A Comparative Study of the Method to Rapid Identification of the Mural Pigments by Combining LIBS-Based Dataset and Machine Learning Methods

Due to the similar chemical composition and matrix effect, the accurate identification of mineral pigments on wall paintings has brought great challenges. This work implemented an identification study on three mineral pigments with similar chemical compositions by combining LIBS technology with the K-nearest neighbor algorithm (KNN), random forest (RF support vector machine (SVM), back propagation artificial neural network (Bp-ANN) and convolutional neural network (CNN) to find the most suitable identification method for mural research. Using the SelectKBest algorithm, 300 characteristic lines with the largest difference among the three pigments were determined. The identification models of KNN, RF, SVM, Bp-ANN and CNN were established and optimized. The results showed that, except for the KNN model, the identification accuracy of other models for mock-up mural samples was above 99%. However, only the identification accuracy of 2D-CNN models reached above 94% for actual mural samples. Therefore, the 2D-CNN model was determined as the most suitable model for the identification and analysis of mural pigments

Non-Stacked γ-Fe2O3/C@TiO2 Double-Layer Hollow Nanoparticles for Enhanced Photocatalytic Applications under Visible Light

Herein, a non-stacked γ-Fe2O3/C@TiO2 double-layer hollow nano photocatalyst has been developed with ultrathin nanosheets-assembled double shells for photodegradation phenol. High catalytic performance was found that the phenol could be completely degraded in 135 min under visible light, due to the moderate band edge position (VB at 0.59 eV and CB at −0.66 eV) of the non-stacked γ-Fe2O3/C@TiO2, which can expand the excitation wavelength range into the visible light region and produce a high concentration of free radicals (such as ·OH, ·O2−, holes). Furthermore, the interior of the hollow composite γ-Fe2O3 is responsible for charge generation, and the carbon matrix facilitates charge transfer to the external TiO2 shell. This overlap improved the selection/utilization efficiency, while the unique non-stacked double-layered structure inhibited initial charge recombination over the photocatalysts. This work provides new approaches for photocatalytic applications with γ-Fe2O3/C-based materials

A Comparative Study of the Method to Rapid Identification of the Mural Pigments by Combining LIBS-Based Dataset and Machine Learning Methods

Due to the similar chemical composition and matrix effect, the accurate identification of mineral pigments on wall paintings has brought great challenges. This work implemented an identification study on three mineral pigments with similar chemical compositions by combining LIBS technology with the K-nearest neighbor algorithm (KNN), random forest (RF support vector machine (SVM), back propagation artificial neural network (Bp-ANN) and convolutional neural network (CNN) to find the most suitable identification method for mural research. Using the SelectKBest algorithm, 300 characteristic lines with the largest difference among the three pigments were determined. The identification models of KNN, RF, SVM, Bp-ANN and CNN were established and optimized. The results showed that, except for the KNN model, the identification accuracy of other models for mock-up mural samples was above 99%. However, only the identification accuracy of 2D-CNN models reached above 94% for actual mural samples. Therefore, the 2D-CNN model was determined as the most suitable model for the identification and analysis of mural pigments

Incorporation of tin into zirconium phosphate to boost efficient conversion of trioses to lactic acid

Dihydroxyacetone isomerization is a fundamental reaction for the production of lactic acid using different feedstocks. However, achieving excellent catalytic activity and resistance against leaching in water is challenging. Herein, we devised a Sn doped zirconium phosphate as effective heterogeneous catalyst. The incorporation of Sn could remarkably aggrandize the content of strong Lewis acid sites while retain relatively high surface areas. Gratifyingly, this catalyst exhibits enhanced activity and reusability for selective dihydroxyacetone isomerization into lactic acid with water as solvent. High lactic acid yields of 70.30 and 76.25% were achieved in water and water/acetone under optimal reaction conditions, respectively. The composition and activity of catalyst are reserved with reduced ions leaching. The excellent catalytic performance is attributed to accelerated conversion of pyruvaldehyde to lactic acid by the strong Lewis acid sites. Nuclear magnetic resonance revealed that the reaction is proceeded via a keto-enol tautomerization process

Molecular signatures associated with ZIKV exposure in human cortical neural progenitors

Zika virus (ZIKV) infection causes microcephaly and has been linked to other brain abnormalities. How ZIKV impairs brain development and function is unclear. Here we systematically profiled transcriptomes of human neural progenitor cells exposed to Asian ZIKV(C), African ZIKV(M), and dengue virus (DENV). In contrast to the robust global transcriptome changes induced by DENV, ZIKV has a more selective and larger impact on expression of genes involved in DNA replication and repair. While overall expression profiles are similar, ZIKV(C), but not ZIKV(M), induces upregulation of viral response genes and TP53. P53 inhibitors can block the apoptosis induced by both ZIKV(C) and ZIKV(M) in hNPCs, with higher potency against ZIKV(C)-induced apoptosis. Our analyses reveal virus- and strain-specific molecular signatures associated with ZIKV infection. These datasets will help to investigate ZIKV-host interactions and identify neurovirulence determinants of ZIKV