Laser-induced Breakdown Spectroscopy Technology Research Articles

Design and fabrication of superhydrophobic microstructured grooved substrates to suppress the coffee-ring effect and enhance the stability of Sr element detection in liquids using LIBS.

A new technique has been developed to enhance the stability of laser-induced breakdown spectroscopy (LIBS) in the analysis of dry droplets by mitigating the coffee ring effect (CRE) on substrates with superhydrophobic microstructured grooves. The substrate was prepared from a laser-etched pure copper base, resembling the surface of a lotus leaf, creating a biomimetic superhydrophobic substrate. The superhydrophobic microstructured grooved substrate contained an array of dome-shaped cones with heights of approximately 140 μm and 100 μm, arranged in a periodic pattern of high-low-high. The superhydrophobic properties of the substrate not only evaporation-induced thermal capillary action but also initiated the Marangoni flow, which moves from the periphery to the center of the droplet as it evaporates. This flow mechanism effectively mitigated the CRE by transporting the analyte from the bottom edge of the droplet across its surface to the central peak. To assess how these superhydrophobic microstructured grooved substrates impede the formation of coffee rings, LIBS was deployed to analyze samples from both structured and unstructured grooved substrates. The results indicated that the relative standard deviation (RSD) of the spectral intensity for Sr I at 407.67 nm in substrates with a superhydrophobic microstructured groove edge length of 0.8 mm was 3.6%. In contrast, for the unstructured grooved substrate and a side length of 0.9 mm, the RSD was significantly higher at 25.4%. This research demonstrates that substrates with superhydrophobic microstructured grooves are capable of effectively mitigating the CRE. Additionally, the study examined how the dimensions of these grooves impact the plasma characteristics across two distinct configurations. Based on these observations, calibration curves for Sr were developed using substrates with groove side lengths of 0.6 mm and 0.8 mm. The performance of the superhydrophobic microstructured grooved substrate was satisfactory, exhibiting determination coefficients (R2) of 0.994 and 0.995 for the Sr element. The detection limits (LOD) were notably low at 0.16 μg mL-1 and 0.11 μg mL-1. The average relative standard deviations (ARSD) were 7.2% and 4.9%, respectively. These results demonstrate that the superhydrophobic microstructured grooved substrate effectively mitigates the CRE, thereby enhancing the detection sensitivity and prediction accuracy for heavy metals. This provides a robust reference for selecting platforms using LIBS technology in the pre-treatment process.

Study on enhancing the quantitative performance of LIBS for Cu element in liquids based on the chitosan-parafilm enrichment method

Abstract To enhance the accuracy of laser-induced breakdown spectroscopy (LIBS) in detecting heavy metal elements in solutions, a chitosan (CS)-Parafilm (PM) enrichment method is proposed. This method involves drying chitosan-heavy metal complexes on a Parafilm substrate. During the drying of droplets, electrostatic attraction and chelation by CS effectively concentrate analytes. Additionally, the hydrophobic effect of the PM substrate induces Marangoni flow, which drags analytes from the bottom edge to the central top of the droplet surface, effectively suppressing the coffee ring effect (CRE). Using LIBS technology, spectra were uniformly collected in an array on the surface of sediments. Through the analysis of cumulative excitation spectra and measurement repeatability, the results showed that the RSD of the emission lines Cu I 324.754 nm and Cu I 327.396 nm was reduced to 3.85% and 3.78%, respectively. This indicates that the CS-PM enrichment method allows for uniform deposition of analytes within the samples, effectively suppressing the CRE. Quantitative analysis of Cu elements using the CS-PM enrichment method was conducted using PSO-SVM, PSO-BPNN, and random forest (RF) algorithms. The RF algorithm demonstrated the best predictive performance with Rp 2 of 0.977, root mean square error of prediction of 3.86 mg l−1, mean absolute error of 3.0 mg l−1, and RPD of 5.52. Thus, it can be seen that CS-PM effectively improves the repeatability of spectral measurements and the accuracy of quantitative analysis predictions, providing a technical reference for enhancing the stability and quantitative performance of element analysis in liquids using LIBS.

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.

A rapid method for measuring the rock brittleness index: Rapid characterization of rock brittleness based on LIBS technology

The brittle index is a crucial indicator in the assessment of rockbursts. The mineral brittle index (MBI) is widely utilized due to its simplicity and accessibility. However, the lengthy and inefficient mineral composition testing cycle presents a significant challenge. A novel approach using laser-induced breakdown spectroscopy (LIBS) to rapidly convert spectral elements into minerals and measure the rock brittleness index was introduced in this paper. The laser spectra of metamorphic sandstone and granite were measured by LIBS, and some rock elements were tested by X-ray fluorescence (XRF) to construct a spectral-elemental model. Furthermore, the mineral composition of the rocks was determined by X-ray diffraction (XRD). Eight major elements, Si, Al, K, Ca, Na, Fe, Mg, and Ti, were selected as independent variables. Element-mineral correlation analysis was performed using centered log-ratio transformation (CLR) processing. A random forest regression (RF-R) element-mineral transformation model was established for rapid conversion of spectral-element-mineral brittle index. Finally, mechanical testing of rock embrittlement in the DJ Tunnel was conducted to verify the efficacy of MBI in characterizing rock embrittlement in the DJ Tunnel. The results demonstrate that predicted values of mineral compositions are in good agreement with experimental values and that predicted values of brittleness indices are also in good agreement with experimental values. The rapid and effective application of LIBS in determining rock mineral composition and rock brittleness index has been realised, which is of great significance for further realising the rapid assessment of rock brittleness and rockburst prediction at engineering sites.

Study on the Impact of Air Pressure on the Laser-Induced Breakdown Spectroscopy of Intumescent Fireproof Coatings

Intumescent fireproof coatings protect steel structures and cables by forming a thick, fire-resistant layer under high temperatures. These coatings can deteriorate over time, impacting their fire resistance. Current testing methods are largely lab-based, lacking in-service evaluation platforms. Laser-Induced Breakdown Spectroscopy (LIBS) is emerging as a promising in situ detection technology but is influenced by low air pressure in high-altitude areas. This study investigates how air pressure affects LIBS signals in intumescent coatings on galvanized steel. Using pressures between 35 and 101 kPa, a linear model was developed to correlate laser pulses to ablation depth for characterizing coating thickness. Results show that spectral intensity decreases with lower air pressure. However, a strong linear relationship persists between laser pulses and ablation depth, with a fitting accuracy above 0.9. The coating thickness is identified by the number of laser pulses required to detect the Zn spectral line from the underlying galvanized steel. As air pressure decreases, the ablation depth increases. The study effectively models and corrects for air pressure effects on LIBS data, enabling its application for field detection of fireproof coatings. This advancement enhances the reliability of LIBS technology in assessing the fire performance of these materials, providing a reference for their in situ evaluation and ensuring better fire safety standards for building steel structures and cables.

Quantitative modeling and uncertainty estimation for small-sample LIBS using Gaussian negative log-likelihood and monte carlo dropout methods

Laser-induced breakdown spectroscopy (LIBS) has emerged as a powerful analytical technique widely applied in materials science, environmental monitoring, and other fields. In traditional quantitative analysis, models are established for the sample-to-element content based on acquired characteristic spectra combined with specific modeling methods. Small-sample LIBS poses significant challenges to researchers due to its high dimensionality and limited sample size, making it difficult for traditional modeling methods to establish effectively generalized and robust models. Addressing the issues of overfitting during training and the uncertainty of LIBS data, we developed an artificial neural network (ANN) model. Unlike traditional ANN which uses mean squared error functions as training losses, Gaussian negative log-likelihood (GLL) functions were used to train the model. This approach allows for simultaneous consideration of the quantitative prediction of sample element concentrations and the uncertainty of the quantification model prediction. Moreover, the Monte Carlo Dropout (MCDropout) method was applied to reduce prediction bias, resulting in a more robust, generalizable, and uncertainty-aware quantitative model. Compared to traditional quantitative methods, the method proposed in this paper achieved quantitative accuracies of 0.9877, 0.9939, 0.9876, and 0.9899 for four elements (Mn, Mo, Cr, Cu) in stainless steel, respectively. The uncertainty output effectively informs data comprehension and spectral analysis. To the best of our knowledge, this study is the first to propose utilizing model-quantified spectral uncertainty in LIBS quantitative tasks. This innovative study not only provides solutions for small-sample LIBS learning but also enhances the stability and accuracy of quantitative modeling by addressing the impact of uncertainty on training and reducing uncertainty. It is crucial for overcoming the limitations of traditional methods in small-sample LIBS quantification and achieving reliable real-time, high-precision LIBS detection, taking LIBS technology a step further towards practical reliability and precision.

Detection of defects in composite insulators based on laser‐induced plasma combined with machine learning

AbstractDue to Prolonged operation and external environmental factors, composite insulators may develop various defects, which can potentially lead to serious accidents in power systems. Detecting and analyzing these defects is critically important. In this study, we combine machine learning with laser‐induced breakdown spectroscopy (LIBS) to identify defects in composite insulators and obtain spectral data from both defective and nondefective samples. The Uniform Manifold Approximation and Projection algorithm was employed to reduce the dimensionality of the data, thereby enhancing detection accuracy and efficiency. Decision tree, random forest (RF), K‐nearest neighbor, and support vector machine algorithms were used to classify the dimensionality‐reduced data. The results indicate that the RF algorithm achieved the best classification performance, with accuracies of 100% and 95.46% for the training and test sets, respectively. Furthermore, the precision, recall, and F1 scores, which reflect model performance, also showed superior results with the RF algorithm. These findings suggest that the combination of LIBS technology and machine learning can rapidly and accurately detect and analyze defects in composite insulators, offering new insights and methods for defect detection in composite insulators.

Fresh Meat Classification Using Laser-Induced Breakdown Spectroscopy Assisted by LightGBM and Optuna.

To enhance the accuracy of identifying fresh meat varieties using laser-induced breakdown spectroscopy (LIBS), we utilized the LightGBM model in combination with the Optuna algorithm. The procedure involved flattening fresh meat slices with glass slides and collecting spectral data of the plasma from the surfaces of the fresh meat tissues (pork, beef, and chicken) using LIBS technology. A total of 900 spectra were collected. Initially, we established LightGBM and SVM (support vector machine) models for the collected spectra. Subsequently, we applied information gain and peak extraction algorithms to select the features for each model. We then employed Optuna to optimize the hyperparameters of the LightGBM model, while a 10-fold cross-validation was conducted to determine the optimal parameters for SVM. Ultimately, the LightGBM model achieved higher accuracy, macro-F1, and Cohen's kappa coefficient (kappa coefficient) values of 0.9370, 0.9364, and 0.9244, respectively, compared to the SVM model's values of 0.8888, 0.8881, and 0.8666. This study provides a novel method for the rapid classification of fresh meat varieties using LIBS.

Spectroscopic assessment and quantitative analysis of the trace element composition of vegetable additives to meat products

In this scientific work, using the method of laser-induced breakdown spectroscopy (LIBS), the spectra of beef samples and impurities in meat products, namely, banana, pineapple, kiwi, bergamot, poria coconut, Chinese angelica, chicken blood vine, were measured by using developed experimental devices. The purpose of the research was to evaluate the qualitative characteristics of additives to meat semi-finished products for the potential formation of the desired properties of the products due to the analysis of the received spectrograms of trace elements of the samples when applying the LIBS method, quantitative analysis for processing the received information. The determined values of the electron temperature of the plasma, the electron density of the used raw material samples, and the assessment of the local heat balance were used as evaluation criteria. When processing the obtained data, the characteristics of the laser-induced plasma surface of the presented samples were analyzed; the electron temperature and electron density were determined, and a quantitative analysis of trace elements was carried out. LIBS technology allows rapid real-time monitoring and qualitative analysis of trace elements online and over long distances. During the research, it turned out that quantitative analysis requires further study and optimisation of experimental conditions, such as pre-treatment of samples. These conditions optimise defocusing, double laser pulse, and sample temperature, which increases the signal/noise ratio of all spectral lines. The combination of fluorescence spectroscopy and Raman technology enables higher detection sensitivity and better molecule control, creating a quantitative analysis method model that can reduce matrix effects and overcome the self-absorption effect. Among the difficulties of using LIBS technology, several elements can be noted online simultaneously, compared to Raman. The combination of spectroscopy and fluorescence spectroscopy can obtain more comprehensive information about the composition of materials, which can become a potential platform for monitoring trace elements in food products.

Reassociation of Skeletal Remains Using Laser-Induced Breakdown Spectroscopy.

A major challenge in forensic anthropology and bioarcheology is the development of fast and effective methods for sorting commingled remains. This study assesses how portable laser-induced breakdown spectroscopy (LIBS) can be used to group skeletal remains based on their elemental profiles. LIBS spectra were acquired from the remains of 45 modern skeletons, with a total data set of 8388 profiles from 1284 bones. Spectral feature selection was conducted to reduce the spectral profiles to the peaks exhibiting the highest variation among individuals. Emission lines corresponding to 9 elements (Ca, P, C, K, Mg, Na, Al, Ba, and Sr) were found important for classification. Linear discriminant analysis (LDA) was concurrently used to classify each spectral profile. From the 45 individuals, each LIBS spectrum was successfully sorted to its corresponding skeleton with an average accuracy of 87%. These findings indicate that variation exists among the LIBS profiles of individuals' skeletal remains, highlighting the potential for portable LIBS technology to aid in the sorting of commingled remains.

Improved convolutional neural network-assisted laser-induced breakdown spectroscopy for identification of soil contamination types

Identification of soil contamination types is of great scientific significance for soil remediation and environmental pollution control. However, traditional identification methods for soil determination are time-consuming, laborious, and complicated. Here, we proposed an accurate method for identifying soil contamination types based on laser-induced breakdown spectroscopy (LIBS) and an improved convolutional neural network (CNN) model. The spectral feature extraction-based multiple attention residual network (SFEMARNet) model was constructed to extract detailed features by spectral feature extraction (SFE) modules, and highlight useful features by multiple attention residual (MAR) modules in LIBS spectral data. In addition, deep learning models and machine learning models were used to identify the data. The results showed that the SFEMARNet model achieved an accuracy of 98.75% on the test set. The recall, precision, and F1-score of the models reached 98.78%, 98.75%, and 98.76%, respectively, which were significantly better than the three deep learning models and of four machine learning models. It seems that the SFEMARNet model combined with LIBS technology may be a potential method for the accurate identification of soil contamination types.

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

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

Controlling capillary flow by using laser-fabricated acrylic substrates with rectangular grooves for the improvement of repeatability of laser induced breakdown spectroscopy liquid analysis

To enhance the consistency of Laser-Induced Breakdown Spectroscopy (LIBS) in detecting dried liquid droplets, a rectangular groove substrate prepared by laser etching of a polymethyl methacrylate (PMMA) acrylic board is proposed in this work to suppress the Coffee Ring Effect (CRE). Located at the corner of the substrate, the right-angled heat source structure induces a significant inward thermal capillary flow within the groove, thus inhibiting solute capillary flow. At the later stages of heating, the edge deposition that occurs during evaporation is suppressed by the sawtooth-like microscopic grooves, along with the capillary effects in associated with the liquid. The rationale of CRE suppression by the rectangular groove substrate is theoredically analyzed based on the coupled thermal-solute capillary flow theory. With LIBS used to scan the samples within rectangular and circular groove substrates, it is revealed that the relative standard deviation (RSD) of spectral intensities for Cd I 259.28 nm and Cu I 324.27 nm in the rectangular groove substrate with a depth of 0.1 mm (RSD = 4.2%, RSD = 2.1%) is less significant than in the circular groove substrate (RSD = 41.2%, RSD = 39.3%). On average, the RSD is 7.6% and 7.3%, respectively, which indicates the effect of the rectangular groove substrate on CRE suppression. On this basis, calibration curves are drawn for Cd and Cu to indicate the good fitting results for the rectangular groove substrate, with the determination coefficient (R2) reaching 0.997 and 0.996 for Cd and Cu, respectively. The limit of detection (LOD) is 0.48 μg/mL and 0.35 μg/mL, respectively. The cross-validation root mean square error (RMSECV) is 2.02 μg/mL and 1.36 μg/mL, respectively. These results demonstrate that the rectangular groove substrate is effective in suppressing the CRE, and in enhancing the sensitivity of detection and the accuracy of heavy metal prediction, which provides guidance for platform selection in the pre-processing stage of LIBS technology.

Rapid and accurate identification of bacteria utilizing laser-induced breakdown spectroscopy.

Timely and accurate identification of harmful bacterial species in the environment is paramount for preventing the spread of diseases and ensuring food safety. In this study, laser-induced breakdown spectroscopy technology was utilized, combined with four machine learning methods - KNN, PCA-KNN, RF, and SVM, to conduct classification and identification research on 7 different types of bacteria, adhering to various substrate materials. The experimental results showed that despite the nearly identical elemental composition of these bacteria, differences in the intensity of elemental spectral lines provide crucial information for identification of bacteria. Under conditions of high-purity aluminum substrate, the identification rates of the four modeling methods reached 74.91%, 84.05%, 85.36%, and 96.07%, respectively. In contrast, under graphite substrate conditions, the corresponding identification rates reached 96.87%, 98.11%, 98.93%, and 100%. Graphite is found to be more suitable as a substrate material for bacterial classification, attributed to the fact that more characteristic spectral lines are excited in bacteria under graphite substrate conditions. Additionally, the emission spectral lines of graphite itself are relatively scarce, resulting in less interference with other elemental spectral lines of bacteria. Meanwhile, SVM exhibited the highest precision rate and recall rate, reaching up to 1, making it the most effective classification method in this experiment. This study provides a valuable approach for the rapid and accurate identification of bacterial species based on LIBS, as well as substrate selection, enhancing efficient microbial identification capabilities in fields related to social security and military applications.

Rapid quantitative analysis of petroleum coke properties by laser-induced breakdown spectroscopy combined with random forest based on a variable selection strategy.

Driven by the "double carbon" strategy, petroleum coke short-term demand is growing rapidly as a negative electrode material for artificial graphite. The analysis of petroleum coke physicochemical properties has always been an important part of its research, encompassing significant indicators such as ash content, volatile matter and calorific value. A strategy based on laser-induced breakdown spectroscopy (LIBS) in combination with chemometrics is proposed to realize the rapid and accurate quantification of the above properties. LIBS spectra of 46 petroleum coke samples were collected, and an original random forest (RF) calibration model was constructed by optimizing the pretreatment parameters. The RF calibration model was further optimized based on variable importance measures (VIM) and variable importance in projection (VIP) methods. After variable selection, the elemental spectral lines related to ash content, volatile matter and calorific value modeling were screened out, thus initially exploring the correlation between these properties and elements. Under the optimized spectral pretreatment method, VI threshold and model parameters, the mean relative error (MREP) of the prediction set of ash content, volatile matter and calorific value were 0.0881, 0.0527 and 0.006, the root mean square error (RMSEP) of the prediction set of ash content, volatile matter and calorific value were 0.0471%, 0.6178% and 0.2697 MJ kg-1, respectively, and the determination coefficient (RP2) of the prediction set was 0.9187, 0.9820 and 0.9510, respectively. The combination of LIBS technology and chemometric methods can provide powerful technical means for the analysis and evaluation of the physicochemical properties of petroleum coke.

Fusion of laser-induced breakdown spectroscopy technology and deep learning: a new method to identify malignant and benign lung tumors with high accuracy.

Precisely distinguishing between malignant and benign lung tumors is pivotal for suggesting therapeutic strategies and enhancing prognosis, yet this differentiation remains a daunting task. The growth rates, metastatic potentials, and prognoses of benign and malignant tumors differ significantly. Developing specialized treatment protocols tailored to various tumor types is essential for enhancing patient survival outcomes. Employing laser-induced breakdown spectroscopy (LIBS) in conjunction with a deep learning methodology, we attained a high-precision differential diagnosis of malignant and benign lung tumors. First, LIBS spectra of malignant tumors, benign tumors, and normal tissues were collected. The spectra were preprocessed and Z score normalized. Then, the intensities of the Mg II 279.6, Mg I 285.2, Ca II 393.4, Cu II 518.3, and Na I 589.6 nm lines were analyzed in the spectra of the three tissues. The analytical results show that the elemental lines have different contents in the three tissues and can be used as a basis for distinguishing between the three tissues. Finally, the RF-1D ResNet model was constructed by combining the feature importance assessment method of random forest (RF) and one-dimensional residual network (1D ResNet). The classification accuracy, precision, sensitivity, and specificity of the RF-1D ResNet model were 91.1%, 91.6%, 91.3%, and 91.3%, respectively. And the model demonstrates superior performance with an area under the curve (AUC) value of 0.99. The above results show that combining LIBS with deep learning is an effective way to diagnose malignant and benign tumors.

Multidimensional characterization of Ni-Zn ferrite films based on laser-induced breakdown spectroscopy technology

Existing experimental results have suggested that doping different elements will significantly affect the properties of the thin film produced using the target material. In this study, the properties of the film were studied under different powers and different pressures through the magnetron sputtering method of preparation of three elements containing nickel, zinc, and iron thin film. First, the film is subjected to laser-induced breakdown spectroscopy (LIBS) detection, and the obtained spectral data are processed to obtain the energy spectrum and two-dimensional (2D) distribution of the elements. Next, the film thickness was examined under a scanning electron microscope (SEM) to verify the feasibility of LIBS technology in film thickness measurement. Another set of experiments was performed to estimate the film thickness based on the obtained data to determine the accuracy of film thickness measurement using LIBS technology. Subsequently, the transmittal rate and energy-dispersive X-ray spectroscopy (EDS) of the thin film that was obtained by the sputtering of Nickel-zinc ferrite film were examined. The optical band gap of the thin film was determined following the transmittal rate of the thin film. Besides, the variation trend of the optical band gap was plotted and then compared with the LIBS spectral data. The statistical data of the element proportion were obtained through EDS, and the calibration curve was generated in combination with the LIBS spectral data. As revealed by the above-mentioned results, the LIBS is capable of analyzing the change of element content in the binary doped thin films.

Evolution and interpretation of LIBS signals based on laser selective layered paint removal from aircraft composite tail

The reliability and controllability of laser selective layered paint removal depend on effective online monitoring technology. In the process of laser cleaning the surface paint layer of the aircraft composite tail, this paper studied the evolution and interpretation of laser induced breakdown spectroscopy (LIBS) signals. The laser power is adjusted to achieve different depths of the laser cleaned composite surface paint layer to compare the Pearson linear correlation between the LIBS online spectrum and the reference spectrum and decode the characteristic peak signal of the LIBS spectrum. The linear correlation of the LIBS spectra and the evolution of the characteristic peaks of the LIBS spectra are mutually verified to establish the intrinsic correlation between the controllability of layered paint removal and the variation of the LIBS spectra, which demonstrates the feasibility of the LIBS technology for online monitoring and feedback of laser paint removal. The results show that the Pearson correlation coefficient r was 0.87–0.89 for the laser cleaning depth range of 44.79–51.50 μm, the layered boundary of laser cleaning the topcoat without damaging the primer is reached, it is proved that the Pearson correlation coefficient r can determine the laser paint removal process. This research compared the different LIBS spectra with cleaning depths at 44.79 and 51.50 μm. It was found that the characteristic peak of strontium element Sr I at wavelength 460.66 nm could characterize the delamination boundary of laser paint removal from aircraft composite tail.

Identification and classification of recyclable waste using laser-induced breakdown spectroscopy technology

The management and disposal of waste is a severe social issue and an essential part of ecological sustainability. As an important component of the green, low-carbon, and recycling economic system, the identification and classification of recyclable waste is the premise of its reuse and energy conservation. The main issues at hand are to improve the classification accuracy and reliability of recyclable waste and to achieve automatic classification. The methods based on physical characteristics and image-based methods are inaccurate and unreliable. The current spectroscopy methods need to process the detected samples in advance, unsuitable for automatic detection. Based on material composition properties, the Laser-Induced Breakdown Spectroscopy (LIBS) technology is here proposed to accurately and reliably identify and classify recyclable waste into six categories at the level of consumer, such as paper, plastic, glass, metal, textile, and wood. The method is also used to subclassify the same category of waste for reuse at the level of a recycling factory. We subclassified metals into iron, stainless steel, copper, and aluminum and plastics into polyvinylchloride, polyoxymethylene, acrylonitrile-butadiene-styrene, polyamide, polyethylene, and polytetrafluoroethylene. The drop-dimension methods of LIBS spectra of waste were researched to eliminate noise and redundant information by principal component analysis (PCA) and linear discriminant analysis (LDA), respectively. Their clustering effects were analyzed to choose a suitable dimension. Combining the random forest (RF), back propagation neural network (BPNN), and convolutional neural network (CNN), we established and compared five classification models, PCA + RF, PCA + BPNN, LDA + RF, LDA + BPNN, and 1D-CNN. For the classification of six categories, the accuracies of proposed classification models are all more than 96%, and LDA(5D) + RF has 100% accuracy and optimal classification performance indices. For the subclassification of metals and plastics, PCA(8D) + RF has the highest classification accuracy of 98.77% and 99.52%, respectively.

Quantitative Detection of the Raw Ore Turquoise Based on Laser-Induced Breakdown Spectroscopy Technology.

Turquoise is one of the key ingredients in some magical Tibetan medicines, and its quality and content directly affect the medicine's effectiveness. In this paper, laser-induced breakdown spectroscopy (LIBS) technology was first applied to detect the raw materials of Tibetan medicine. The traditional data analysis methods could not meet the practical requirements of modern Tibetan medicine factories due to matrix effects. The concept of correlation coefficient (ρ) in pattern recognition technique was introduced as an evaluation index, and the model was established based on the intensities of the four characteristic Al and Cu spectral lines of the samples for different contents of turquoise, which was applied to estimate the contents of turquoise in the samples to be tested. We detected the LIBS on 126 samples of raw ore from 42 areas in China and evaluated the turquoise content using self-developed software with an error of <10%. This paper's technical testing process and methods can also be applied to test other mineral compositions and provide technical support for modernizing and standardizing Tibetan medicines.