Engineering grain boundary anisotropy to elucidate grain growth behavior in alumina

Current grain growth models have evolved to account for the relationship between grain boundary energy/mobility anisotropy and the five degrees of grain boundary character. However, the role of grain boundary networks on overall growth kinetics remains poorly understood. To experimentally investigate this problem, a highly textured Al₂O₃ was fabricated by colloidal casting in a strong magnetic field to engineer a unique spatial distribution of grain boundary character. Microstructural evolution was quantified and compared to an untextured sample. From this comparison, a prevalence of (0001)/(0001) terminated grain boundaries with anisotropic networks were identified in the textured sample. These boundaries and their networks were found to be driving grain growth at a faster rate than predicted by models. These findings will allow better modelling of grain growth in real systems by experimentally exploring the impact thereon of grain boundary plane anisotropy and relative energy/mobility differences between neighboring boundaries.     

A Complexing Agent to Enable a Wide-Temperature Range Bromine-Based Flow Battery for Stationary Energy Storage

Bromine-based flow batteries (Br-FBs) are considered one of the most promising energy storage systems due to their features of high energy density and low cost. However, they generally suffer from uncontrolled diffusion of corrosive bromine particularly at high temperatures. That is because the interaction between polybromide anions and the commonly used complexing agent (N–methyl–N–ethyl–pyrrolidinium bromide [MEP]) decreases with increasing temperatures, which causes serious self-discharge and capacity fade. Herein, a novel bromine complexing agent, 1–ethyl–2–methyl–pyridinium bromide (BCA), is introduced in Br-FBs to solve the above problems. It is proven that BCA can combine with polybromide anions very well even at a high temperature of 60 °C. Moreover, the BCA contributes to decreasing the electrochemical polarization of Br⁻/Br₂ couple, which in turn improves their power density. As a result, a zinc–bromine flow battery with BCA as the complexing agent can achieve a high energy efficiency of 84% at 40 mA cm⁻², even at high temperature of 60 °C and it can stably run for more than 400 cycles without obvious performance decay. This paper provides an effective complexing agent to enable a wide temperature range Br-FB.     

Inductive effect in Mn-doped ZnO nanoribon arrays grown on Ni foam: A promising key for boosted capacitive and high specific energy supercapacitors

Transition metal oxides have been explored in supercapacitor applications owing to their safety, low cost, high specific capacitance and high electrochemical activity. Among all transition metal oxides, zinc oxide based materials show remarkable response for designing the supercapacitors with high electrochemical activity. Here in, Mn doped ZnO (Zn_1-xMn_xO₃ with x = 0, 0.25, 0.50, 0.75 and 1) was synthesized by a facile hydrothermal method. Doping of Mn into the ZnO increased the surface area and decease the charge transfer resistance for the Zn_0.5Mn_0.5O₃. All the synthesized materials were characterized by x-ray diffraction (XRD), scanning electron microscopy SEM), BET, electrochemical tests and other various analytical techniques to confirm the structural, morphological, textural and suprcapacitive properties. The synthesized material Zn_0.5Mn_0.5O₃ having the porous nanoribons structure with BET surface area (2490 cm²/g). The electrochemical studies showed significantly enhanced response toward pseudocapacitive nature. The synthesized material exhibited the excellent specific capacitance (515F/g), specific energy (28.61 Wh/kg) and specific power (1000 W/kg) at current density of 2 mA/g. Such impressive and superior properties make the MnZnO₃ material as promising candidate for new generation supercapacitor applications.     

Rapid synthesis of garnet-type Li7La3Zr2O12 solid electrolyte with superior electrochemical performance

Li₇La₃Zr₂O₁₂-based garnet-type solid electrolytes are promising candidates for use in all-solid-state lithium batteries (ASSLBs). However, their potential in large-scale commercial applications is largely hindered by the time/energy-consuming and lithium-wasting synthetic method which typically needs a long-duration high temperature solid state reaction process. Herein we invent a fast preparation route that involves a short-period thermal reaction (1100 °C for 10 min) in laboratory muffle furnaces following by conventional hot pressing technique to get almost fully dense (Al, Ga, Ta, Nb)-doped garnet-type electrolytes with high phase purity (>99.9 %). The large and compact grains, low porosity and high phase purities of garnet ceramic electrolytes synthesized in this study ensure superior electrochemical performance. Particularly, Ga-doped cubic Li₇La₃Zr₂O₁₂ shows extremely low E_a values (0.17?0.18 eV) and record-high lithium ionic conductivities (>2 × 10^?3 S cm^-1 at 25 °C).     

Super-Resolution Reconstruction of Images Based on Microarray Camera

In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array images. The array images are interpolated to obtain a HR image initially followed by a convolution neural network (CNN) procedure for enhancement. The convolution layers of our convolution neural network are 3×3 or 1×1 layers, of which the 1×1 layers are used to improve the network performance particularly. A bottleneck structure is applied to reduce the parameter numbers of the nonlinear mapping and to improve the nonlinear capability of the whole network. Finally, we use a 3×3 deconvolution layer to significantly reduce the number of parameters compared to the deconvolution layer of FSRCNN-s. The experiments show that the proposed method can not only ameliorate effectively the texture quality of the target image based on the array images information, but also further enhance the quality of the initial high resolution image by the improved CNN.     

面向电信承载网的监控系统设计与实现

面对电信承载网连接的日益增长的海量终端设备，运营商需要结合网络拓扑对终端设备产生的数据进行高效的汇聚统计、异常分析、故障定位处理等操作。针对已有系统存在的操作困难、分析效率低等问题，设计与实现了一个面向电信承载网的高效监控系统，提供实时与离线数据分析和多维可视化分析的能力。对网管、认证、终端等系统及设备采集的数据进行结构化存储，对采集的数据进行拓扑相关性和时间序列方法分析，根据分析结果实现基于动态阈值控制的异常实时告警、定位等操作，并提供多维度可视化分析对网络状态进行实时监控。实际应用结果表明，该系统性能优异，具有良好交互性，能较好地满足承载网运维人员业务分析需求。     

基于组合双向拍卖的云资源调度方法

针对跨数据中心的资源调度问题，提出了一种基于组合双向拍卖（PCDA）的资源调度方案。首先，将云资源拍卖分为三个部分：云用户代理报价、云资源提供商要价、拍卖代理组织拍卖；其次，在定义用户的优先级及任务紧迫度的基础上，在拍卖过程中估算每一个工作发生的服务等级协议（SLA）违规并以此计算云提供商的收益，同时每轮竞拍允许成交多项交易；最终达到根据用户等级合理分配云资源调度的效果。仿真实验结果表明该算法保证了竞拍成功率，与传统一次拍卖成交一项的组合双向拍卖方案相比，PCDA在竞拍时间段产生的能耗降低了35.00%，拍卖云提供商的利润提高了约38.84%。     

Potential of renewable energy system utilisation at decentralised level in the state of Uttarakhand in India

ABSTRACT

Design and implementation of an effective dissemination programme for decentralised renewable energy system necessitate an accurate estimate of its utilisation potential. Hence, in this study, an attempt has been made to develop frameworks to estimate the utilisation potential of decentralised renewable energy systems in the state of Uttarakhand in India. Estimations imply large resource, technical and economic potentials of the domestic solar water heater, solar home system, solar lantern, family size biogas plant and improved biomass cookstove in Uttarakhand. With higher impact on the purchasing power of households, prevailing soft loan scheme has been found to be more appropriate than a capital subsidy for promoting the usage of decentralised renewable energy systems.     

Design of disturbance observer based on adaptive-neural control for large-scale time-delay systems in the presence of actuator fault and unknown dead zone

This article presents an adaptive neural compensation scheme for a class of large-scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov–Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.     

煤磨热风管道改造的效果

中联万基水泥有限公司4500t/d熟料生产线采用SCLW4-12×13.4第四代篦冷机,煤磨热风取风口和余热发电AQC热风取风口在篦冷机同一对称位置,煤磨与余热发电系统存在争风现象,开停煤磨对余热发电系统影响较大。通过从废气管道处开孔,加装管道和电动阀,与热风管道汇合通往煤磨沉降室,调整相应阀门开度,可以控制入煤磨烟气温度,这样不但提高了余热发电量,而且提高了煤磨产量,同时也降低了窑头排风机的负荷,降低了熟料生产电耗。