Co?N graphene encapsulated cobalt catalyst for H2O2 decomposition under acidic conditions

Hydrogen peroxide (H₂O₂) has been listed as one of the 100 most important chemicals in the world. However, huge amount of residual H₂O₂ is hard to timely decomposed into O₂ and H₂O under acidic condition, easily resulting in explosion hazard. Here, we reported a core–shell structure catalyst, that is graphene with Co N structure encapsulated Co nanoparticles. Co N graphene shell serves as the active site for the H₂O₂ decomposition, and Co core further enhance this decomposition. Benefiting from it, the H₂O₂ decomposition were close to 100% after 6 cycles without pH adjustment, which increased 6 orders of magnitude compared with no catalyst. At the same time, the O₂ generation reached 99.67% in 2 h with little metal leaching, and ·OH has been greatly inhibited to only 0.08%. This work can cleanly remove H₂O₂ with little deep oxidation and protect the process of H₂O₂ utilization to achieve a safer world.     

MHD mixed convection of a Cu–water nanofluid flow through a channel with an open trapezoidal cavity and an elliptical obstacle

In the current work, numerical simulations are achieved to study the properties and the characteristics of fluid flow and heat transfer of (Cu–water) nanofluid under the magnetohydrodynamic effects in a horizontal rectangular canal with an open trapezoidal enclosure and an elliptical obstacle. The cavity lower wall is grooved and represents the heat source while the obstacle represents a stationary cold wall. On the other hand, the rest of the walls are considered adiabatic. The governing equations for this investigation are formulated, nondimensionalized, and then solved by Galerkin finite element approach. The numerical findings were examined across a wide range of Richardson number (0.1 ≤ Ri ≤ 10), Reynolds number (1 ≤ Re ≤ 125), Hartmann number (0 ≤ Ha ≤ 100), and volume fraction of nanofluid (0 ≤ φ ≤ 0.05). The current study's findings demonstrate that the flow strength increases inversely as the Reynolds number rises, which pushes the isotherms down to the lower part of the trapezoidal cavity. The Nu_avg rises as the Ri rise, the maximum Nu_avg = 10.345 at Ri = 10, Re = 50, ϕ = 0.05, and Ha = 0; however, it reduces with increasing Hartmann number. Also, it increase by increasing ϕ, at Ri = 10, the Nu_avg increased by 8.44% when the volume fraction of nanofluid increased from (ϕ = 0–0.05).     

矿用履带式运输车在复杂开采条件下的应用

为了方便煤炭的运输，某矿使用了矿用履带式搬运车。履带运输车解决了轮式运输车在恶劣道路条件下不能行走的问题，这种履带车是一种新型的运输工具，其特点是体积小、转弯灵活、动力强大，可在不受场地限制的情况下轻松通过狭窄的地方，能有效解决山区、土壤、陡坡等恶劣条件下物料的运输问题，适合普通运输车辆无法通行的地方。介绍了矿用履带式搬运车的主要结构以及工作原理，并给出了实际应用中应注意的事项。     

A comparison of shear stress estimation methods for a single geobag on a rough bed

There are several methods for estimating bed shear stress in the literature, but comprehensive comparisons among them are limited and under specific conditions. This study compared these methods first on a bare smooth bed, and then for a single geobag on a rough bed in the interest of determining the stability of geobags used in riverbank protection structures. The geobag was filled with cement or sand and tested under different open channel flow conditions. The turbulent kinetic energy method appeared to best represent the local bed shear stress on the geobag when using the newly calibrated proportionality constants. The Reynolds stress method via extrapolation was relatively unaffected by changes to the geobags shape and measurement locations, suggesting this method inadequately represents the local bed shear stress. The Patel method and the universal law of the wall method failed to represent local bed shear stress in the rough bed cases due to instrument limitations and the breakdown of the law of the wall. This study highlights the impact of different methods on the bed shear stress estimation.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

认知双向中继网络在κ-μ衰落信道下的中断性能分析

针对认知双向中继网络在进行数据传输时面临的复杂无线信道场景问题,采用广义κ-μ分布构建认知双向中继网络中的视距和非视距无线传输信道,推导次网络在κ-μ衰落信道下的统一中断概率,并分析次网络在多种单一和混合衰落信道情况下的中断性能。仿真结果表明,无线信道的衰落程度对次网络的中断概率影响显著,依据衰落信道类型合理设置网络参数将有助于提升次网络中断性能。     

Braking behaviours of C/C–SiC mated with iron/copper-based PM in dry,wet and salt fog conditions

C/C–SiC composites have enormous potential as a new generation of brake materials. It is worth studying the friction and wear behaviours of these materials in special environments to ensure the safe and effective braking of trains in practical applications. In this study, the braking behaviours and wear mechanisms of C/C–SiC mating with iron/copper-based PM in dry, wet and salt fog conditions are compared in detail. The results show that the coefficient of friction (COF) in the wet condition is reduced by 14.13% compared with that under the dry condition. The COF value of the first braking under salt fog condition is increased by 12.27% and 30.75% compared to the dry and wet conditions, respectively. Additionally, the tail warping phenomenon of the braking curve disappears in wet condition, which is attributed to the weak adhesion of friction interfaces and the lubrication of the water film. The main wear mechanisms of C/C–SiC mating with iron/copper-based PM under dry condition are adhesive, fatigue and oxidation wear. However, the dominant wear in wet condition is abrasive wear. The cooling and lubrication of water reduce the tendency of thermal stress, and weaken adhesive and fatigue wear. Furthermore, salt fog can accelerate the corrosion of alloy friction film, leading to the damage of friction film. Meanwhile, the third body particles formed in salt fog condition participate in the braking process. The wear mechanisms in salt fog condition are dominated by abrasive and delamination wear.     

Modelling of mechanical properties of five maize varieties at critical processing conditions in the production of fermented slurry-ogi

This research work was conducted to investigate the impact of critical processing conditions on the selected mechanical properties of maize in the production of fermented ogi slurry. Five varieties of maize (A4W, C3Y, D8W, B2Y, and E9W) were soaked at 28 ^ºC and average hot soaking at 65^ºC, respectively, for 96 h at 12-h interval. Selected mechanical properties were evaluated based on a 5 × 2 × 9 factorial design (varieties× soaking methods× soaking periods). Force (FB) and energy required to break (EB) maize grains decreased significantly (p < 0.05) up to the 12th hour. The EB reduced from 873.3 to 70.0 N mm and from 873.3 to 77.8 N mm for variety E9W at soaking conditions of 28^ºC and 65^ºC, respectively. Similar trends were observed for other maize varieties. Modulus of elasticity and resilience decreased significantly (p < 0.05) with increase in soaking period and moisture content. The EB to break maize grains was directly proportional to the product of Young’s modulus and area (E_m A^1.5), the FB and area (F_m A°^.5) and force required to break and geometric mean diameter (F D_g²) of maize grains with a high R² (0.9610.999). This study suggested that the duration of soaking between 12 and 24 h should be enough to significantly (p > 0.05) reduce the hardness, force, and energy required to break whole maize grains in the production of this fermented product and relevant for predicting minimum required energy for a large-scale operation.     

碱铝硅酸盐玻璃化学强化关键影响因素概述

化学强化是一种玻璃机械强度增强方法,适用于异型、超薄、高碱、高膨胀玻璃增强,因新型超薄显示产品的屏幕保护玻璃发展需要,化学强化技术重新在碱铝硅酸盐玻璃品种掀起研究热潮。本文对化学强化本质及铝硅酸盐玻璃在屏幕保护玻璃应用进行了回顾,基于玻璃化学强化的高CS、DOL和低CT诉求,归纳总结了关键影响因素,第1,碱铝硅酸盐玻璃的成分及结构是基础,氧化铝有利玻璃网络孔隙增大创造交换通道,氧化钠或氧化锂是离子交换关键物质;第2,对于玻璃组成和结构设计,要求玻璃网络键合度R=O/Si或O/(Si+Al)满足2.15~2.40,碱金属氧化物质量分数大于13%且膨胀系数大于6×10^-6/℃;第3,在化学强化工艺方面,化学强化温度决定离子扩散系数,化学强化时间决定DOL,一步法仅能获得相对较大的CS,而DOL不很理想,只有两种离子参与交换的二步法才有利于CS和DOL同步提高。     

车用有机朗肯循环系统中计量泵的性能研究

在模拟车用有机朗肯循环(organic Rankine cycle,ORC)系统的工作环境下,设计并搭建以R123作为工质的液压隔膜计量泵性能测试平台。在额定转速(2900 r/min)下,通过调节液压隔膜计量泵的冲程(25%、50%、75%、100%)和出口压力,得到变工况下液压隔膜计量泵的特性曲线,并分析液压隔膜计量泵的关键参数对车用ORC系统性能的影响情况。结果表明:液压隔膜计量泵的流量受冲程影响,与出口压力基本无关。其输入功率和实际运行效率均随着冲程的增加而升高,最高分别可达523.91 W、88.27%。应用该液压隔膜计量泵的模拟车用ORC系统吸热量变化范围较广,系统热效率最高可达12.81%。液压隔膜计量泵可应用于车用ORC系统,通过调节液压隔膜计量泵的冲程和出口压力,可实现提高车用ORC系统热效率、增加系统净输出功率的目的。