution with LIBS.

A Calibration-Free Optical Emission Spectroscopic Method to Determine the Composition of a Spark Discharge Plasma Used for AuAg Binary Nanoparticle Synthesis

Spark discharge generators (SDGs) employ controlled gaseous environments to induce spark ablation of non-insulating electrodes, resulting in the formation of various nanostructures in the gas phase. The method offers technological advantages such as continuous particle production, scalable yield, and minimal waste. Additionally, the versatility of the process enables the generation of alloy nanoparticles from various material combinations, including immiscible ones. In order to fully exploit its potential, understanding the atomic mixing process during electrode ablation, particularly in the case of dissimilar electrodes, is crucial. Temporally and spatially resolved optical emission spectroscopy (OES) has been previously demonstrated as an effective characterization tool for spark plasmas in SDGs. However, to gain a deeper insight into the vapor mixing process, it is essential to quantitatively determine the plasma composition in both space and time. This paper introduces a calibration-free OES-based method tailored for spark plasmas utilized in binary nanoparticle generation. The method introduces the so-called multi-element combinatory Boltzmann plots, which use intensity ratios of emission atomic lines from different materials, allowing for the direct estimation of total number concentration ratios. The approach is tested using synthetic spectra and validated with experimental spark spectra obtained near an alloyed gold-silver (AuAg) electrode with a known composition. The study demonstrates the capabilities and robustness of the proposed method, with a focus on the AuAg system due to its significance in plasmonic research and frequent synthesis using spark ablation.

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.

Adversarial Data Augmentation and Transfer Net for Scrap Metal Identification Using Laser-Induced Breakdown Spectroscopy Measurement of Standard Reference Materials

In this study, we propose a transfer learning-based classification model for identifying scrap metal using an augmented training dataset consisting of laser-induced breakdown spectroscopy (LIBS) measurement of standard reference material (SRMs) samples, considering varying experimental setups and environmental conditions. LIBS provides unique spectra for identifying unknown samples without complicated sample preparation. Thus, LIBS systems combined with machine learning methods have been actively studied for industrial applications such as scrap metal recycling. However, in machine learning models, a training set of the used samples may not cover the diversity of the scrap metal encountered in field measurements. Moreover, differences in experimental configuration, where laboratory standards and real samples are analyzed in situ, may lead to a wider gap in the distribution of training and test sets, dramatically reducing the performance of the LIBS-based fast classification system for real samples. To address these challenges, we propose a two-step Aug2Tran model. First, we augment the SRM dataset by synthesizing spectra of unobserved types through attenuation of dominant peaks corresponding to sample composition and generating spectra depending on the target sample using a generative adversarial network. Second, we used the augmented SRM dataset to build a robust real-time classification model with a convolutional neural network, which is further customized for the target scrap metal with limited measurements through transfer learning. For evaluation, SRMs of five representative metal types, including aluminum, copper, iron, stainless steel, and brass, are measured with a typical setup to form the SRM dataset. For testing, scrap metal from actual industrial fields is experimented with three different configurations, resulting in eight different test datasets. The experimental results show that the proposed scheme produces an average classification accuracy of 98.25% for the three experimental conditions, as high as the results of the conventional scheme with three separately trained and executed models. Additionally, the proposed model improves the classification accuracy of arbitrarily shaped static or moving samples with various surface contaminations and compositions, and even for differing ranges of charted intensities and wavelengths. Therefore, the proposed Aug2Tran model can be used as a systematic model for scrap metal classification with generalizability and ease of implementation.

Self-absorption correction method for one-point calibration laser-induced breakdown spectroscopy

As an important variant of calibration-free laser-induced breakdown spectroscopy (CF-LIBS), one-point calibration LIBS (OPC-LIBS) corrects the Boltzmann plot of the unknown sample by using one known sample and obtains higher quantitative accuracy than CF-LIBS. However, the self-absorption effect restricts its accuracy. In this work, a new self-absorption correction (SAC) method for OPC-LIBS is proposed to solve this problem. This method uses an algorithm to correct the self-absorption and does not require the calculation of the self-absorption coefficient. To verify the effectiveness of this SAC method, Ti, V, and Al elements in two titanium alloys were determined by classical OPC-LIBS and OPC-LIBS with SAC. The average relative errors (AREs) of all elements in the two samples were decreased from 8.78% and 9.28% to 8.07% and 7.56%, respectively. The results demonstrated the effectiveness of this SAC method for OPC-LIBS.

Towards Automated Classification of Zooplankton Using Combination of Laser Spectral Techniques and Advanced Chemometrics

Zooplankton identification has been the subject of many studies. They are mainly based on the analysis of photographs (computer vision). However, spectroscopic techniques can be a good alternative due to the valuable additional information that they provide. We tested the performance of several chemometric techniques (principal component analysis (PCA), non-negative matrix factorisation (NMF), and common dimensions and specific weights analysis (CCSWA of ComDim)) for the unsupervised classification of zooplankton species based on their spectra. The spectra were obtained using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. It was convenient to assess the discriminative power in terms of silhouette metrics (Sil). The LIBS data were substantially more useful for the task than the Raman spectra, although the best results were achieved for the combined LIBS + Raman dataset (best Sil = 0.67). Although NMF (Sil = 0.63) and ComDim (Sil = 0.39) gave interesting information in the loadings, PCA was generally enough for the discrimination based on the score graphs. The distinguishing between Calanoida and Euphausiacea crustaceans and Limacina helicina sea snails has proved possible, probably because of their different mineral compositions. Conversely, arrow worms (Parasagitta elegans) usually fell into the same class with Calanoida despite the differences in their Raman spectra.

Intrinsic Performance of Monte Carlo Calibration-Free Algorithm for Laser-Induced Breakdown Spectroscopy

The performance of the Monte Carlo (MC) algorithm for calibration-free LIBS was studied on the example of a simulated spectrum that mimics a metallurgical slag sample. The underlying model is that of a uniform, isothermal, and stationary plasma in local thermodynamical equilibrium. Based on the model, the algorithm generates from hundreds of thousands to several millions of simultaneous configurations of plasma parameters and the corresponding number of spectra. The parameters are temperature, plasma size, and concentrations of species. They are iterated until a cost function, which indicates a difference between synthetic and simulated slag spectra, reaches its minimum. After finding the minimum, the concentrations of species are read from the model and compared to the certified values. The algorithm is parallelized on a graphical processing unit (GPU) to reduce computational time. The minimization of the cost function takes several minutes on the GPU NVIDIA Tesla K40 card and depends on the number of elements to be iterated. The intrinsic accuracy of the MC calibration-free method is found to be around 1% for the eight elements tested. For a real experimental spectrum, however, the efficiency may turn out to be worse due to the idealistic nature of the model, as well as incorrectly chosen experimental conditions. Factors influencing the performance of the method are discussed.