Quantitative detection of alkali metal Na based on laser-induced breakdown spectroscopy technology

The alkali metal compounds released from high alkali fuels such as low-quality coal during thermal utilization can affect the normal operation of thermal equipment. Therefore, rapid online detection of alkali metal content in low-quality coal is of great significance for controlling alkali metal release during combustion. The alkali metal Na element is used as the detection object, and the mixed samples of graphite and sodium chloride powder with different ratios are used as experimental samples. The influencing factors of measuring Na element in samples using laser induced breakdown spectroscopy (LIBS) technology are studied. The impact of two signal strength calculation methods on signal stability is compared. The influence of experimental parameters on signal strength and signal-to-noise ratio is analyzed. The quantitative calculation models for Na element have been established. Research has shown that the characteristic spectral lines of Na element, Na I 588.995 nm and Na I 589.592 nm, are suitable as the main analytical spectral lines. Using the area intensity of the dual line characteristic spectral lines of Na element as the signal intensity can effectively improve signal stability. When the laser energy is 60 mJ and the delay time is 1000 ns, the relative standard deviation of spectral signal intensity is low and the signal-to-noise ratio is high. The quantitative calculation models are established using traditional calibration method, partial least squares (PLS) method, and support vector machine (SVR) with spectral signal intensity as the input and Na element addition in the sample as the output. The accuracy of each model is compared and analyzed. The results indicate that the PLS model may exhibit overfitting when the sample size is small and the input quantity is large. The fitting accuracy of the SVR model is 0.9783, the root mean square percentage error of the training set is 13.42%, and the root mean square percentage error of the test set is 13.51%. Compared with traditional calibration model, when the sample size is small, the SVR model can better correct the influence of matrix effects and improve the accuracy of alkali metal quantitative detection in low-quality coal

Successful transplantation of guinea pig gut microbiota in mice and its effect on pneumonic plague sensitivity

Microbiota-driven variations in the inflammatory response are predicted to regulate host responses to infection. Increasing evidence indicates that the gastrointestinal and respiratory tracts have an intimate relationship with each other. Gut microbiota can influence lung immunity whereby gut-derived injurious factors can reach the lungs and systemic circulation via the intestinal lymphatics. The intestinal microbiota’s ability to resist colonization can be extended to systemic infections or to pathogens infecting distant sites such as the lungs. Unlike the situation with large mammals, the microtus Yersinia pestis 201 strain exhibits strong virulence in mice, but nearly no virulence to large mammals (such as guinea pigs). Hence, to assess whether the intestinal microbiota from guinea pigs was able to affect the sensitivity of mice to challenge infection with the Y. pestis 201 strain, we fed mice with guinea pig diets for two months, after which they were administered 0.5 ml of guinea pig fecal suspension for 30 days by oral gavage. The stools from each mouse were collected on days 0, 15, and 30, DNA was extracted from them, and 16S rRNA sequencing was performed to assess the diversity and composition of the gut microbiota. We found that the intestinal microbiota transplants from the guinea pigs were able to colonize the mouse intestines. The mice were then infected with Yersinia pestis 201 by lung invasion, but no statistical difference was found in the survival rates of the mice that were colonized with the guinea pig’s gut microbiota and the control mice. This indicates that the intestinal microbiota transplantation from the guinea pigs did not affect the sensitivity of the mice to pneumonic plague

Quantitative analysis of common elements in steel using a handheld μ-LIBS instrument

Quantitative analysis of steel by a handheld μ-LIBS device using dominant factor based PLS combined with standardization is presented and results are compared with conventional PLS and a handheld XRF device.</p

A data selection method for matrix effects and uncertainty reduction for laser-induced breakdown spectroscopy

Abstract Severe matrix effects and high signal uncertainty are two key bottlenecks for laser-induced breakdown spectroscopy (LIBS) quantification and wide applications. Based on the understanding that the superposition of both matrix effects and signal uncertainty directly affects plasma parameters and further influences spectral intensity and LIBS quantification performance, a data selection method based on plasma temperature matching (DSPTM) was proposed to reduce both matrix effects and signal uncertainty. By selecting spectra with less plasma temperature differences for all samples, the proposed method was able to build up the quantification model more relying on spectra with less matrix effects and signal uncertainty, therefore improving final quantification performance. In application for quantitative analysis for zinc (Zn) content in brass alloys, it was found that both the accuracy and precision were improved using either univariate model or multiple linear regression (MLR). More specifically, for univariate model, the root-mean-square-error of prediction (RMSEP), the determination coefficients (R^2), relative standard derivation (RSD) were improved from 3.30%, 0.864, 18.8% to 1.06%, 0.986, 13.5%, respectively; while for MLR, RMSEP, R2, RSD were improved from 3.22%, 0.871, 26.2% to 1.07%, 0.986, 17.4%, respectively. Results proved that DSPTM can be used as an effective way to reduce matrix effects and to improve repeatability by selecting reliable data.</jats:p

Classification of ginseng according to plant species, geographical origin, and age using laser-induced breakdown spectroscopy and hyperspectral imaging

This study used LIBS and HSI combined with chemometrics to determine the ginseng samples based on plant species, geographical origin, and age.</p

Evaluation of femtosecond laser-induced breakdown spectroscopy system as an offline coal analyzer.

Developments in femtosecond laser induced breakdown spectroscopy (fs-LIBS) applications during the last two decades have further centered on innovative métier tie-in to the advantageous properties of femtosecond laser ablation (fs-LA) introduced into LIBS. Yet, for industrially-oriented application like coal analysis, no research has exposed to view the analytical capabilities of fs-LA in enhancing the physical processes of coal ablation and the impact into quantitative correlation of spectra and data modeling. In a huge coal market, fast and accurate analysis of coal property is eminently important for coal pricing, combustion optimization, and pollution reduction. Moreover, there is a thirst need of precision standardization for coal analyzers in use. In this letter, the analytical performance of a one-box femtosecond laser system is evaluated relative to an industrially applied coal analyzer based on five objectives/measures: spectral correlation, relative sensitivity factors, craters topology, plasma parameters, and repeatability. Despite high-threshold operation parameters of the fs system, competitive results are achieved compared to the optimized analytical conditions of the ns-coal analyzer. Studies targeting the in-field optimization of fs-LIBS systems for coal analysis can potentially provide insights into fs-plasma hydrodynamics under harsh conditions, instrumental customization, and pave the way for a competitive next-generation of coal analyzers