Search Results (431)

In response to the demands for planetary material detection, in this study, we propose an optical system for a compact remote Raman–LIBS (CRBS, Laser-Induced Breakdown Spectroscopy) combined spectrometer based on liquid lens focusing. This system adopts a design approach incorporating liquid lens focusing, a shared pulsed excitation source, and a common optical path for both transmission and reception. Compared to existing international combined Raman–LIBS spectrometer systems, the proposed optical system is more compact and achieves integrated Raman and LIBS detection capabilities, thereby facilitating system miniaturization and enhancing detection efficiency. This system represents a promising approach for compact, robust remote surface analysis instruments for terrestrial and planetary science. This study provides a theoretical foundation for achieving stable in-orbit detection in lunar material exploration and other long-distance signal detection missions. Full article

To address the recognition challenges caused by blurred state boundaries and the limitations of single monitoring modalities during aircraft skin laser paint stripping, this study proposes a multimodal data fusion method for state recognition based on laser-induced breakdown spectroscopy (LIBS) and surface imaging. By constructing a synchronous monitoring platform, a dataset covering five key physical states, namely topcoat (Tc), topcoat–primer transition (Tc-Pr), primer (Pr), primer–substrate transition (Pr-As), and substrate damage (As), was established. The proposed gated weighted multimodal fusion network (PGMF-Net) employs SE-ResNet1D to capture variations in elemental composition features from the spectra and integrates ResNet18 to extract changes in surface morphology from the images. The experimental results show that the proposed model outperforms the single-modal methods as well as the compared early-fusion and late-fusion methods, achieving a recognition accuracy of 94.12% on the test set and an average accuracy of 94.87% in stratified cross-validation. The bootstrap-based confidence interval analysis further verifies the stability of this method under the current dataset conditions. Further analysis indicates that the single-spectrum model has difficulty effectively distinguishing coating transition states because different transition states contain identical or highly similar characteristic peak information. The single-vision model, however, shows insufficient sensitivity to subtle substrate damage, whereas multimodal fusion enables complementary representation of material composition information and surface morphological information. Experimental validation under different power conditions further confirms that the model outputs are generally consistent with the macroscopic morphological evolution observed on the sample surface. This method compensates for the limitations of traditional single-source monitoring and provides a methodological foundation for online monitoring and state feedback during the laser paint stripping process. Full article

In recent years, pesticides have been widely applied in the commercial cultivation of traditional Chinese medicinal plants to increase the yield of medicinal materials. Xinhui dried tangerine peel (Citri Reticulatae Pericarpium), a common ingredient in traditional Chinese medicine, utilizes the citrus peel as its medicinal part. During cultivation, the peel is directly exposed to pesticides, making it susceptible to pesticide residue accumulation. To enable the rapid identification of pesticide types and their targeted removal, this study integrated laser-induced breakdown spectroscopy with ensemble learning algorithms. Three lightweight neural network models—1D-CNN, Res-CNN, and LIBS-UNet—were developed and trained using either a single loss function or a composite loss function. The 1D-CNN, Res-CNN, and LIBS-UNet models achieved accuracies of 97.50% and 98.69%, 95.00% and 95.73%, and 74.06% and 76.88% for the single loss and composite loss functions, respectively. During the model ensemble stage, individual models were weighted according to their classification accuracy and test similarity matrices. Through this approach, the pesticide identification accuracy reached 99.99%. This study demonstrates that ensemble learning can effectively integrate the strengths of multiple weak classifiers, thereby significantly enhancing classification performance and providing a novel approach for the rapid detection of pesticide residues in traditional Chinese medicine ingredients. Full article

This study demonstrates the application of Laser-Induced Breakdown Spectroscopy (LIBS) for the elemental analysis of agricultural soils in South Punjab, Pakistan. Soil degradation due to intensive farming, imbalanced fertilizer use, and declining organic matter has reduced crop productivity in the region. To address this, rapid and accurate soil diagnostics are essential. LIBS, coupled with Calibration-Free analysis (CF-LIBS), was employed to quantitatively determine the concentrations of major and trace elements—including calcium, silicon, iron, aluminum, magnesium, titanium, potassium, sodium, lithium, and barium—without requiring chemical standards. Plasma characterization was performed using the Boltzmann plot method, yielding temperatures between 7750 and 9000 K, and electron number densities were derived from Stark-broadened spectral profiles. The results reveal significant spatial variability in elemental composition, reflecting differences in land use and irrigation sources. This work confirms LIBS as a versatile, efficient, and reliable tool for soil health assessment, offering a practical solution for monitoring soil nutrients and supporting sustainable agricultural management in resource-limited settings. Full article

Electric energy metering cabinets serve as critical nodes in power grid operations, providing essential protection for key components in distribution networks. Under environmental stressors, the non-metallic casings of electric energy metering cabinets are susceptible to aging-induced performance degradation, which may result in electrical safety hazards. However, rapid and precise methods for evaluating the performance of these non-metallic casings are still lacking. Laser-Induced Breakdown Spectroscopy (LIBS), capable of rapid multi-element detection with non-contact analytical advantages, was employed in this study. Thermal aging experiments were conducted to investigate the performance degradation mechanisms of sheet molding compound (SMC)—a representative non-metallic cabinet material. The research analyzed time-dependent trends in material performance and microstructural evolution during aging. By integrating LIBS with multi-analytical techniques, this study further explored the feasibility of quantitatively evaluating the bending strength of thermally aged SMC, which has rarely been reported in previous studies. Based on LIBS spectral data, bending strength characterization revealed its attenuation patterns with aging duration. The relationships between bending strength and plasma temperature, as well as the characteristic line intensity ratios of K, Al, and Ca, were systematically examined. A multivariate linear regression model incorporating these key variables was subsequently developed, yielding a high coefficient of determination (R² = 0.9657) between the predicted and measured bending strength values. This model represents a promising initial step, but further validation with a larger dataset is necessary to enhance its reliability and generalizability. Full article

This study presents a comparative evaluation of multiple-approach optical spectroscopic sensor—Laser-Induced Breakdown Spectroscopy (LIBS), Mid-Infrared Spectroscopic sensing (MIRS), and Hyperspectral Imaging (HSI)-based sensors operating in the Visible–Near-Infrared (VNIR) and Short-Wave Infrared (SWIR) ranges—for the quantitative detection of calcium carbonate (CaCO₃) in pelletized CaCO₃-CaO mixtures. The objective was to assess and compare the sensing performance of these optical sensor platforms for carbonate quantification. Each spectroscopic sensor dataset was processed using chemometric calibration methods, including Partial Least Squares Regression (PLSR), to ensure robust and reproducible quantitative predictions. Although the samples consisted of binary CaCO₃-CaO mixtures, the sensing task focused exclusively on CaCO₃ content. Results indicate that LIBS, MIRS, and HSI-SWIR-based sensing approaches achieved comparable quantitative performance, with LIBS providing the highest prediction accuracy. In contrast, the HSI-VNIR sensor configuration demonstrated lower predictive capability relative to the other optical sensing modalities. These findings highlight the potential and limitations of different optical sensor technologies for carbonate detection in heterogeneous mineral systems. Full article

Accurate classification of uranium slag origin and category is essential for nuclear environmental monitoring and safety. This study presents a hybrid framework combining laser-induced breakdown spectroscopy (LIBS), four preprocessing methods, and five machine learning algorithms for rapid uranium slag classification. A total of nine sample categories were collected from three mining areas, with categories defined by their U concentration levels within each origin. Standard normal variate (SNV), Savitzky–Golay smoothing (SG), and their combinations (SNV-SG, SG-SNV) were applied to evaluate preprocessing effects. To address ultra-high-dimensional spectral data (49,242 points per spectrum), principal component analysis (PCA) and random forest (RF) were employed for feature engineering, integrated with support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbors (KNN) classifiers. Hyperparameter optimization via five-fold cross-validation and Bayesian optimization enhanced accuracy and efficiency. RF-based hybrid models consistently outperformed PCA-based counterparts. Remarkably, the RF-LDA model with SNV-SG preprocessing achieved 100% classification accuracy across all test sets with a processing time of only 10.46 s, demonstrating exceptional discriminative power and computational efficiency. These findings establish that combining RF feature selection with advanced machine learning offers a robust solution for LIBS-based nuclear material classification, with significant implications for both nuclear safety and resource management. Full article

Total nitrogen (TN) is a critical indicator of water eutrophication. Conventional detection methods (e.g., alkaline potassium persulfate digestion and the Kjeldahl method) suffer from complex sample preparation, time-consuming operations, and reagent-induced pollution. Laser-induced breakdown spectroscopy (LIBS) offers unique advantages for rapid water quality analysis, yet it predominantly relies on costly actively Q-switched lasers, with passive Q-switching rarely explored due to multi-pulse output instability. This study employed a passively Q-switched laser as the excitation source for water TN measurement. By optimizing the multi-pulse trigger position, the signal-to-background ratio (SBR) was effectively enhanced. Combined with the substrate liquid–solid conversion method, key parameters (trigger delay, laser energy, argon flow rate) were optimized. Laboratory measurements of KNO₃ standard solutions (0–25 mg/L) using cyanogen (CN) molecular spectral lines yielded a coefficient of determination (R²) of 0.98, and a limit of detection (LoD) of 2.19 mg/L. Tests on actual water samples showed relative deviations ranging from 3.93% to 6.39%. These results demonstrate that passively Q-switched LIBS is a viable, cost-effective solution for rapid water nitrogen detection. Full article

At the end of its last experimental campaign, in December 2023, the Joint European Torus (JET) became available for testing a compact and lightweight Laser-Induced Breakdown Spectroscopy (LIBS) system to be mounted on its robotic arm. The purpose of the test was the in situ chemical characterization of its internal walls and plasma-facing components (PFCs). Among the areas measured, special attention was devoted to the PFCs of the divertor, as this area is most affected by the re-deposition of material eroded from the first wall and unburned nuclear fuel (deuterium and tritium). In this article, we present the results of the LIBS characterization of a PFC of the High Field Gap Closure (HFGC), highly subjected to these phenomena. The in-depth distribution of several ITER-relevant chemical species is discussed through in-depth and correlation analyses, and the interpretation of the results is explained in terms of erosion and re-deposition of materials from the first wall. The study allowed us to estimate the thickness of the ablated layers by each laser shot, which is on the order of a few tens of nanometers, and to outline a mapping of the thickness of the re-deposited material. Full article

A holmium-doped yttrium aluminum garnet (Ho:YAG) thin disk was experimentally investigated under Q-switching and cavity-dumping operation schemes, pumped by a 1.9 µm laser diode (LD). The laser generated pulses at 2090 nm with energies more than 6 mJ and pulse duration down to 3.8 ns, corresponding to a peak power of 1.6 MW with near-diffraction-limited beam quality. The compact and robust system was used for laser-induced breakdown spectroscopy (LIBS) experiments, demonstrating its practical usability. These results represent, to the best of our knowledge, the first demonstration of a Ho:YAG thin-disk laser providing MW peak power in the nanosecond regime. Full article

Background: Deep vein thrombosis (DVT) is one of the most common cardiovascular diseases and is especially prevalent in areas with environmental pollution. Bioaccumulation of toxic heavy metals may lead to deterioration of homeostasis with cellular change, endothelial dysfunction, DNA impairment and cellular signaling. The reason for this is usually the accumulation of thrombogenic toxins in the body as a result of long-term exposure or a lack of regulatory gene expression. In this study, we aimed to measure the minerals that potentially accumulate in the nail. The measurement method was laser-induced breakdown spectroscopy (LIBS), which is a form of atomic emission spectroscopy. It uses a highly energetic laser source to form a plasma of excited atoms emitting light of characteristic wavelengths. It provides accurate quantification and reveals the relationship between tissue accumulation of toxic heavy metals and DVT formation. Methods: Between January 2020 and December 2021, 100 patients diagnosed with lower-extremity deep vein thrombosis were screened in a single tertiary healthcare center. Among them, 50 patients who met the eligibility criteria and consented to participate were included in the study. An additional 50 age-matched healthy volunteers were enrolled as controls. Demographic and clinical characteristics were recorded. Nail samples were obtained from each participant, and elemental emission intensities were quantitatively analyzed using laser-induced breakdown spectroscopy (LIBS). Results: No difference in clinical characteristics was detected between the groups. While iron, calcium and silicon were found to be high in DVT patients, magnesium was found to be low. Regarding the magnesium emission, ROC analysis showed 76–90% specificity and 69–82% sensitivity, respectively. Conclusions: LIBS is a useful method because it is easy to use and can be used with a small sample. According to the results of our study, information about the pathogenesis of DVT was obtained through nail analysis. Therefore, we believe that LIBS analysis is a method that may be useful in determining the causes and predisposing factors for DVT. Full article

Efficient sorting of resin-based CFRP composites is critical for optimizing composite recycling streams. In this work, a methodology integrating Laser-Induced Breakdown Spectroscopy (LIBS) with Machine Learning (ML)-enhanced classification models to achieve accurate material discrimination is presented. LIBS is employed to identify the chemical composition of individual compounds, producing spectrograms that are subsequently processed to group chemically similar materials based on Epoxy resin (Bisphenol-A). The grouped datasets that contain 4000 peaks and 665 features were sampled to standardize feature dimensionality and cleaned to remove noise. A statistical analysis is then conducted to select the most informative features, followed by dimensionality reduction using Linear Discriminant Analysis (LDA). Finally, classification is performed using a Support Vector Classification (SVC) model, fine-tuned to the processed data to maximize accuracy. With a 5-fold cross validation (CV), the average nested accuracy score is 0.8317 ± 0.0212. This integrated approach demonstrates the potential for advancing automated sorting technologies in composite recycling applications. Full article

Estuaries are dynamic environments that influence the transport of metals and nutrients from land to sea, with suspended particulate matter (SPM) serving as a key vehicle for them. Laser-Induced Breakdown Spectroscopy (LIBS) offers a rapid, versatile, and non-destructive approach for multi-element analysis of SPM, allowing their direct measurement on collected filters without complex sample preparation. In this study, LIBS was applied to evaluate the spatio-temporal variability of major and trace elements (Si, Fe, Al, Ti, Na, Li, K, Rb, Ca, and Mg) along the Pacoti River estuary, Brazil, during rainy and dry seasons. Elemental patterns generally reflected the salinity gradient and tidal dynamics, highlighting element-specific behaviors with most elements showing inverse correlations with salinity, while Ca and Mg displayed positive correlations. These findings confirm the potential of LIBS as a powerful tool for environmental monitoring, providing rapid, high-throughput characterization of SPM and enabling an improved understanding of biogeochemical processes in estuarine systems. Full article

With the rapid development of the steel industry, the accurate detection of alloy element contents is of great significance for the evaluation of material properties and quality control. This study aims to establish a rapid, stable, and highly accurate quantitative detection method based on handheld LIBS to achieve effective analysis of key elements such as Fe, Cr, Mn, Ni, and Cu. To meet the demand of the steel industry for rapid, stable, and high-accuracy quantification of key alloy elements such as Cr, Mn, Ni, and Cu, this study was carried out on 20 types of standard steel spectral samples. Support Vector Regression (SVR) and Partial Least Squares Regression (PLSR) models were constructed, respectively. The SVR penalty factor C (0.1–10) and loss parameter ε (0.001–1), as well as the PLSR latent variable number Lv (1–20), were optimized using five-fold cross-validation repeated 100 times. Model performance was evaluated using the coefficient of determination (R²), root-mean-square error (RMSE), and mean relative error (MRE). In the comparison of quantitative performance, excellent predictive ability for major elements such as Fe and Cr was achieved by both models; test-set R² values exceeded 0.92, meeting the detection requirements for high-content alloy elements. For low-content Ni, Cu, and Mn, PLSR gives R² values of 0.92, 0.93, and 0.89, while SVR yields 0.85, 0.49, and 0.36, showing clear limitations, especially for Cu and Mn. After introducing Multilayer Perceptron feature extraction, the R² of Ni, Cu, and Mn increases to 0.99, 0.99, and 0.97 for PLSR and to 0.99, 0.93, and 0.94 for SVR, with RMSE and MRE markedly reduced. In summary, the integration of LIBS with MLP feature extraction and PLSR offers both rapid processing capabilities and high precision, significantly improving the quantification of low-concentration elements, and is well-suited for real-time online monitoring in steel production, facilitating quality control and process optimization. Full article