Thermal properties of hierarchical YSZ and LZO ceramic microspheres with multi-scaled voids investigated by using theoretical,experimental and simulation methods

Heat transfer within ceramic feedstock powders is still unclear, which impedes optimization of the thermal and mechanical properties of the thermal sprayed coatings. The microspheres (yttria-stabilized zirconia YSZ and lanthanum zirconate LZO) were prepared via the electro-spraying assisted phase inversion method (ESP). The thermal properties of the two ESP microspheres and a commercial hollow spherical powder (HOSP) were investigated by using theoretical, experimental, and simulation methods. Thermal conductivity of the single microsphere was estimated via a novel nest model that was derived from the Maxwell-Eucken 1 and the EMT model. Thermal conductivity of a single YSZ/LZO-ESP microsphere prepared at 1100–1200 °C was within 0.36–0.75 W/m K, which was ～ 20 % lower than that of a single YSZ-HOSP microsphere with a similar porosity. Heat flux simulation showed that high tortuosity around the multi-scaled voids of the ESP microsphere led to a more efficient decrease in thermal conductivity compared with total porosity.     

Novel proton-conducting layered perovskite based on BaLaInO4 with two different cations in B-sublattice: Synthesis,hydration, ionic (O2−, H+) conductivity

The present study focused on the novel material with significantly improved properties for the application in the area of clean energy. The new complex oxide BaLaIn_0·5Y_0·5O₄ with layered perovskite structure was obtained for the first time. It was proved that the introduction of Y³⁺ ions in the perovskite layer of BaLaInO₄ leads i) to the rise of the oxygen-ionic conductivity due to the increase in mobility of oxygen ions as a result of the expand of the cell volume and ii) to the enhancement of protonic conductivity due to the increase in the proton concentration and mobility. The sample BaLaIn_0·5Y_0·5O₄ is nearly pure proton conductor below 400 °C and has the protonic conductivity value 1.6?10^?5 S/cm at this temperature.     

High voltage and self-healing zwitterionic double-network hydrogels as electrolyte for zinc-ion hybrid supercapacitor/battery

The hydrogel electrolyte is an important part of safety and development potential in zinc-based energy storage equipment due to its inherent low mechanical strength and voltage decomposition. However, hydrogel electrolytes possess a reduced working life for zinc dendrites growth and a narrow voltage window. In this study, a hydrogel electrolyte prepares by the zwitterionic monomer [2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl) (MS) and sodium alginate (SA) alleviate these problems. The zwitterionic double-network hydrogel has good mechanical strength, inhibits the growth of zinc dendrites, enhances practicability, greatly increases the voltage window (0–2.4 V), and has self-healing properties to its rich functional groups. The assembled zinc-ion hybrid supercapacitors (ZHSs) have a high-power density of 172.33 W kg^?1 and an energy density of 88.56 Wh·kg^?1 at 0.5 A g^?1. The assembled zinc-ion battery also has good electrochemical performance. Flexible ZHSs and batteries provide power to the timer stably under different bending angles. The zwitterionic double-network hydrogel can be applied to both zinc-based supercapacitors and batteries.     

Investigation into adsorption equilibrium and thermodynamics for water vapor on montmorillonite clay

The isothermal adsorption curves for water vapor on montmorillonite were measured by a gravimetric adsorption system. Dent's model was employed to estimate the adsorption behaviors of water vapor on primary adsorption sites and secondary adsorption sites. The thermodynamics analysis of water vapor adsorption was performed. At low vapor pressure region, primary adsorption predominates, and with increasing vapor pressure, secondary adsorption becomes notable. Primary adsorption sites have an evidently stronger adsorption affinity than secondary adsorption sites. With increasing vapor pressure, Gibbs free energy variation rapidly increases and then reduces slowly. Although increasing vapor pressure raises adsorption spontaneity on primary adsorption sites, the enhancement in vapor pressure decreases the spontaneity of water vapor adsorption on secondary adsorption sites. As adsorbed loading increases, isosteric heat of adsorption and entropy loss decrease first and then increase quickly. The gradually growing water clusters are responsible for the increase of entropy loss at late stage.     

Feasibility of high performance in p-type Ge1−xBixTe materials for thermoelectric modules

GeTe is a promising candidate for the fabrication of high-temperature segments for p-type thermoelectric (TE) legs. The main restriction for the widespread use of this material in TE devices is high carrier concentration (up to ∼ 10²¹ cm⁻³), which causes the low Seebeck coefficient and high electronic component of thermal conductivity. In this work, the band structure diagram and phase equilibria data have been effectively used to attune the carrier concentration and to obtain the high TE performance. The Ge_1−xBi_xTe (x = 0.04) material prepared by the Spark plasma sintering (SPS) technique demonstrates a high power factor accompanied by moderate thermal conductivity. As a result, a significantly higher dimensionless TE figure of merit ZT = 2.0 has been obtained at ∼ 800 K. Moreover, we are the first to propose that application of the developed Ge_1−xBi_xTe (x = 0.04) material in the TE unicouple should be accompanied by SnTe and CoGe₂ transition layers. Only such a unique solution for the TE unicouple makes it possible to prevent the negative effects of high contact resistance and chemical diffusion between the segments at high temperatures.     

基于卷积神经网络的5G蜂窝网络无线定位方法

5G蜂窝网络发展迅猛，其覆盖面积将逐渐增大，因此使用5G蜂窝网络进行定位是有研究潜力的研究方向。本文提出一种新的深度学习技术来实现高效、高精度和低占用的定位，以代替传统指纹定位过程中繁重的指纹库生成以及距离计算。该方法建立了一个特殊的卷积神经网络，并根据5G天线信号的接收信号强度指示、相位和到达角等特征量，选择合适的输入数据格式构造样本组建训练集，对该卷积神经网络进行训练。训练得到的卷积神经网络可以替代指纹定位中的庞大指纹库，非常有利于直接在5G移动设备端实现定位。虽然卷积神经网络在训练过程中需要大量时间，但在训练完毕后直接进行分类定位的速度非常快，可以保障定位实现的实时性。本文所实现的卷积神经网络权重与偏置所占内存不到0.5 MB，且能够在实际应用环境中以95%的定位准确率以及0.1 m的平均定位精度实现高精度定位。     

疫情下电磁场理论课程的网络教学

本文主要总结了新冠疫情期间作者的电磁场理论课程在线教学经验。对比分析了录播和直播的优缺点后,选择录播教学方式。基于超星网络教学平台,展示了录播网络教学的具体措施,包括网上答疑和学习效果检查以及在线批改作业等。给出了网络教学可以为线下教学继续使用的方法和手段,为疫情结束后的正常教学提供了新的网络教学补充措施。     

5G核心网业务模型的智能化预测研究

摘 要：核心网业务模型的建立是5G网络容量规划和网络建设的基础，通过现有方法得到的理论业务模型是静态不可变的且与实际网络存在偏离。为了克服现有5G核心网业务模型与现网模型适配性较差以及规划设备无法满足用户实际业务需求的问题，提出了一种长短期记忆（long short-term memory，LSTM）网络与卷积LSTM （convolution LSTM，ConvLSTM）网络双通道融合的 5G 核心网业务模型预测方法。该方法基于人工智能（artificial intelligence，AI）技术以实现高质量的核心网业务模型的智能预测，形成数据反馈闭环，实现网络自优化调整，助力网络智能化建设。