查看更多>>摘要：? 2022 Elsevier B.V.A chemical-hydrodynamic model of laser induced plasma is developed to study a process of deposition of titanium oxides from titanium laser induced plasma to the titanium target surface. The model is relevant to texturing and coating of titanium bone implants that is done by scanning the ablation laser across implant surfaces. Such the procedure improves the biocompatibility and durability of the implants. The model considers plasma chemical reactions, formation of condensed species inside the plasma plume, and deposition and accumulation of these species on the ablation surface. A chemical part of the model is based on minimization of Gibbs free energy of the chemical system; it is used to calculate the chemical composition of the plasma. A hydrodynamic part uses the 2D fluid-dynamic equations that model a 3D axisymmetric plasma plume and assumes the mass and energy exchange between the plasma and the surface. The initial parameters for the model are inferred from experiment. The model shows that condensed titanium oxides, mostly TiO2, form in a peripheral plasma zone and gradually adhere to the surface during the plasma plume evolution. The model predicts the major component and thickness of the deposit and can be applied for the optimization of experiments aimed at surface modification.
查看更多>>摘要：? 2022 Elsevier B.V.The use of zinc oxide (ZnONPs) and titanium dioxide nanoparticles (TiO2NPs) in cosmetic formulations to obtain protection against the sun's UV radiation has become a common practice owing to their high photo-stability and the absence of allergic reactions of these nanomaterials. The manufacturing of cosmetics modified with nanoparticles has been regulated in Europe since 2009. Nevertheless, methodologies for sample pre-treatment and characterisation (quantification and size assessment) of nanoparticles from complex matrices are scarce and still in development. The proposed methodology was based on the tip sonication of moisturisers in an organic solvent [40 mL of acetone, 40% amplitude, 5 min and discontinuous mode with 59 s of relax after 59 s tip sonication] followed by spICP-MS, which provides information about concentration and size of the NPs. The whole methodology was repeatable (RSDs lower than 10% for size and concentration values of ZnONPs and TiO2NPs) and accurate (analytical recoveries of 102 ± 12% and 119 ± 3%for ZnONPs and TiO2NPs standards, respectively). The over-all procedure was applied to several commercial moisturisers with solar sun protection. spICP-MS results were compared with total content of analyte in the extracts and in the acid digested samples. Finally, transmission electron microscopy coupled to energy dispersive X-ray spectroscopy analysis of extracts from moisturising creams was carried out as comparative (qualitative) assessment.
查看更多>>摘要：? 2022 Elsevier B.V.Accurate classification of soil types and contamination is crucial for crops' productivity. Among the soil analysis techniques, laser-induced breakdown spectroscopy (LIBS) has become a prominent technology for real-time characterization of soil properties. LIBS coupled with supervised machine learning and chemometrics methods (e.g., partial least squares discriminate analysis (PLS-DA), principal component analysis (PCA)) has demonstrated great capabilities for soils classification. However, when the training and test spectra have different distribution and not representative of each other, there are generalization issues, which make the model trained on training spectra hard to adapt to test spectra. In this work we propose a method to calibrate the test spectra using the median of principal components (PCs). PCA is used to analyze the spectra distribution. We independently compute the median of both training's and test's PCs, and then the test's median is adjusted based on its differences with training's. With the calibrated PCs, the test spectra is reconstructed accordingly. To test the performance of the proposed calibration algorithm, we conduct experiments on a publicly available challenging LIBS dataset. We compare our calibration algorithm with the current best performing calibration method on the same test set, using the same machine learning (ML) algorithm, PLS-DA, trained with the same training set. Our method improves the test accuracy by 1.2%. The reason using PLS-DA for performance comparison is that it is currently the best performing ML algorithm. To further improve the test accuracy, other ML algorithms are investigated. Convolutional neural networks (CNN) have achieved good accuracy in lithological classification with LIBS recently. Therefore, it is extended in this work to soil classification. We use CNN as a tool for feature extraction and as an end-to-end classifier. We use the CNN based extraction mechanism with other classifiers, such as support vector machine (SVM) and random forest (RF), for soil classification. The performance of CNN models on the calibrated test spectra is compared, which concludes that CNN combined with SVM achieves the best accuracy and improves the test accuracy by 3.1% compared to the best performing ML algorithm PLS-DA.
查看更多>>摘要：? 2022 Elsevier B.V.Routine application of X-ray fluorescence (XRF) spectrometry in laboratory practice requires special procedures to correct for matrix effects. One of the most popular procedures is the influence coefficients method. In this method the regression equation relating analytical signal to the target element concentration is extended with empirical terms correcting the influence from particular matrix elements. While the influence coefficients method is quite accurate, it is rather laborious as it requires individual selection of matrix terms for each element under study. The influence coefficients method is based on the least squares regression technique, thus the number of matrix correction terms is limited by the number of available standard calibration samples. Here we propose a very simple technique that can take into account an unlimited number of terms in the influence coefficients method through the employment of partial least squares regression (PLS) where spectral intensities, their ratios and squared intensities are employed as variables. Unlike traditional application of PLS in XRF studies where the regression model is built using spectral intensities only, the proposed approach inspired by classic influence coefficients allows elimination of complex specific XRF matrix effects. The paper describes the suggested method and demonstrates its' performance in two EDXRF data sets with significant matrix effects (ore and steel samples).
查看更多>>摘要：? 2022 Elsevier B.V.We have investigated the optical-optical double resonance (OODR) spectroscopy of 87Rb for transition 5S1/2 → 5P3/2 → 5D3/2,5/2 in magnetic fields. Unlike the electromagnetically induced transparency (EIT) spectroscopy of the transition to high Rydberg states (Opt Express, 25(2017)33575), the spectral observation to this low excited state remains much discrepancy compared to the theory simulation as the strong coupling between 5P3/2 and 5D3/2,5/2 leads to a state-dependent spectral broadening due to double resonance optical pumping (DROP) effect. Instead, the OODR method employs a weak optical coupling between these two states, serving as probe, where the spectral broadening can be ignored. We experimentally record the Zeeman-splitting spectrum of 87Rb to 5D3/2 and 5D5/2 in magnetic fields by OODR and the observed spectra are well in accordance with the theoretical calculation. It shows OODR is more suitable to investigate the high resolution spectroscopy of the excited state when the relaxation in DROP can't be neglected.
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