Surface homogenizing heterostructure coatings induced by Ti3C2Tx MXene for enhanced cycle performance of lithium-rich cathode materials

The layered lithium-rich manganese-based cathode material (Li_1.2Mn_0.54Co_0.13Ni_0.13) has the significant advantage of high specific capacity, but this material also suffers serious defects, including severe capacity attenuation and voltage attenuation during the cycle. At present, most researchers have been working to optimize the cycle performance of lithium-rich materials. In this work, we propose a surface homogenizing heterostructure coating induced by MXene modification to reduce capacity reduction and voltage decay. It can be found that the initial Coulombic efficiency (ICE) increases from 77.2% for the bare electrode Li_1.2Mn_0.54Co_0.13Ni_0.13 (LMO) to 85.5% for 1.4 wt% MXene (Ti₃C₂T_x, T_x represents the surface terminations: OH, O, F) modified lithium-rich (TO2). Furthermore, the discharge specific capacity of the electrode at 5 C rate increased from 160.7 mAh g⁻¹ for LMO to 200.6 mAh g⁻¹ for TO2. More prominently, the outstanding cycle stability with capacity retention rate is 82.1% for TO2 after 200 cycles, while only 64.7% for LMO, and the average discharge voltage dropped from 0.788 to 0.468 V. In addition, the mechanism for improving the electrochemical performance is systematically studied.     

Rapid degradation characteristics of an air-cooled PEMFC stack

Durability is one of the obstacles to the large-scale commercialization of proton exchange membrane fuel cell (PEMFC) stacks. Understanding its decay behavior is a prerequisite for improving durability. In this study, rapid degradation characteristics of an air-cooled PEMFC stack are investigated. Due to the simultaneous presence of various degradation sources, the maximum power of the PEMFC stack has been reduced by 39.6% after just 74.6 h of operations. Performance degradation characteristics are sought by analyzing the cell voltage, temperature distribution, ion chromatography, and surface morphology of the gas diffusion layer. The result shows that abnormal cell voltage and temperature distribution can reflect the problematic location. The fluoride ion emission rate is 0.111 mg/day, which proves that the membrane has been seriously degraded. Contact angle reduction and impurities attached to the surface of the gas diffusion layer lead to the water management failure. It is also found that the main factor for performance degradation could be different under different current conditions. And more information can be found under higher current conditions during monitoring the decay of PEMFCs. This study helps to deepen the understanding of performance degradation characteristics.     

A Voltage Control Method for a Distributed Line Feeder by means of Multiagent Approach

This paper proposes a method for the coordinated control of power factor by means of a multiagent approach. The proposed multiagent system consists of two types of agent: single feeder agent (F_AG) and bus agent (B_AG). In the proposed system, an F_AG plays as an important role, which decides the power factors of all distributed generators by executing the load flow calculations repeatedly. The voltage control strategies are implemented as the class definition of Java into the system. In order to verify the performance of the proposed method, it has been applied to a typical distribution model system. The simulation results show that the system is able to control very violent fluctuation of the demands and the photovoltaic (PV) generations.     

Novel dynamic semiempirical proton exchange membrane fuel cell model incorporating component voltages

This paper introduces a novel dynamic semiempirical model for the proton exchange membrane fuel cell (PEMFC). The proposed model not only considers the stack output voltage but also provides valid waveforms of component voltages, such as the no‐load, activation, ohmic, and concentration voltages of the PEMFC stack system. Experiments under no‐load, ramping load, and dynamic load conditions are performed to obtain various voltage components. According to experimental results, model parameters are optimised using the lightning search algorithm by providing valid theoretical ranges of parameters to the lightning search algorithm code. In addition, the correlation between the vapour and water pressures of the PEMFC is obtained to model the component voltages. Finally, all component voltages and the stack output voltage are validated by using the experimental/theoretical waveforms mentioned in previous research. The proposed model is also compared with a recently developed semiempirical model of PEMFC through particle swarm optimisation. The proposed dynamic model may be used in future in‐depth studies on PEMFC behaviour and in dynamic applications for health monitoring and fault diagnosis.     

Estimation of power density in IMPATT using different materials

ABSTRACT

The RF output power dissipated per unit area is calculated using Runge-Kutta method for the high-moderate-moderate-high (n⁺-n-p-p⁺) doping profile of double drift region (DDR)-based impact avalanche transit time (IMPATT) diode by taking different substrate at Ka band. Those substrates are silicon, gallium arsenide, germanium, wurtzite gallium nitride, indium phosphide and 4H-silicon carbide. A comparative study regarding power dissipation ability by the IMPATT using different material is being presented thereby modelling the DDR IMPATT diode in a one-dimensional structure. The IMPATT based on 4H-SiC element has highest power density in the order of 10¹⁰ Wm^?2 and the Si-based counterpart has lowest power density of order 10⁶ Wm^?2 throughout the Ka band. So, 4H-SiC-based IMPATT should be preferable over others for the power density preference based application. This result will be helpful to estimate the power density of the IMPATT for any doping profile and to select the proper element for the optimum design of the IMPATT as far as power density is concerned in the Ka band. Also, we have focused on variation of power density with different junction temperatures and modelled the heat sink with analysis of thermal resistances.     

AlF3-modified LiCoPO4 for an advanced cathode towards high energy lithium-ion battery

Surface-interface reaction between the electrode and electrolyte plays a key role in lithium-ion storage properties, especially for high voltage cathode such as LiCoPO₄ and Ni-riched cathode. Generally, surface modification is an effective method to improve the electrochemical performance of electrode materials. Herein, in order to revise the LiCoPO₄ cathode with desirable properties, uniform AlF₃-modified LiCoPO₄ (LiCoPO₄@AlF₃) cathode materials in nano-sized distribution are synthesized. XRD result indicates that there is no structural transformation observed after AlF₃ coating. TEM characterization and XPS analysis reveal that the surface of LiCoPO₄ particle is coated by a nano-sized uniform AlF₃ layer. Further, the electrochemical results indicate that AlF₃ layer significantly improves the cycling and rate performances of LiCoPO₄ cathode within the voltage range of 3.0–5.0 V. After a series of optimization, 4 mol% AlF₃-coated LiCoPO₄ material exhibits the best properties including an initial discharge capacity of 159 mA h g^?1 at 0.1 C with 91% capacity retention after 50 cycles, especially a discharge capacity of 90 mA h g^?1 can be obtained at 1 C rate. CV curves indicate that the polarization of cathode is reduced by AlF₃ layer and EIS curves reveal that AlF₃ layer relieves the increase of resistance to facilitate Li-ion transfer at the interface between electrode and electrolyte during the cycling process. The enhanced electrochemical performances are attributed to that the AlF₃ layer can stabilize the interface between the cathode and electrolyte, form steady SEI film and suppress the electrolyte continuous decomposition at 5 V high voltages. This feasible strategy and novel characteristics of LiCoPO₄@AlF₃ could promise the prospective applications in the stat-art of special lithium-ion battery with high energy and/or power density.     

基于约瑟夫森量子电压的交流功率测量系统及方法研究

基于约瑟夫森量子电压标准设计了交流功率差分测量系统。通过分析差分采样系统的误差分布及误差传递函数,提出换向差分测量方法,减小了差分采样系统的增益误差,提高了电压幅值测量准确度;通过分析衰减系数η,证明了采用换向差分测量较容易实现10-7量级电压幅值测量。通过评估差分采样系统零相位,结果证明了差分采样系统具有较好的相位测量稳定性。分析了交流功率差分测量系统的不确定度分量,评估了功率因数为1.0,0.5 L和0.5 C时的功率测量不确定度,通过与国家交流功率基准装置进行实验比对,证明了基于约瑟夫森量子电压交流功率测量系统不确定度评估的合理性。     

The vibration and pull-in mechanism of two coupled elastically restrained beams assembly subjected to electrostatic force

ABSTRACT

The novel model of the coupled beam-type electrodes with elastic roots, subjected to the AC and DC voltages is constructed. The exact critical pull-in voltage of the two coupled elastically restrained beams with size effect is presented. The analytical method of the general system subjected to the AC and DC voltages simultaneously is presented. The characteristic mechanism of coupled vibration is investigated.     

MOOC Learner’s Final Grade Prediction Based on an Improved Random Forests Method

Massive Open Online Course (MOOC) has become a popular way of online learning used across the world by millions of people. Meanwhile, a vast amount of information has been collected from the MOOC learners and institutions. Based on the educational data, a lot of researches have been investigated for the prediction of the MOOC learner’s final grade. However, there are still two problems in this research field. The first problem is how to select the most proper features to improve the prediction accuracy, and the second problem is how to use or modify the data mining algorithms for a better analysis of the MOOC data. In order to solve these two problems, an improved random forests method is proposed in this paper. First, a hybrid indicator is defined to measure the importance of the features, and a rule is further established for the feature selection; then, a Clustering-Synthetic Minority Over-sampling Technique (SMOTE) is embedded into the traditional random forests algorithm to solve the class imbalance problem. In experiment part, we verify the performance of the proposed method by using the Canvas Network Person-Course (CNPC) dataset. Furthermore, four well-known prediction methods have been applied for comparison, where the superiority of our method has been proved.