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Objective: This paper discusses the feasibility of using mineral element fingerprinting technology of to identify tobacco producing areas, screens out the effective indicators, and constructs the discrimination model of tobacco producing areas. Methods: The contents of 20 mineral elements in tobacco leaves from 11 producing areas were simultaneously determined by ICP-MS, and the data were analyzed by variance analysis, cluster analysis, principal component analysis and discriminant analysis. Results: The contents of 16 elements were significantly different among producing areas, and principal component analysis resulted in 6 principal components, with the cumulative variance contribution of 89.253%. Using linear discriminant analysis, four elementsmineral, K, Mn, Se and Ba, were screened as the effective indicators to discriminate the geographical origin of tobacco leaves. The established discriminant model could accurately distinguish tobacco leaf samples from different tobacco producing areas. Conclusion: Significant differences were showed in the contents of mineral elements in tobacco leaves from 11 producing areas. Four elements, K, Mn, Se and Ba, are important indicators for distinguishing tobacco producing areas. Mineral element fingerprinting technology can be used for distinguishing tobacco producing areas.

As a two-dimensional(2D) building block of new materials, graphene has received widespread attention due to its exceptional thermal properties. The thermal properties and recent advances on graphene-based material were reviewed. The intrinsic thermal conductivity of graphene and the effect of layers, defects and edge were briefly introduced. The resent research progress in graphene fiber as thermal conductivity material was analyzed and discussed. A variety of graphene films (graphene film, graphene hybrid film, graphene/polymer composite film) were grouped by category and the influencing factors of the thermal conductivity were reviewed. The structure, thermal conductivity property and current researches of 3D graphene (graphene with random orientation in the polymer matrix, graphene with specific orientation in the polymer matrix) were summarized. Finally, the challenges and prospects of graphene-based materials were also pointed out, especially inhigh power, highly integrated systems such as LED lighting and smart phones, graphene based thermal conductivity materials have a good development prospect.

The mass production of graphene sheets is a prerequisite for exploiting the applications of graphene. A major focus of experimental research has been concentrated on the development of effective approaches to produce well-defined graphene in large scale. Due to its high yield and cost efficiency, chemical reduction of graphene oxide has been proofed to be one of the most feasible approaches to achieve this goal. Graphene consists almost entirely of sp2 hybridized carbon atoms, while graphene oxide additionally contains sp3 regions, oxygen functional groups and structural defects. The fundamental understanding of the mechanism of chemical reduction of graphene oxide is a key issue when preparing graphene materials via graphene oxide precursor. Although dozens of methods have been exploited to facilitate the chemical reduction of graphene, only few studies were focused on the reduction mechanism.The chemical reduction strategies of graphene oxide, and as well as their mechanisms were reviewed in this paper. It was suggested that the core issue of the reduction is how to clarify the removal of hydroxyl groups and the simultaneous restoration of a conjugated structure.

The 600℃ high temperature titanium alloy composite with graphene oxide addition was prepared by temperature-controlled mixing method and hot isostatic pressing. Microstructure and mechanical properties of composite were studied by metallographic observation, energy spectrum analysis, phase analysis and tensile test. The results show that graphene oxides are dispersed uniformly in the 600℃ high temperature titanium alloy powder when the content of graphene oxide is 0.3%(mass fraction) and the main mode of action is physical adsorption. Compared with the alloy without graphene oxide, the microstructure of the composite with 0.3% graphene oxide is obviously refined, and the average size of equiaxed alpha phase is reduced by 36%. Meanwhile, the average room temperature tensile strength and yield strength increase by 7.8% and 10.4% respectively and Vickers hardness increases by 25.6%. The strengthening mechanisms of graphene oxide on 600℃ high temperature titanium alloy mainly include grain refinement strengthening,dislocation streng-thening and precipitation strengthening of the (TiZr)6Si3 second-phase.

Quasi-static compression experiments on graphene-reinforced aluminum matrix composites were carried out by means of microcomputer controlled electronic universal testing machine, while dynamic behavior of the composites at various high strain rates was determined by split hopkinson pressure bar (SHPB). In addition, scanning electron microscopy (SEM) was employed to examine the morphological feature of aluminum matrix composites reinforced respectively by grapheme and SiC. The results show that at all strain rate, the yield strength of aluminum is improved both with addition of graphene and SiC, by incorporation of graphene, the yield strength of aluminum is improved more significantly, but without affecting the strain hardening rate of the material. In comparison with SiC as reinforcements, use of graphene undermines strain rate sensitivity of the composites, and meanwhile results in a decline in ultimate strength. J-C and Z-A constitutive models were fitted respectively to the experimental results to obtain relative parameters. Comparison between the two models suggests that J-C model is more accurate in terms of describing stress-strain behavior of both composites reinforced respectively by graphene and SiC.

A three-dimensional mathematical model is developed based on the Navier-Stokes momentum equation and the low Reynolds number turbulence k-ε equation. This model comprehensively considers energy conservation,molten steel solidification and the influence of the mushy zone on the flow process. Heat flux obtained through an inverse heat transfer model combined with measured temperature is set as the boundary condition to the coupling model,to investigate fluid flow,heat transfer and solidification in the continuous casting mold. It is found that liquid steel flow has important influence on the distributions of temperature and heat flux,and the solidification process is governed by a synthetic action of fluid flow and heat flux. The proposed model and the non-uniform characteristic of solidification can provide reference for further investigation on longitudinal cracks and other surface defects during continuous casting.

A highly sensitive fluorescence sensor for monitoring low concentrations of hydrogen peroxide was designed. The sensor employs the commercially available palladium or platinum metal on activated charcoal as catalysts to decompose hydrogen peroxide into water and molecular oxygen. The produced oxygen concentration can be measured in real time using an oxygen-sensitive layer doped with photostable oxygen probes. The sensor exhibits high sensitivity that is able to measure hydrogen peroxide concentration down to 20 ppb and can measure hydrogen peroxide concentration in the range of 0.1–100 ppm and 0.02–100 ppm, respectively. The response is fully reversible and the typical response time is less than one minute, which makes it suitable to continuously measure hydrogen peroxide over a long duration. Due to the excellent batch-to-batch consistency of palladium or platinum metal on activated charcoal, the sensor can be massively produced with good reproducibility and affordable price, which holds great potential for constructing sensors for industrial and practical applications. [ABSTRACT FROM AUTHOR]

Biocatalysis is an attractive approach for the synthesis of chiral pharmaceuticals and fine chemicals, but assessing and/or improving the enantioselectivity of biocatalyst towards target substrates is often time and resource intensive. Although machine learning has been used to reveal the underlying relationship between protein sequences and biocatalytic enantioselectivity, the establishment of substrate fitness space is usually disregarded by chemists and is still a challenge. Using 240 datasets collected in our previous works, we adopt chemistry and geometry descriptors and build random forest classification models for predicting the enantioselectivity of amidase towards new substrates. We further propose a heuristic strategy based on these models, by which the rational protein engineering can be efficiently performed to synthesize chiral compounds with higher ee values, and the optimized variant results in a 53-fold higher E-value comparing to the wild-type amidase. This data-driven methodology is expected to broaden the application of machine learning in biocatalysis research. Machine learning has been used to reveal the relationship between protein sequences and biocatalytic enantioselectivity, but the establishment of substrate fitness space is challenging. Herein, the authors build random forest classification models for predicting the enantioselectivity of amidase toward different substrates, adopting chemistry and geometry descriptors. [ABSTRACT FROM AUTHOR]