Optimized structure and electrochemical properties of sulfonated carbon nanotubes/Co–Ni bimetallic layered hydroxide composites for high-performance supercapacitors

Technical development in electronic devices is frequently stifled by their insufficient capacity and cyclic stability of energy-storage devices. The nano-structured materials have sensational importance for providing novel and optimized combination to overcome exiting boundaries and provide efficient energy storage systems. Metal hydroxide materials with high capacity for pseudo-capacitance properties have grabbed special attention. Lately, the blend of nickel and cobalt hydroxides has been considered as a favorable class of metallic hydroxide materials owing to their comparatively high capacitance and exceptional redox reversibility. The sulfonated carbon nanotube fluid (SCNTF) was prepared by the ion exchange method to be utilized as the exceptional templates due to astonishing specific surface area, ensuring the maximum utilization of the active material. The CoNi-layered double hydroxides (LDHs)/SCNTF core-shell nanocomposite was prepared by the simple solvothermal method. Structural analysis showed that the composite material had the high conductance of carbon materials, the pseudo-capacitance characteristics of metal hydroxides, and porous structure, which facilitates the ion shuttle when the electrolyte reacts with the active material. Electrochemical analysis results showed that CoNi-LDHs/SCNTF had excellent rate performance, reversible charge-discharge properties and cycle stability. It exhibited an extreme specific capacity of 1190.5 F g^?1 at a current density of 1 A g^?1; whereas specific capacity remained 953.7 F g^?1 at the current density was 10 A g^?1. In addition, the capacity retention rate after 5000 charge-discharge cycles at a current density of 20 A g^?1 was 81.0%. The results indicated that the CoNi-LDHs/SCNTF core-shell nanocomposite material is cost efficient and an effective substitute in energy storage applications.     

Boosting discharge capability of α-Ni(OH)2 by cobalt doping based on robust spherical structure

α-Ni(OH)₂ is a promising candidate of the currently commercialized β-Ni(OH)₂ due to its higher theoretical discharge capacity in alkaline solution; however, its instability and poor conductivity plague the practical application. Herein, we propose α-Ni(OH)₂ with Co doping and spherical structure to strengthen the stability and enhance the conductivity and use it as the cathode for nickel-metal hydride batteries. Studies show that proper Co doping promotes the electrochemical reaction between the active materials and the electrolyte due to the spherical α-Ni(OH)₂ with enlarged interlayer distance and abundant hole channels, as well as high conductivity of Co, therefore, the obtained spherical α-Ni(OH)₂ with 7 mol% Co doping delivers significantly improved discharge capability, which is 384.6 mAh g^?1 at 70 mA g^?1 (0.2 C), increased by 54.3 mAh g^?1 compared with pure α-Ni(OH)₂, and at a high current of 5 C, it still gives 269.4 mAh g^?1, in contrast 218.5 mA g^?1 for the pure α-Ni(OH)₂. Besides, the cycling stability of the α-Ni(OH)₂ with 7 mol% Co doping maintains 340 cycles at a capacity retention of 80% (1C), which is extended 110 cycles in contrast to the pure α-Ni(OH)₂. These results provide the underpinning platform of α-Ni(OH)₂ for battery applications with high discharge ability and cycle life.     

基于双时间点检测的毫米波雷达人流量监测方法

针对现有基于视频监控的人流量统计方案成本高、算法复杂且不利于个人隐私保护的局限性，利用毫米波雷达体积小、成本低、分辨率高的特点，提出了一种基于双时间点检测的人流量监测方法。该方法先获取人体目标散射点位置和多普勒频移信息来构成点云数据，然后根据多普勒频移正负来判断人体的运动方向，并筛选具有高多普勒频移值的点云数据以降低干扰点对聚类结果的影响；在双时间点对特定区域内人员数量进行统计，并根据双时间点之间所获取的点云数据聚类结果对所统计人员数据进行修正。实验结果表明，该方法能够用匿名的方式以较高的正确率统计人员进出。     

NiFe layered double hydroxide as an efficient bifunctional catalyst for electrosynthesis of hydrogen peroxide and oxygen

Two electron oxygen reduction reaction to produce hydrogen peroxide (H₂O₂) is a promising alternative technique to the multistep and high energy consumption anthraquinone process. Herein, Ni–Fe layered double hydroxide (NiFe-LDH) has been firstly demonstrated as an efficient bifunctional catalyst to prepare H₂O₂ by electrochemical oxygen reduction (2e^? ORR) and oxygen evolution reaction (OER). Significantly, the NiFe-LDH catalyst possesses a high faraday efficiency of 88.75% for H₂O₂ preparation in alkaline media. Moreover, the NiFe-LDH catalyst exhibits excellent OER electrocatalytic property with small overpotential of 210 mV at 10 mA cm^?2 and high stability in 1 M KOH solution. On this basis, a new reactor has been designed to electrolyze oxygen and generate hydrogen peroxide. Under the ultra-low cell voltage of 1 V, the H₂O₂ yield reaches to 47.62 mmol g_cat^?1 h^?1. In order to evaluate the application potential of the bifunctional NiFe-LDH catalyst for H₂O₂ preparation, a 1.5 V dry battery has been used as the power supply, and the output of H₂O₂ reaches to 83.90 mmol g_cat^?1 h^?1. The excellent electrocatalytic properties of 2e^? ORR and OER make NiFe-LDH a promising bifunctional electrocatalyst for future commercialization. Moreover, the well-designed 2e^? ORR-OER reactor provides a new strategy for portable production of H₂O₂.     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

Double emulsion (W/O/W) gel stabilised by polyglycerol polyricinoleate and calcium caseinate as mangiferin carrier: insights on formulation and stability properties

Mangiferin (MGF) is a phenolic compound isolated from mango, but its poor solubility significantly limits its use. In this study, MGF was embedded into the inner aqueous phase of W₁/O/W₂ emulsions. Firstly, the dissolution method of MGF was determined. MGF remained stable in solution with pH 13 at 30 min, and its solubility reached 10 mg mL⁻¹. When the pH of MGF solutions was adjusted from pH 13 to pH 6, MGF did not immediately crystallise, providing sufficient time to construct the MGF-loaded W₁/O/W₂ emulsions. Subsequently, the MGF-loaded W₁/O/W₂ emulsions were constructed using polyglycerol polyricinoleate (PGPR) and calcium caseinate (CAS). The formation and stability of the W₁/O/W₂ emulsions were investigated. The MGF-loaded W₁/O/W₂ emulsions stabilised with 1% PGPR and 1% – 3% CAS exhibited a low viscosity, limited loading capacity, and poor stability. Conversely, the MGF-loaded W₁/O/W₂ emulsions stabilised by 3%PGPR–3%CAS exhibited optimal loading capacity (encapsulation efficiency = 95.31% and loading efficiency = 0.91%) and stability, which was attributed to the fact that high viscosity and gel state retarded the migration of inner aqueous phase. These results indicated that the W₁/O/W₂ emulsions stabilised by PGPR and CAS may be a potential alternative for encapsulating mangiferin.     

Mutual information maximizing GAN inversion for real face with identity preservation

Recent generative adversarial networks (GANs) have yielded remarkable performance in face image synthesis. GAN inversion embeds an image into the latent space of a pretrained generator, enabling it to be used for real face manipulation. However, current inversion approaches for real faces suffer the dilemma of initialization collapse and identity loss. In this paper, we propose a hierarchical GAN inversion for real faces with identity preservation based on mutual information maximization. We first use a facial domain guaranteed initialization to avoid the initialization collapse. Furthermore, we prove that maximizing the mutual information between inverted faces and their identities is equivalent to minimizing the distance between identity features from inverted and original faces. Optimization for real face inversion with identity preservation is implemented on this mutual information-maximizing constraint. Extensive experimental results show that our approach outperforms state-of-the-art solutions for inverting and editing real faces, particularly in terms of face identity preservation.     

汽车密封件双料一体旋转模具设计

针对PP+TPE汽车密封件设计了大型双料一体旋转注射模,介绍了二次合模的精确定位、可控注射系统、顶出平衡设计。该模具技术能够使两种性能完全不同的材料按照指定区域分布,并且达到塑件尺寸的精度需求,实现同时与钣金、玻璃、塑件等不同材质的零件进行复合配套。本项目实施后,在模具精度、寿命等方面接近或部分达到国外同类产品先进水平,而价格仅为国外产品的40%,性价比优势明显。     

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings

Due to the severe outdoor PM2.5 pollution in China, many people have installed air-cleaning systems in homes. To make the systems run automatically and intelligently, we developed a recurrent neural network (RNN) that uses historical data to predict the future indoor PM2.5 concentration. The RNN architecture includes an autoencoder and a recurrent part. We used data measured in an apartment over the course of an entire year to train and test the RNN. The data include indoor/outdoor PM2.5 concentration, environmental parameters and time of day. By comparing three different input strategies, we found that a strategy employing historical PM2.5 and time of day as inputs performed best. With this strategy, the model can be applied to predict the relatively stable trend of indoor PM2.5 concentration in advance. When the input length is 2 h and the prediction horizon is 30 min, the median prediction error is 8.3 µg/m³ for the whole test set. For times with indoor PM2.5 concentrations between (20,50] µg/m³ and (50,100] µg/m³, the median prediction error is 8.3 and 9.2 µg/m³, respectively. The low prediction error between the ground-truth and predicted values shows that the RNN can predict indoor PM2.5 concentrations with satisfactory performance.     

基于Abaqus的双裂纹不同间距对裂纹扩展速率的研究

扩展有限元法是近年经过大量运用的,在传统有限元的范围中求解不连续问题一种有效计算方法,它是基于单位分解的思想,在计算不连续问题时加入跳跃函数。以ABAQUS为平台,基于扩展有限元方法 (XFEM),以含双穿透型裂纹的有限宽板受横向拉伸载荷为力学模型,建立相应的裂纹尖端应力的有限元模型,研究焊接接头区域不同间距双裂纹相互作用对裂纹扩展速率的影响。结果表明:双裂纹间距的的大小并没有对裂纹的扩展速率产生影响。