Plasma parameters correction method based on plasma image-spectrum fusion for matrix effect elimination in LIBS.

Matrix effect is one of the obstacles that hinders the rapid development of laser-induced breakdown spectroscopy (LIBS), and it is currently a hot, challenging, and focal point in research. To eliminate the matrix effect, this study proposed a plasma parameters correction method based on plasma image-spectrum fusion (PPC-PISF). This method corrects the total number density, plasma temperature, and electron number density variations caused by matrix effect using effective features in plasma images and spectra. To verify the feasibility of this method, experiments were conducted on pressed and metal samples, and the results were compared with those corrected by image-assisted LIBS (IA-LIBS). For the pressed samples, after correction by PPC-PISF, the R2 of the calibration curves all improved to above 0.993, the average root-mean-square error (RMSE) decreased by 41.05%, and the average relative error (ARE) decreased by 59.35% evenly in comparison to IA-LIBS. For the metal samples, after correction by PPC-PISF, the R2 of the calibration curves all increased to above 0.997. Additionally, the RMSE decreased by 29.63% evenly, the average ARE decreased by 38.74% compared to IA-LIBS. The experimental results indicate that this method is an effective method for eliminating the matrix effect, promoting the further development of LIBS in industrial detection.

Investigation of Hydrothermal Performance in Micro-Channel Heat Sink with Periodic Rectangular Fins.

The micro-channel heat sink (MCHS) is an excellent choice due to its exceptional cooling capabilities, surpassing those of its competitors. In this research paper, a computational fluid dynamics analysis was performed to investigate the laminar flow and heat transfer characteristics of five different configurations of a variable geometry rectangular fin. The study utilized a water-cooled smooth MCHS as the basis. The results indicate that the micro-channel heat sink with a variable geometry rectangular fin has better heat dissipation capacity than a straight-type micro-channel heat sink, but at the same time, it has larger pressure loss. Based on the analysis of various rectangular fin shapes and Reynolds numbers in this study, the micro-channel heat sink with rectangular fins exhibits Nusselt numbers and friction factors that are 1.40-2.02 and 2.64-4.33 times higher, respectively, compared to the smooth heat sink. This significant improvement in performance results in performance evaluation criteria ranging from 1.23-1.95. Further, it is found that at a relatively small Reynolds number, the micro-channel heat sink with a variable geometry rectangular fin has obvious advantages in terms of overall cooling performance. Meanwhile, this advantage will decrease when the Reynolds number is relatively large.

A plasma image-spectrum fusion correction strategy for improving spectral stability based on radiation model in laser induced breakdown spectroscopy.

Spectral fluctuation is one of the main obstacles affecting the further development of LIBS, and it is also the current research hotspot and difficulty. To meet the requirements of industrial monitoring, a novel method named plasma image-spectrum fusion laser induced breakdown spectroscopy (PISF-LIBS) was proposed to correct the spectral fluctuation and improve the quantitative accuracy. In this method, by systematically analyzing the spectral radiation model, six main factors affecting the spectral stability were obtained. Further, the standard spectrum in the ideal plasma state which is not affected by these six factors was calculated, and the deviation from the actual spectrum was obtained. According to the above analysis, the calculated deviation was mainly affected by these six factors and can be estimated through them. Therefore, this study creatively proposed to use the effective information in the plasma images and spectra to indirectly characterize the deviation, so as to realize the correction of spectral fluctuation. To verify the wide applicability of PISF-LIBS in experimental conditions, the LIBS spectra of aluminum alloy obtained under four different experimental conditions were used. After PISF-LIBS correction, the R2 increased to more than 0.974, and the RMSE, MAPE and RSD of the prediction set decreased by 44.789%, 47.854% and 51.687% on average. To further verify the wide applicability of PISF-LIBS in experimental samples, alloy steel samples and pressed samples were also used. For alloy steel samples, after PISF-LIBS correction, the R2 increased to more than 0.996, and the RMSE, MAPE and RSD of the prediction set decreased by 48.337%, 52.856% and 25.819% evenly. For pressed samples, the R2 increased over 0.992, and the RMSE, MAPE and RSD of the prediction set decreased by 61.493%, 61.080% and 39.945% averagely. The experimental results prove the effectiveness and wide applicability of PISF-LIBS in spectral fluctuation correction.

Time-Resolved Laser-Induced Breakdown Spectroscopy for Accurate Qualitative and Quantitative Analysis of Brown Rice Flour Adulteration.

To solve the adulteration problem of brown rice flour in the commodity market, a novel, accurate, and stable detection method based on time-resolved laser-induced breakdown spectroscopy (TR-LIBS) is proposed. Qualitative and quantitative analysis was used to detect five adulterants and seven different adulterant ratios in brown rice flour. Being able to excavate more information from plasma by obtaining time-resolved spectra, TR-LIBS has a stronger performance, which has been further verified by experiments. For the qualitative analysis of adulterants, the traditional machine learning models based on TR-LIBS, linear discriminant analysis (LDA), naïve Bayes (NB) and support vector machine (SVM) have significantly better classification accuracy than those based on traditional LIBS, increasing by 3-11%. The deep learning classification model based on TR-LIBS also achieved the same results, with an accuracy increase of more than 8%. For the quantitative analysis of the adulteration ratio, compared with traditional LIBS, the quantitative model based on TR-LIBS reduces the limit of detection (LOD) of five adulterants from about 8-51% to 4-19%, which effectively improves the quantitative detection performance. Moreover, t-SNE visualization proved that there were more obvious boundaries between different types of samples based on TR-LIBS. These results demonstrate the great prospect of TR-LIBS in the identification of brown rice flour adulteration.

Classification of steel using laser-induced breakdown spectroscopy combined with deep belief network.

The identification of steels is a crucial step in the process of recycling and reusing steel waste. Laser-induced breakdown spectroscopy (LIBS) coupled with machine learning is a convenient method to classify the types of materials. LIBS can generate characteristic spectra of various samples as input variable for steel classification in real time. However, the performance of classification model is limited to the complex input due to similar chemical composition in samples and nonlinearity problems between spectral intensities and elemental concentrations. In this study, we developed a method of LIBS coupled with deep belief network (DBN), which is suitable to deal with a nonlinear problem, to classify 13 brands of special steels. The performance of the training and validation sets were used as the standard to optimize the structure of DBN. For different input, such as the intensities of full-spectra signals and characteristic spectra lines, the accuracies of the optimized DBN model in the training, validation, and test set are all over 98%. Moreover, compared with the self-organizing maps, linear discriminant analysis (LDA), k-nearest neighbor (KNN) and back-propagation artificial neural networks (BPANN), the result of the test set showed that the optimized DBN model performed second best (98.46%) in all methods using characteristic spectra lines as input. The test accuracy of the DBN model could reach 100% and the maximum accuracy of other methods ranged from 62.31% to 96.16% using full-spectra signals as input. This study demonstrates that DBN can extract representative feature information from high-dimensional input, and that LIBS coupled with DBN has great potential for steel classification.