Kuohu Li

Analytical-performance improvement of laser-induced breakdown spectroscopy for steel using multi-spectral-line calibration with an artificial neural network

A multi-spectral-line calibration (MSLC) approach based on an artificial neural network (ANN) was developed to improve the accuracy and precision of steel analysis using laser-induced breakdown spectroscopy (LIBS). The intensity ratios of multiple spectral lines of target and matrix elements were used to train an ANN. The resulting model was able to relate the spectra to the concentrations of target elements more accurately than the conventional internal calibration approach, which led to improvements in the accuracy and precision of the LIBS analysis. This approach was applied to LIBS analysis of steel samples to predict the Cr and Ni concentrations. Compared with a conventional internal calibration approach, the root-mean-square errors of cross-validation for Cr and Ni decreased from 0.018 and 0.067 wt% to 0.010 and 0.023 wt%, respectively, using the proposed MSLC, and the average values of the relative standard deviation for Cr and Ni decreased from 11.3 and 19.5% to 6.4 and 12.9%, respectively.

Determination of cobalt in low-alloy steels using laser-induced breakdown spectroscopy combined with laser-induced fluorescence

Cobalt element plays an important role for the properties of magnetism and thermology in steels. In this work, laser-induced breakdown spectroscopy combined with laser-induced fluorescence (LIBS-LIF) was studied to selectively enhance the intensities of Co lines. Two states of Co atoms were resonantly excited by a wavelength-tunable laser. LIBS-LIF with ground-state atom excitation (LIBS-LIFG) and LIBS-LIF with excited-state atom excitation (LIBS-LIFE) were compared. The results show that LIBS-LIFG has analytical performance with LoD of 0.82μg/g, R(2) of 0.982, RMSECV of 86μg/g, and RE of 9.27%, which are much better than conventional LIBS and LIBS-LIFE. This work provided LIBS-LIFG as a capable approach for determining trace Co element in the steel industry.

Accuracy improvement on polymer identification using laser-induced breakdown spectroscopy with adjusting spectral weightings

A new approach to polymer identification by laser-induced breakdown spectroscopy (LIBS) with adjusting spectral weightings (ASW) was developed in this work aiming at improving the identification accuracy. This approach has been achieved through increasing the intensities of specific characteristic spectral lines which are important to polymer identification but difficult to be excited. Using the ASW method, the identification accuracies of all 11 polymers were increased to nearly 100%, while the accuracies of PE, PU, PP and PC were only 98%, 74%, 90% and 98%, respectively, without using the ASW method.

Acidity measurement of iron ore powders using laser-induced breakdown spectroscopy with partial least squares regression

Laser-induced breakdown spectroscopy (LIBS) with partial least squares regression (PLSR) has been applied to measuring the acidity of iron ore, which can be defined by the concentrations of oxides: CaO, MgO, Al₂O₃, and SiO₂. With the conventional internal standard calibration, it is difficult to establish the calibration curves of CaO, MgO, Al₂O₃, and SiO₂ in iron ore due to the serious matrix effects. PLSR is effective to address this problem due to its excellent performance in compensating the matrix effects. In this work, fifty samples were used to construct the PLSR calibration models for the above-mentioned oxides. These calibration models were validated by the 10-fold cross-validation method with the minimum root-mean-square errors (RMSE). Another ten samples were used as a test set. The acidities were calculated according to the estimated concentrations of CaO, MgO, Al₂O₃, and SiO₂ using the PLSR models. The average relative error (ARE) and RMSE of the acidity achieved 3.65% and 0.0048, respectively, for the test samples.

Sensitive determinations of Cu, Pb, Cd, and Cr elements in aqueous solutions using chemical replacement combined with surface-enhanced laser-induced breakdown spectroscopy.

In this study, chemical replacement combined with surface-enhanced laser-induced breakdown spectroscopy (CR-SENLIBS) was for the first time applied to improve the detection sensitivities of trace heavy metal elements in aqueous solutions. Utilizing chemical replacement effect, heavy metal ions in aqueous solution were enriched on the magnesium alloy surface as a solid replacement layer through reacting with the high chemical activity metallic magnesium (Mg) within 1 minute. Unitary and mixed solutions with Cu, Pb, Cd, and Cr elements were prepared to construct calibration curves, respectively. The CR-SENLIBS showed a much better detection sensitivity and accuracy for both unitary and mixed solutions. The coefficients of determination R2 of the calibration curves were above 0.96, and the LoDs were of the same order of magnitude, i.e., in the range of 0.016-0.386 μg/mL for the unitary solution, and in the range of 0.025-0.420 μg/mL for the mixed solution. These results show that CR-SENLIBS is a feasible method for improving the detection sensitivity of trace element in liquid sample, which definitely provides a way for wider application of LIBS in water quality monitoring.

Laser-induced breakdown spectroscopy using laser pulses delivered by optical fibers for analyzing Mn and Ti elements in pig iron

A portable fiber-optic laser-induced breakdown spectroscopy (FO-LIBS) system was developed and employed to quantitatively analyze Mn and Ti elements in pig iron. The ablated craters produced by FO-LIBS are shallower with flatter bottom surfaces as compared with those produced by a conventional LIBS system without using optical fibers to deliver the laser pulses. This is beneficial on the special occasions requiring shallower destruction. The time-resolved images of plasma plumes were obtained and compared using both LIBS systems. Plasmas with lower temperature and electron density generated by the FO-LIBS system were thinner and more uniform, which means a lower self-absorption. Using the FO-LIBS system, the coefficients of determination (R2 factors) of calibration curves for Mn and Ti elements were 0.997 and 0.998, respectively. The leave-one-out cross-validation (LOOCV) method was used to evaluate the detection accuracy. The root-mean-square errors of cross-validation (RMSECV) for Mn (concentration range 0.072–2.06 wt%) and Ti (concentration range 0.006–0.399 wt%) elements were 0.0501 and 0.0054 wt%, respectively. These results are comparable with or even slightly better than those obtained by conventional LIBS. Furthermore, the FO-LIBS system is more compact and cost effective, more suitable for harsh environments, and hence more promising for industrial applications.

Quantitative analyses of Mn, V, and Si elements in steels using a portable laser-induced breakdown spectroscopy system based on a fiber laser

A portable laser-induced breakdown spectroscopy (LIBS) system based on a fiber laser was developed and employed to quantitatively analyze manganese (Mn), vanadium (V), and silicon (Si) elements in steels. After background removal, the coefficients of determination (R2 factors) of the calibration curves for Mn, V, and Si elements reached 0.997, 0.991 and 0.992, respectively, obvious improvements compared to those of the original spectra. The leave-one-out cross-validation (LOOCV) method was used to test the system. The root-mean-square error of cross-validation (RMSECV) for Mn (0.072–2.06 wt%), V (0.009–0.821 wt%), and Si (0.099–1.85 wt%) elements were 0.037, 0.041 and 0.079 wt%, respectively. The average relative errors (AREs) for Mn elements reached 7.6%. These results are comparable with those of the conventional LIBS which refers to utilizing the traditional flash-lamp-pumped laser as a laser source. However, compared to conventional LIBS, a fiber laser LIBS (FL-LIBS) is more compact, robust, and cost effective. The FL-LIBS, coupling a compact fiber laser and spectrometer, is a convenient approach to providing a portable solution for real-time and in situ detection in industry, especially in harsh environments.

Wavelet-based interference correction for laser-induced breakdown spectroscopy

To minimize the impact of spectral interference on laser-induced breakdown spectroscopy (LIBS) quantitative analyses, an algorithm based on wavelet transform was developed for simultaneous correction of spectral interference and continuum background. The root-mean-square error of calibration (RMSEC) of the univariate regression model for the element of interest was applied to determine the wavelet function, decomposition level, and scaling factor α. When the interference-free analytical lines of the elements of interest cannot be directly obtained from the measured spectra, they can be extracted from the spectra with the developed method for quantitative analysis. This method was applied for LIBS analyses of chromium (Cr), silicon (Si), titanium (Ti), and manganese (Mn) with continuum backgrounds and spectral interference in low alloy steel samples. The root-mean-square errors of cross-validation (RMSECV) of elements Cr, Si, Ti, and Mn were 0.0295, 0.0140, 0.0183, and 0.0558 wt%, respectively. The results demonstrated that the developed algorithm contributed to accuracy improvement for LIBS quantitative analyses with the presence of spectral interference.

Quantitative analysis of steel samples using laser-induced breakdown spectroscopy with an artificial neural network incorporating a genetic algorithm

In this work, a genetic algorithm (GA) was employed to select the intensity ratios of the spectral lines belonging to the target and domain matrix elements, then these selected line-intensity ratios were taken as inputs to construct an analysis model based on an artificial neural network (ANN) to analyze the elements copper (Cu) and vanadium (V) in steel samples. The results revealed that the root mean square errors of prediction (RMSEPs) for the elements Cu and V can reach 0.0040 wt. % and 0.0039 wt. %, respectively. Compared to 0.0190 wt. % and 0.0201 wt. % of the conventional internal calibration approach, the reduction rates of the RMSEP values reached 78.9% and 80.6%, respectively. These results indicate that the GA combining ANN can excellently execute the quantitative analysis in laser-induced breakdown spectroscopy for steel samples and further improve analytical accuracy.

Investigation on self-absorption at reduced air pressure in quantitative analysis using laser-induced breakdown spectroscopy

The self-absorption at reduced air pressure for quantitative analysis of Mn and Cu elements in steel using laser-induced breakdown spectroscopy was investigated. The calibration curves of Mn and Cu elements at the air pressures of 100, 80, 50, 20, and 1 kPa were studied. The results show that, the nonlinearity of calibration curves which caused by self-absorption effects at atmosphere could be significantly improved by reducing the air pressure to 1 kPa, and the coefficients of determination (R2) of linear calibration curves of Mn and Cu lines are all higher than 0.99. The further study explored that the reason for the improvement was that the induced plasma became low density and the self-absorption coefficient was close to 1 when the air pressure reduced to 1 kPa.