Achieving excellent energy storage density of Pb0.97La0.02(ZrxSn0.05Ti0.95-x)O3 ceramics by the B-site modification

The applications of antiferroelectric (AFE) materials in miniaturized and integrated electronic devices are limited by their low energy density. To address the above issue, the antiferroelectricity of the reinforced material was designed to improve its AFE-ferroelectric (FE) phase transition under electric fields. In this present study, the composition of Zr⁴⁺ (0.72 Å) and Ti⁴⁺ (0.605 Å) at B-site of Pb_0.97La_0.02(Zr_xSn_0.05Ti_0.95-x)O₃ ceramics with orthogonal reflections are synthesized via the tape-casting method. These ceramics are modified to enhance their antiferroelectricity by reducing their tolerance factor. A recoverable energy storage density W_rec 12.1 J/cm³ was obtained for x = 0.93 under 376 kV/cm, which is superior value than reported until now in lead-based energy storage systems. Moreover, the discharge energy density can reach 10.23 J/cm³, and 90 % of which can be released within 5.66 μs. This work provides a new window and potential materials for further industrialization of pulse power capacitors.     

高熵形状记忆合金的研究进展

高熵形状记忆合金是在等原子比NiTi合金的基础上,结合高熵合金的概念,逐渐发展起来的一种新型高温形状记忆合金。近年来,已开发出了综合性能优异的(TiZrHf)50(NiCoCu)50系和(TiZrHf)50(NiCuPd)50系高熵形状记忆合金,引起了广泛的关注和研究兴趣。本文从物相组成、微观组织、马氏体相变行为、形状记忆效应和超弹性等角度出发,综述了高熵形状记忆合金的研究进展,并对高熵形状记忆合金未来的研究重点进行了展望。     

Synthesis,characterization and charge transport properties of Pr–Ni Co-doped SrFe2O4 spinel for high frequency devices applications

Ferrites are materials of interest due to their broad applications in high technological devices and a lot of research has been focused to synthesize new ferrites. In this regard, an effort has been devoted to synthesize spinel Pr–Ni co-substituted strontium ferrites with a nominal formula of Sr_1-xPr_xFe_2-yNi_yO₄ (0.0 ≤ x ≤ 0.1, 0.0 ≤ y ≤ 1.0). The cubic structure of pure and Pr–Ni co-substituted strontium ferrite samples calcinated at 1073 K for 3 h has been confirmed through X-ray diffraction (XRD). Average sizes of crystallites (18–25 nm) have been estimated from XRD analysis and nanometer particle sizes of synthesized ferrites have been further verified by scanning electron microscopy (SEM). SEM results have also shown that particles are mostly agglomerated and all the samples possess porosity. It has been observed that at 298 K, the values of resistivity (ρ) increase, while that of AC conductivity, dielectric loss, and dielectric constants decrease with increasing amounts of Pr³⁺ and Ni²⁺ ions. The values of dielectric parameters initially decrease with frequency and later become constant and can be explained on the basis of dielectric polarization. Electrochemical impedance spectroscopy (EIS) studies show that the charge transport phenomenon in ferrite materials is mainly controlled via grain boundaries. Overall, synthesized ferrite materials own enhanced resistivity values in the range of 1.38 × 10⁹–1.94 × 10⁹ Ω cm and minimum dielectric losses, which makes them suitable candidates for high frequency devices applications.     

Machine learning of phases and mechanical properties in complex concentrated alloys

The mechanical properties of complex concentrated alloys (CCAs) depend on their formed phases and corresponding microstructures.The data-driven prediction of the phase formation and associated mechanical properties is essential to discovering novel CCAs.The present work collects 557 samples of various chemical compositions,comprising 61 amorphous,167 single-phase crystalline,and 329 multi-phases crystalline CCAs.Three classification models are developed with high accuracies to category and understand the formed phases of CCAs.Also,two regression models are constructed to predict the hard-ness and ultimate tensile strength of CCAs,and the correlation coefficient of the random forest regression model is greater than 0.9 for both of two targeted properties.Furthermore,the Shapley additive expla-nation (SHAP) values are calculated,and accordingly four most important features are identified.A significant finding in the SHAP values is that there exists a critical value in each of the top four fea-tures,which provides an easy and fast assessment in the design of improved mechanical properties of CCAs.The present work demonstrates the great potential of machine learning in the design of advanced CCAs.     

Investigation the effect of FeNiCoCrMo HEA addition on properties of B4C ceramic prepared by spark plasma sintering

In this study, monolithic B₄C and B₄C-based ceramics incorporating FeNiCoCrMo dual-phase (FCC and BCC) high entropy alloys (HEAs) were produced by spark plasma sintering (SPS). The effect of additives on the densification behavior, mechanical properties, microstructures, and phase evaluation of the samples were investigated. X-ray analysis confirmed the existence of FCC structured HEA and depletion of BCC structured HEA, after high-temperature reaction between B₄C-HEAs. The addition of HEAs enhanced the densification behavior by liquid phase sintering. Furthermore, hardness and fracture toughness values of the samples increased with increasing HEAs content. Fracture toughness and hardness values for all composites were higher than the monolithic B₄C. A combination of the highest density (∼99.22 %) and the best mechanical properties (32.3 GPa hardness and 4.53 MPa m^1/2 fracture toughness) was achieved with 2.00 vol.% HEA addition.     

Ir-Pt/C composite with high metal loading as a high-performance anti-reversal anode catalyst for proton exchange membrane fuel cells

Voltage reversal induced by hydrogen starvation can severely corrode the anode catalyst support and deteriorate the performance of proton exchange membrane fuel cells. A material-based strategy is the inclusion of an oxygen evolution reaction catalyst (e.g., IrO₂) in the anode to promote water electrolysis over harmful carbon corrosion. In this work, an Ir-Pt/C composite catalyst with high metal loading is prepared. The membrane-electrode-assembly (MEA) with 80 wt% Ir-Pt(1:2)/C shows a first reversal time (FRT) of up to 20 hours, which is about ten times that of MEA with 50 wt% Ir-Pt(1:2)/C does. Furthermore, the MEA with 80 wt% Ir-Pt(1:2)/C exhibits a minimum cell voltage loss of 6 mV@1 A/cm² when the FRT is terminated in 2 hours, in which the MEA with 50 wt% Ir-Pt(1:2)/C exhibits a voltage loss of 105 mV@1 A/cm². Further physicochemical and electrochemical characterizations demonstrate that the destruction of anode catalyst layer caused by the voltage reversal process is alleviated by the use of the composite catalyst with high metal loading. Hence, our results reveal that the combination of OER catalyst on the Pt/C with high metal loading is a promising approach to alleviate the degradation of anode catalyst layer during the voltage reversal process for PEMFCs.     

基于改进残差特征蒸馏的轻量级超分辨率网络

基于深度学习的图像超分辨率算法通常采用递归的方式或参数共享的策略来减少网络参数，这将增加网络的深度，使得运行网络花费大量的时间，从而很难将模型部署到现实生活中。为了解决上述问题，本文设计一种轻量级超分辨率网络，对中间特征的关联性及重要性进行学习，且在重建部分结合高分辨率图像的特征信息。首先，引入层间注意力模块，通过考虑层与层之间的相关性，自适应地分配重要层次特征的权重。其次，使用增强重建模块提取高分辨率图像中更精细的特征信息，以此得到更加清晰的重建图片。通过大量的对比实验表明，本文设计的网络与其他轻量级模型相比，有更小的网络参数量，并且在重建精度和视觉效果上都有一定的提升。     

Solution plasma-assisted preparation of highly dispersed NiMnAl-LDO catalyst to enhance low-temperature activity of CO2 methanation

In this work, the solution plasma-assisted method was used to prepare NiMnAl-LDO (layered double oxides) catalysts with different treatment times, which were used for the CO₂ methanation reaction. Solution plasma treatment can enhance the dispersibility of the catalyst, create oxygen defects and improve the chemical adsorption capacity of the catalyst. The results show that the low-temperature activity of the catalyst has been improved after the solution plasma treatment. We demonstrate that the NiMnAl-LDO-P(20) catalyst with high dispersion has the highest catalytic activity in CO₂ methanation (81.3% CO₂ conversion and 96.7% CH₄ selectivity at 200 °C). Even though working for 70 h, the catalyst is still highly stable. This work provides a great promise for improving the low-temperature activity of Ni-based catalysts.     

A high temperature and pressure framework for supercritical water electrolysis

Water electrolysis technologies aim to provide a significant increase in green hydrogen production efficiency. In this work, a framework was developed to explore the use of supercritical water for alkaline electrolysis. This framework was used to perform Arrhenius analysis as a function of potential, and to explore activation energies for sub- and supercritical water electrolysis. An analysis of the conductivity of solution unveiled a discontinuity in the trends between sub- and supercritical potassium hydroxide solution conductivity. Unlike prior work on supercritical water electrolysis, this work investigates trends in electrochemical parameters, the sources of these trends, and how they change between the sub- and supercritical regimes.     

Effects of high-pressure-modified soy 11S globulin on the gel properties and water-holding capacity of pork batter

The effects of high-pressure-modified soy 11S globulin (0.1, 200, and 400 MPa) on the gel properties, water-holding capacity, and water mobility of pork batter were investigated. The high-pressure-modified soy 11S globulin significantly increased (P < 0.05) the emulsion stability, cooking yield, hardness, springiness, chewiness, resilience, cohesiveness, the a^* and b^* values, and the G′ and G′′ values of pork batter at 80 °C, compared with those of 0.1 MPa-modified globulin. In contrast, the centrifugal loss and initial relaxation time of T_2b, T₂₁, and T₂₂ significantly decreased (P < 0.05). Meanwhile, the microstructure was denser, and the voids were smaller and more uniform compared with those of 0.1 MPa-modified globulin. In addition, the sample with 11S globulin modified at 400 MPa had the best water-holding capacity, gel structure, and gel properties among the samples. Overall, the use of high-pressure-modified soy 11S globulin improved the gel properties and water-holding capacity of pork batter, especially under 400 MPa.