高温石墨炉法测定痕量金属的条件选择

高温石墨炉法测定痕量金属对工作条件的要求非常严格,影响分析准确度和精密度的原因较多。从干燥温度和时间,灰化温度和时间,原子化温度和时间,高温除残温度和时间的选择,自动进样器的调整,试样处理等几个方面,合理、正确地选择工作条件能够得到最佳吸收灵敏度和测量精密度。     

太原南站将建成我国中西部重要交通枢纽

太原南站,一座在建的太原市标志性建筑。建成投入使用的太原南站,还将成为我国中西部的重要交通枢纽,并大幅提升太原市在全国铁路交通的地位以及城市的辐射力和凝聚力。太原南站作为山西第一座大型现代化火车站,对接了石太客运专线、大西客运专线以及太中银等线路,在我国中西部的枢纽作用十分重要。2012年10月初,太原南站站场内,主体建筑和内装修施工基本完工,铁路线引入线工程也已基本完成,站场钢结构雨棚吊装只剩东南一角尚在施工。在这里两台汽车起重机和一台履带起重机,正     

Solid state reaction synthesis and photoluminescence properties of Dy3+ doped NaGd(MoO4)2 phosphor

Dy³⁺ doped NaGd(MoO₄)₂ phosphors were synthesized by a traditional solid-state reaction route using NH₄HF₂ as a flux. The influence of calcination temperature on the crystal structure and spectral properties was studied, and the optimum calcination temperature for producing Dy³⁺ doped NaGd(MoO₄)₂ phosphor was experimentally confirmed. The concentration quenching of Dy³⁺ fluorescence and excitation-wavelength dependent spectroscopic properties were studied. On the base of both the Van Uitert's and I-H models, the electric dipole–dipole (D–D) interaction was ascribed to be the main physical mechanism responsible for energy transfer between Dy³⁺ ions. It was also discovered that the color coordinates of the Dy³⁺ doped NaGd(MoO₄)₂ phosphor depends on the Dy³⁺ doping concentration and the excitation wavelength.     

WC nano-particle surface injection via laser shock peening onto 5A06 aluminum alloy

A novel technique, named nano-particle surface injection via laser shock peening-NPSI/LSP, for surface modification of light alloys is reported. The WC nano-particles were implanted into a 5A06 aluminum alloy surface under a very high pressure (up to Giga or even tens of Giga Pascal), produced by a Q-switched pulsed Nd:Glass laser system. The results confirmed that the surface performance of the 5A06 aluminum alloy have been dramatically improved by the NPSI/LSP process based on an integrated strengthening mechanism of laser shock peening, nano-particles and nano-particle intensified shock peening.     

纳米CeO2在聚合物中的应用

综述了纳米CeO2在提高聚合物的抗老化性能、力学性能、热稳定性能、导电性能及纳米CeO2/聚合物复合材料作为催化材料方面的应用。     

Enhanced photocatalytic degradation performance of organic contaminants by heterojunction photocatalyst BiVO4/TiO2/RGO and its compatibility on four different tetracycline antibiotics

Photocatalytic performance of four tetracycline antibiotics using BiVO₄/TiO₂/RGO composites was investigated. To make full use of catalysis, optimum preparation conditions involved RGO content, solution pH and hydrothermal temperature on the structure forming of BiVO₄/TiO₂/RGO composites were investigated. Subsequently, the obtained visible light-driven photocatalyst was used to degrade four kinds of tetracycline antibiotics involved tetracycline (TC), chlortetracycline (CTC), oxytetracycline (OTC) and doxycycline (DXC) for wastewater treatment. Results showed that BiVO₄/TiO₂/RGO photocatalyst exhibited excellent photocatalytic activity and high compatibility due to the enhanced separation efficiency of photo-generated carriers with high reduction and oxidation capability. The degradation process of four kinds of tetracycline antibiotics was traced and detected through identifying intermediates produced in the reaction system. And a possible catalytic mechanism for BiVO₄/TiO₂/RGO photocatalyst was put forward based on band gap structure of BiVO₄ and TiO₂.     

随机截尾机电设备三参数威布尔分布模型及其应用研究

针对某型飞机机电设备故障率较高的实际问题,提出首先采用平均秩次法进行故障数据预处理,其次假设数据服从三参数威布尔分布并采用相关系数优化法和最小二乘法相结合求得相关参数,最后采用K-S检验法和蒙特卡洛仿真模拟,对可靠性评估结果进行了检验.通过实例验证得出,提出的模型可以准确地描述机电设备在实际使用环境中的故障情况,计算过程简单,便于编程实现.     

Predicting elastic modulus of porous La0.6Sr0.4Co0.2Fe0.8O3-δ cathodes from microstructures via FEM and deep learning

In this work, a deep learning accelerated homogenization framework is developed for prediction of elastic modulus of porous materials directly from their inner microstructures. The finite element method (FEM) and the homogenization theory are used to obtain the macroscopic properties of materials based on their microstructures. Based on a large dataset consisting of various microstructures and corresponding elastic properties via FEM, a deep convolutional neural network (CNN) is trained to capture the nonlinear functional relationship between the microstructure features and their macroscopic elastic properties. The deep learning model is finally well validated against extra new samples with excellent predictive performances. This demonstrates that the CNN deep learning model can be trusted as a surrogate model for the FEM based homogenization method, with the computation time being reduced by several orders of magnitude. The proposed deep learning framework is highly extendable for prediction of various macroscopic properties from microstructures.