Microwave hybrid fast sintering of red clay ceramics

This work aimed to examine the performance of the hybrid sintering of clay ceramic in a microwave furnace, compared to the sintering process in a conventional furnace. The raw materials were subjected to X-ray fluorescence, loss on ignition (LOI), X-ray diffraction, particle size distribution, real specific mass, and thermogravimetric analyses. The red clay ceramic mass was prepared, extruded, pre-sintered in a conventional furnace at 600°C/60 min, and sintered at temperatures between 700 °C and 1100 °C. The sintering conventional (resistive oven) was carried out for 60 min with a heating rate of 10°C/min. In the microwave furnace, the sintering times were 5, 10, and 15 min, with a heating rate of 50°C/min, with a sintering chamber coated with silicon carbide (susceptor). The sintered specimens were characterized according to linear shrinkage, water absorption, apparent porosity, apparent specific mass, X-ray diffraction, Raman spectroscopy analysis, spectroscopy analysis in the ultraviolet and visible regions, microhardness, and scanning electron microscopy. The results showed that microwave sintering promoted an increase in the microhardness and apparent specific mass, and reduction in water absorption and apparent porosity values, due to greater densification in the microstructure. The best results occurred for specimens sintered at 1100°C.     

Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.     

新型无机内保温涂层的性能及应用

根据水泥烧成热耗的组成，降低高温设备表面散热是降低水泥烧成热耗的重要途径之一，而减少高温窑炉墙壁的热传导可有效降低设备的表面散热。本文在介绍无机内保温涂层隔热原理的基础上，对保温涂层的应用效果进行了对比研究，通过在传统耐火隔热材料的基础上增加新型无机内保温涂层，可有效降低高温设备外表面温度，减少水泥生产中的散热损失，达到节能降耗的目的。     

新型地锚张弦梁式泥石流格栅坝构件受力的简化计算方法

针对常见泥石流防治结构被冲击破坏的问题，基于“柔性消能”理念，结合张弦梁结构和竖向预应力锚杆技术，提出一种既能改善结构受力性能、增加结构整体抗冲击能力、保证结构安全可靠，又能减小结构构件截面尺寸、节约成本、便于现场施工组装和后期运营维护的新型地锚张弦梁式泥石流格栅坝，并阐述其技术原理。根据泥石流荷载分布和新型地锚张弦梁式泥石流格栅坝的受力特征，给出其简化的内力计算方法；并利用SAP2000建立新结构有限元模型，分析了结构的整体受力，验证了构件简化计算方法的合理性；结合Python语言和Qt De? signer软件开发了相应的设计计算软件，对新型地锚张弦梁式泥石流格栅坝的内力进行求解。结果表明：提出的新结构抗冲击性能好，构件受力均匀；以后设计中应关注竖杆的剪切脆性破坏和立柱偏心受力情况，保证结构安全；变形协调仅使底层张弦梁与竖杆内力偏大，实际工程应用时，应着重验算底层构件，防止其破坏；文中提出的简化计算方法能较准确的反映结构的受力特性，具有一定的合理性，研究可为新型地锚张弦梁式泥石流格栅坝的设计计算和推广应用提供理论依据和技术支持。     

Effects on the crystallite growth behavior of LaPO4 nanopowders and its mechanical properties

The effects of pH of the reaction solution and the concentration of phosphoric acid on the crystal growth behavior of LaPO₄ crystallites were investigated and the mechanical properties of rare-earth phosphates were compared. As a result, the concentration of phosphoric acid of 10% was beneficial to the crystal growth of LaPO₄ nanocrystalline. When the pH value of the reaction solution was 2, the size of LaPO₄ crystallites increased gradually with the increasing reaction temperature, and the smallest crystallite size of 43.27 nm was obtained after heat-treatment at 1000 °C. Simultaneously, the activation energy for crystal growth of LaPO₄ nanocrystalline was relatively lower (26.82 kJ mol⁻¹). With the decreasing radii of rare-earth ions, the hardness, Young's modulus and fracture toughness of the bulk rare-earth phosphates exhibited a reduced tendency, resulted from the increase of porosity under the same preparation process.     

Strong tribocatalytic dye degradation by tungsten bronze Ba4Nd2Fe2Nb8O30

The triboelectric effect has recently demonstrated its great potential in environmental remediation and even new energy applications for triggering a number of catalytic reactions by utilizing trivial mechanical energy. In this study, Ba₄Nd₂Fe₂Nb₈O₃₀ (BNFN) submicron powders were used to degrade organic dyes via the tribocatalytic effect. Under the frictional excitation of three PTFE stirring rods in a 5 mg/L RhB dye solution, BNFN demonstrates a high tribocatalytic degradation efficiency of 97% in 2 h. Hydroxyl radicals (?OH) and superoxide radicals (?O₂－) were also detected during the catalysis process, which proves that triboelectric energy stimulates BNFN to generate electron-hole pairs. The tribocatalysis of tungsten bronze BNFN submicron powders provides a novel and efficient method for the degradation of wastewater dye by utilizing trivial mechanical energy.     

粉体含能材料生产与储运中的静电安全及防护

分析了静电产生的原因,阐述了粉体含能材料生产中的静电起电现象、静电的危害、静电安全性评估标准以及建立在此标准基础上的静电放电危险的评价办法,提出了粉体含能材料在生产、运输中所需要采取的静电防护措施。     

基于面元素重排的三维网格模型数据隐藏算法

目的 为了解决目前三维数据隐藏算法不能兼顾无失真和盲提取的问题，提出一种新的完全无失真的三维网格模型数据隐藏盲算法。方法 首先使用混沌逻辑映射选择嵌入与提取模式，保证数据的安全性。然后利用面元素重排，完全不会造成三维模型失真的性质，通过不同嵌入模式规则对三角面元素进行重排，以嵌入秘密数据。接收端则可根据相应的提取模式规则提取秘密数据。结果 仿真结果与分析表明，该算法不会对三维模型造成任何失真，嵌入容量为每顶点2比特，且能抵抗仿射变换攻击、噪声攻击和平滑攻击等。结论 这种三维数据隐藏盲算法无失真，容量大、安全性高、鲁棒性强，适用于三维载体不容修改的情形，如军事、医学、秘密通信和版权保护等。     

Efficient Visible-to-UV Photon Upconversion Systems Based on CdS Nanocrystals Modified with Triplet Energy Mediators

Developing high-performance visible-to-UV photon upconversion systems based on triplet–triplet annihilation photon upconversion (TTA-UC) is highly desired, as it provides a potential approach for UV light-induced photosynthesis and photocatalysis. However, the quantum yield and spectral range of visible-to-UV TTA-UC based on nanocrystals (NCs) are still far from satisfactory. Here, three different sized CdS NCs are systematically investigated with triplet energy transfer to four mediators and four annihilators, thus substantially expanding the available materials for visible-to-UV TTA-UC. By improving the quality of CdS NCs, introducing the mediator via a direct mixing fashion, and matching the energy levels, a high TTA-UC quantum yield of 10.4% (out of a 50% maximum) is achieved in one case, which represents a record performance in TTA-UC based on NCs without doping. In another case, TTA-UC photons approaching 4 eV are observed, which is on par with the highest energies observed in optimized organic systems. Importantly, the in-depth investigation reveals that the direct mixing approach to introduce the mediator is a key factor that leads to close to unity efficiencies of triplet energy transfer, which ultimately governs the performance of NC-based TTA-UC systems. These findings provide guidelines for the design of high-performance TTA-UC systems toward solar energy harvesting.     

Prediction of Flooding in Packed Liquid-Liquid and High-Pressure Extraction Columns Using a Gaussian Process

Reliable prediction of flooding conditions is needed for sizing and operating packed extraction columns. Due to the complex interplay of physicochemical properties, operational parameters and the packing-specific properties, it is challenging to develop accurate semi-empirical or rigorous models with a high validity range. State of the art models may therefore fail to predict flooding accurately. To overcome this problem, a data-driven model based on Gaussian processes is developed to predict flooding for packed liquid-liquid and high-pressure extraction columns. The optimized Gaussian process for the liquid-liquid extraction column results in an average absolute relative error (AARE) of 15.23 %, whereas the algorithm for the high-pressure extraction column results in an AARE of 13.68 %. Both algorithms can predict flooding curves for different packing geometries and chemical systems precisely.