Swapping catalytic active sites from cationic Ni to anionic F in Fe–F–Ni3S2 enables more efficient alkaline oxygen evolution reaction and urea oxidation reaction

Electrolysis of water for producing hydrogen instead of traditional fossil fuels is one of the most promising methods to alleviate environmental pollution and energy crisis. In this work, Fe and F ion co-doped Ni₃S₂ nanoarrays grown on Ni foam substrate were prepared by typical hydrothermal and sulfuration processes for the first time. Density functional theory (DFT) calculation demonstrate that the adsorption energy of the material to water is greatly enhanced due to the doping of F and Fe, which is conducive to the formation of intermediate species and the improvement of electrochemical performance of the electrode. The adsorption energy of anions (F and S) and cations (Fe and Ni) to water in each material was also calculated, and the results showed that F ion showed the most optimal adsorption energy of water, which proved that the doping of F and Fe was beneficial to improve the electrochemical performance of the electrode. It is worth noting that the surface of Fe–F–Ni₃S₂ material will undergo reconstruction during the process of water oxidation reaction and urea oxidation reaction, and amorphous oxides or hydroxides in situ would be formed on the surface of electrode, which are the real active species.     

Generalized differential quadrature analysis of MHD mixed convective motion of Casson fluids past an isothermal irregular slender geometry via Cattaneo–Christov's approach

Classical Fourier's theory is well-known in continuum physics and thermal sciences. However, the primary drawback of this law is that it contradicts the principle of causality. To explore the thermal relaxation time characteristic, Cattaneo–Christov's theory is adopted thermally. In this regard, the features of magnetohydrodynamic (MHD) mixed convective flows of Casson fluids over an impermeable irregular sheet are revealed numerically. In addition, the resulting system of partial differential equations is altered via practical transformations into nonlinear ordinary differential equations. An advanced numerical algorithm is developed in this respect to get higher approximations for temperature and velocity fields, as well as their corresponding wall gradients. For validating our numerical code, the current outcomes are compared with the available literature results. Moreover, it is revealed that the velocity field is more prominent in the suction flow situation as compared with the injection flow case. It is also found that the Casson fluid is hastened in the case of lower yield stress. Larger values of thermal relaxation parameters create a lessening trend in the temperature distribution and its related boundary layer breadth.     

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。     

基于改进灰狼优化算法的服务功能链映射算法

随着工业互联网、车联网、元宇宙等新型互联网应用的兴起，网络的低时延、可靠性、安全性、确定性等方面的需求正面临严峻挑战。采用网络功能虚拟化技术在虚拟网络部署过程中，存在服务功能链映射效率低与部署资源开销大等问题，联合考虑节点激活成本、实例化开销，以最小化平均部署网络成本为优化目标建立了整数线性规划模型，提出基于改进灰狼优化算法的服务功能链映射（improved grey wolf optimization based service function chain mapping，IMGWO-SFCM）算法。该算法在标准灰狼优化算法基础上添加了基于无环K最短路径（K shortest path，KSP）问题算法的映射方案搜索、映射方案编码以及基于反向学习与非线性收敛改进三大策略，较好地平衡了其全局搜索及局部搜索能力，实现服务功能链映射方案的快速确定。仿真结果显示，该算法在保证更高的服务功能链请求接受率下，相较于对比算法降低了11.86%的平均部署网络成本。     

An optimal configuration for hybrid SOFC,gas turbine,and Proton Exchange Membrane Electrolyzer using a developed Aquila Optimizer

In this research, a technical, economic and environmental analysis has been proposed to a Hybrid Solid Oxide Fuel Cell (SOFC) system-based hybrid system including biomass, gas turbine, and Proton Exchange Membrane Electrolyzer. A multi-objective optimization technique has been utilized to improve the overall product cost and the exergy effectiveness based on a developed version of Aquila Optimizer (DAO). The main idea of using the developed version is to improve the accuracy and the precision of the original Aquila optimizer. The system is then authenticated in terms of energy/exergy effectiveness, and energy-economic efficiency. The achievements indicate that employing the optimization algorithm for different configurations provided satisfying results for the system.     

Automated prediction system of household energy consumption in cities using web crawler and optimized artificial intelligence

The supply of electrical energy is critical to convenient and comfortable living. However, people consume a large amount of energy, contributing to an energy crisis and global warming, and damaging some ecological cycles. Residential electricity consumption has greater elasticity than industrial and business consumption; it therefore has high energy-saving potential. This work establishes an automated platform, which provides information about residential electricity consumption in each city in Taiwan. Machine learning was used to forecast future residential electricity demand. A nature-inspired optimization method was applied to enhance the accuracy of the best machine learner, yielding an even better hybrid ensemble model. Performance measures indicate that the resulting model is accurate and provides effective information for reference. An automatic web-based system based on the model was combined with a web crawler and scheduled to run automatically to provide information on monthly residential electricity consumption in each county and city. By providing energy consumption information across the country, power providers and government can discuss policy and set different goals for energy use. The results of this study can facilitate the early implementation of energy-saving and carbon emission-reducing in cities and aid utility companies in establishing energy conservation guidelines.     

Role of functionalized graphene quantum dots in hydrogen evolution reaction: A density functional theory study

Rapid advances in the field of catalysis require a microscopic understanding of the catalytic mechanisms. However, in recent times, experimental insights in this field have fallen short of expectations. Furthermore, experimental searches of novel catalytic materials are expensive and time-consuming, with no guarantees of success. As a result, density functional theory (DFT) can be quite advantageous in advancing this field because of the microscopic insights it provides and thus can guide experimental searches of novel catalysts. Several recent works have demonstrated that low-dimensional materials can be very efficient catalysts. Graphene quantum dots (GQDs) have gained much attention in past years due to their unique properties like low toxicity, chemical inertness, biocompatibility, crystallinity, etc. These properties of GQDs which are due to quantum confinement and edge effects facilitate their applications in various fields like sensing, photoelectronics, catalysis, and many more. Furthermore, the properties of GQDs can be enhanced by doping and functionalization. In order to understand the effects of functionalization by oxygen and boron based groups on the catalytic properties relevant to the hydrogen-evolution reaction (HER), we perform a systematic study of GQDs functionalized with the oxygen (O), borinic acid (BC₂O), and boronic acid (BCO₂). All calculations that included geometry optimization, electronic and adsorption mechanism, were carried out using the Gaussian16 package, employing the hybrid functional B3LYP, and the basis set 6-31G(d,p). With the variation in functionalization groups in GQDs, we observe significant changes in their electronic properties. The adsorption energy E_ads of hydrogen over O-GQD, BC₂O-GQD, and BCO₂-GQD is ?0.059 eV, ?0.031 eV and ?0.032 eV respectively. Accordingly, Gibbs free energy (ΔG) of hydrogen adsorption is extraordinarily near the ideal value (0 eV) for all the three types of functionalized GQDs. Thus, the present work suggests pathways for experimental realization of low-cost and multifunctional GQDs based catalysts for clean and renewable hydrogen energy production.     

Crystallization behavior and density functional theory study of solution combustion synthesized silicon doped calcium phosphates

The influence of heat-treatment temperatures (700 °C, 900°C, 1200 °C) on the phase, physical properties, crystallization rate, and in vitro properties of the solution combustion synthesized silicon-doped calcium phosphates (CaPs) were investigated. The thermodynamic aspects (enthalpy, entropy, and free energy) of the synthesis process and the crystallographic properties of the final samples were first predicted and then confirmed using density functional theory (DFT). Results demonstrated that the crystallization rate was controlled by the fuel(s) type (glycine, citric acid, and urea) and the amounts of Si⁴⁺ ions (0, 0.1, 0.4 mol). The highest calculated crystallization rate values of the un-doped, 0.1, and 0.4 mol Si-doped samples were 64%, 22%, 38%, respectively. The obtained results from the DFT simulation revealed that crystal growth in the direction of c-axis of hydroxyapatite (HAp) structure could change the stability of (001) surface of (HAp). Also, the computational data confirmed the adsorption of Si–OH groups on the (001) surface of HAp during the SCS process with an adsorption energy of 1.53 eV. AFM results in line with DFT simulation showed that the observed change in the surface roughness of Si-doped CaPs from 2 to 8 nm could be related to the doping of Si⁴⁺ ions onto the surface of CaPs. Besides, the theoretical and experimental investigation showed that crystal growth and doping of Si⁴⁺ ions could decrease the activation energy of oxygen reduction reaction (ORR). Furthermore, the results showed that the crystallized HAp structure could have great potential to efficiently reduce oxidative stress in human body.     

基于重要度排序的配电自动化终端优化布局方法

针对配电自动化终端优化布局问题，提出了一种基于重要度排序的终端优化布局方法。首先以等年值综合费用为目标函数，供电可靠性和投入产出比为双重约束建立了配电自动化终端优化布局模型。然后通过分析配电自动化终端对供电可靠性提升的影响，给出各个节点的“二遥”及“三遥”终端安装重要度定义及计算公式。最后采用枚举法确定最优终端安装数量，基于节点终端安装重要度排序确定终端的最优安装位置。该方法考虑了已布局节点对剩余节点终端安装重要度的影响，能够在降低计算量的同时兼顾布局合理性。运用所提方法RBTS-BUS2系统及扩充模型进行终端优化布局并与智能优化算法的布局结果进行对比，验证了本文方法的有效性及优越性。