Transient liquid phase sintering of high-purity mullite for high-temperature structural ceramics

High-purity mullite ceramics, promising engineering ceramics for high-temperature applications, were fabricated using transient liquid phase sintering to improve their high-temperature mechanical properties. Small amounts of ultrafine alumina or silica powders were uniformly mixed with the mullite precursor depending on the silica-alumina ratio of the resulting ceramics to allow for the formation of a transient liquid phase during sintering, thus, enhancing densification at the early stage of sintering and mullite formation by the reaction between additional alumina and the residual glassy phase (mullitization) at the final stage of sintering. The addition of alumina powder to the silica-rich mullite precursor resulted in a reaction between the glassy silica and alumina phases during sintering, thereby forming a mullite phase without inhibiting densification. The addition of fine silica powder to the mullite single-phase precursor led to densification with an abnormal grain growth of mullite, whereas some of the added silica remained as a glassy phase after sintering. The resulting mullite ceramics prepared using different powder compositions showed different sintering behaviors, depending on the amount of alumina added. Upon selecting an optimum process and the amount of alumina to be added, the pure mullite ceramics obtained via transient liquid phase sintering exhibited high density (approximately 99%) and excellent high-temperature flexural strength (approximately 320 MPa) at 1500 °C in air. These results clearly demonstrate that pure mullite ceramics fabricated via transient liquid phase sintering with compositions close to those of stoichiometric mullite could be a promising process for the fabrication of high-temperature structural ceramics used in an ambient atmosphere. The transient liquid phase sintering process proposed in this study could be a powerful processing tool that allows for the preparation of superior high-temperature structural ceramics used in the ambient processing atmosphere.     

Optimization of hydrogen enrichment via palladium membrane in vacuum environments using Taguchi method and normalized regression analysis

In this study, the separation of hydrogen from gas mixtures using a palladium membrane coupled with a vacuum environment on the permeate side was studied experimentally. The gas mixtures composed of H₂, N₂, and CO₂ were used as the feed. Hydrogen permeation fluxes were measured with membrane operating temperature in the range of 320–380 °C, pressures on the retentate side in the range of 2–5 atm, and vacuum pressures on the permeate side in the range of 15–51 kPa. The Taguchi method was used to design the operating conditions for the experiments based on an orthogonal array. Using the measured H₂ permeation fluxes from the Taguchi approach, the stepwise regression analysis was also employed for establishing the prediction models of H₂ permeation flux, followed by the analysis of variance (ANOVA) to identify the significance and suitability of operating conditions. Based on both the Taguchi approach and ANOVA, the H₂ permeation flux was mostly affected by the gas mixture composition, followed by the retentate side pressure, the vacuum degree, and the membrane temperature. The predicted optimal operating conditions were the gas mixture with 75% H₂ and 25% N₂, the membrane temperature of 320 °C, the retentate side pressure of 5 atm, and the vacuum degree of 51 kPa. Under these conditions, the H₂ permeation flux was 0.185 mol s^?1 m^?2. A second-order normalized regression model with a relative error of less than 7% was obtained based on the measured H₂ permeation flux.     

Experimental study on mitigating wind erosion of calcareous desert sand using spray method for microbially induced calcium carbonate precipitation

Wind erosion is one of the significant natural calamities worldwide, which degrades around one-third of global land. The eroded and suspended soil particles in the environment may cause health hazards, i.e. allergies and respiratory diseases, due to the presence of harmful contaminants, bacteria, and pollens. The present study evaluates the feasibility of microbially induced calcium carbonate precipitation (MICP) technique to mitigate wind-induced erosion of calcareous desert sand (Thar desert of Rajasthan province in India). The temperature during biotreatment was kept at 36 °C to stimulate the average temperature of the Thar desert. The spray method was used for bioaugmentation of Sporosarcina (S.) pasteurii and further treatment using chemical solutions. The chemical solution of 0.25 pore volume was sprayed continuously up to 5 d, 10 d, 15 d, and 20 d, using two different concentration ratios of urea and calcium chloride dihydrate viz 2:1 and 1:1. The biotreated samples were subjected to erosion testing (in the wind tunnel) at different wind speeds of 10 m/s, 20 m/s, and 30 m/s. The unconfined compressive strength of the biocemented crust was measured using a pocket penetrometer. The variation in calcite precipitation and microstructure (including the presence of crystalline minerals) of untreated as well as biotreated sand samples were determined through calcimeter, scanning electron microscope (SEM), and energy-dispersive X-ray spectroscope (EDX). The results demonstrated that the erosion of untreated sand increases with an increase in wind speeds. When compared to untreated sand, a lower erosion was observed in all biocemented sand samples, irrespective of treatment condition and wind speed. It was observed that the sample treated with 1:1 cementation solution for up to 5 d, was found to effectively resist erosion at a wind speed of 10 m/s. Moreover, a significant erosion resistance was ascertained in 15 d and 20 d treated samples at higher wind speeds. The calcite content percentage, thickness of crust, bulk density, and surface strength of biocemented sand were enhanced with the increase in treatment duration. The 1:1 concentration ratio of cementation solution was found effective in improving crust thickness and surface strength as compared to 2:1 concentration ratio of cementation solution. The calcite crystals formation was observed in SEM analysis and calcium peaks were observed in EDX analysis for biotreated sand.     

Co2FeO4@rGO composite: Towards trifunctional water splitting in alkaline media

Development of efficient, low cost and multifunctional electrocatalysts for water splitting to harvest hydrogen fuels is a challenging task, but the combination of carbon materials with transition metal-based compounds is providing a unique and attractive strategy. Herein, composite systems based on cobalt ferrite oxide-reduced graphene oxide (Co₂FeO₄) @(rGO) using simultaneous hydrothermal and chemical reduction methods have been prepared. The proposed study eliminates one step associated with the conversion of GO into rGO as it uses direct GO during the synthesis of cobalt ferrite oxide, consequently rGO based hybrid system is achieved in-situ significantly, the optimized Co₂FeO₄@rGO composite has revealed an outstanding multifunctional applications related to both oxygen evolution reaction (OER) and hydrogen counterpart (HER). Various metal oxidation states and oxygen vacancies at the surface of Co₂FeO₄@rGO composites guided the multifunctional surface properties. The optimized Co₂FeO₄@rGO composite presents excellent multifunctional properties with onset potential of 0.60 V for ORR, an overpotential of 240 mV at a 20 mAcm^?2 for OER and 320 mV at a 10 mAcm^?2 for HER respectively. Results revealed that these multifunctional properties of the optimized Co₂FeO₄@ rGO composite are associated with high electrical conductivity, high density of active sites, crystal defects, oxygen vacancies, and favorable electronic structure arisinng from the substitution of Fe for Co atoms in binary spinel oxide phase. These surface features synergistically uplifted the electrocatalytic properties of Co₂FeO₄@rGO composites. The multifunctional properties of the Co₂FeO₄@ rGO composite could be of high interest for its use in a wide range of applications in sustainable and renewable energy fields.     

Effect of micro-cracks on the in-plane electronic conductivity of proton exchange membrane fuel cell catalyst layers based on lattice Boltzmann method

Micro-cracks commonly occur on the catalyst layers (CLs) during the manufacturing of catalyst coated membranes (CCMs). However, the crack shape parameters effect on CLs in-plane (IP) electronic conductivity λ_s is not clear. In this work, the relationship between crack parameters and the λ_s is obtained based on the two-dimensional (2D) multiple-relaxation time (MRT) lattice Boltzmann method (LBM). The LBM numerical model is validated by the normalized λ_s experiment applied on three different home-made cracked CLs, and the parameter study focus on crack width, length, quantity and phase angle are carried out. The results show that the decrease of λ_s has different sensitivity |k| to the parameters above. The crack width has little effect on λ_s decrease, and the |k_w| is 0.038. However, crack arm length and quantity show more significant impact, which |k_l| and |k_N| are 0.753 and 0.725, respectively. The CLs with different crack propagation directions show significant anisotropy on λ_s, and a 53.53% decrease in λ_s is observed between 0° and 90° crack phase angle change. To manufacture a high electronic conductivity CL, crack initiation and migration mitigation are highly encouraged.     

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。