In situ simultaneous quantitative analysis multi-elements of archaeological ceramics via laser-induced breakdown spectroscopy combined with machine learning strategy

Effect of target thickness and laser irradiance on the back-reflection-enhanced laser-induced breakdown spectroscopy signal in glass

In the work that is being presented here, the effect of sample thickness and laser irradiance on the reduction of the signal-to-background ratio SBG and the plasma parameters, specifically electron temperature and electron density, is being investigated using back-reflection-enhanced laser-induced breakdown spectroscopy (BRELIBS). Copper and silver discs that had been highly polished were attached to the back surface of the glass target, and the Nd-YAG laser beam that was focused on the front surface of the target was tuned to its fundamental wavelength. The thicknesses of the transparent glass samples that were analysed were 1 mm, 3 mm, and 6 mm. One is able to achieve a range of different laser irradiance levels by adjusting the working distance that exists between the target sample and the focusing lens. The end result of this is that the signal-to-background ratio in the BRELIBS spectra of thicker glass samples is significantly lower as compared to the ratio in the spectra of thinner glass samples. In addition, a significant influence of modifying the laser irradiance (by increasing the working distance on the SBG ratio) is seen at various glass thicknesses for both BRELIBS and LIBS, with BRELIBS having a better SBG. Nevertheless, the laser-induced plasma parameter known as the electron temperature has not been significantly impacted by the decrease in the glass thickness.

Novel Random Forest Ensemble Modeling Strategy Combined with Quantitative Structure-Property Relationship for Density Prediction of Energetic Materials

With the further development of the concept of green chemistry, the new generation of energetic materials tends to exhibit detonation properties such as higher insensitivity, higher density, and higher energy. Therefore, the precise molecular design and green and efficient synthesis of energetic materials will be one of the serious challenges. For the purpose of accurate prediction of detonation performance of energetic materials, an ensemble modeling strategy based on the combination of Monte Carlo (MC) and variable importance measurement (VIM) improved random forest (RF) and quantitative structure-property relationship (QSPR) is proposed, which was successfully used for density prediction of energetic materials. First, the structure of 162 energetic compounds was optimized by Gaussian software, and the molecular descriptor data were calculated by CODESSA software based on the optimized molecular structure. Then, the MCVIMRF_Med ensemble model was constructed on the basis of the above molecular descriptor data and the corresponding energetic compound density index. The joint X-Y distance algorithm (SPXY) is used to partition the data set. And then, MC is used to further divide the calibration set data into multiple subsets for the construction of the ensemble model. The subset size and the number of iterations of the MCVIMRF_Med ensemble model were optimized through MC cross validation. The final output strategy of the ensemble model is optimized based on the optimized parameters, and an output optimization method based on median screening is proposed and successfully applied for the prediction performance optimization of the MCVIMRF_Med ensemble model. To further investigate the performance of the MCVIMRF_Med ensemble model, the performance of it was compared with partial least squares, RF, VIMRF, and MCVIMRF calibration models. It shows that the MCVIMRF_Med ensemble model can achieve a better prediction result for the density of energetic materials, with R ² _CV of 0.9596, RMSECV of 0.0437 g/cm³, R ² _P of 0.9768, RMSEP of 0.0578 g/cm³, and relative analysis deviation of prediction set of 3.951. Therefore, the MCVIMRF_Med ensemble modeling strategy combined with QSPR is an effective approach for the density prediction of energetic materials. This work is expected to provide new research ideas and technical support for accurate prediction of detonation performance of energetic materials.