Features of crystal structures and thermo-mechanical properties of weberites RE3NbO7 (RE=La,Nd, Sm,Eu, Gd) ceramics

The features of crystal structures, thermo-mechanical properties and their dominant mechanisms of weberites RE₃NbO₇ were studied as high-temperature oxides. We concentrated on connections between structures and thermo-mechanical properties, the influences of bond lengths, lattice distortion degrees and microstructures on these properties were estimated. The shortening of bond length and increment of bonding strength would lead to the increase of mechanical properties. The Vickers hardness (4.5-7.8 GPa) and toughness (0.5-1.6 MPa·m^1/2) of weberites RE₃NbO₇ are enhanced by grain refinement and increment of bond strength, while crystal structures, bond lengths, and lattice distortion degrees influenced their Young's modulus (100-170 GPa). Nano-indentation was applied to test the influence of microstructures on modulus and hardness. The dominant mechanisms for mechanical properties and thermal conductivity were proposed, which was conducive to properties tailoring and engineering applications of weberites RE₃NbO₇ oxides.     

Elastic modulus prediction based on thermal conductivity for silica aerogels and fiber reinforced composites

The speed of sound is a critical parameter in the test of mechanical and thermal properties. In this work, we proposed a testing method to obtain the elastic modulus of silica aerogel from the sound speed formulas. The solid thermal conductivity of the silica aerogel is experimentally measured for predicting the sound speeds, and then the elastic modulus is calculated based on the elasticity sound speed model. The experimental data of the solid thermal conductivity of silica aerogels with different densities are employed and the obtained elastic modulus is fitted as a power-law exponential function of the density. Two existing sound speed models and three groups of available experimental data are also employed to validate the present fitting relation, and good agreement is obtained for the silica aerogel in the density range of 150–350 kg/m³. The fitting formula can also be extended to estimate the elastic modulus of the glass fiber-reinforced silica aerogel composite. The results show that the elastic modulus of the aerogel composite is sensitive to the glass fiber volume fraction, while the thermal conductivity is weakly dependent on the glass fiber volume fraction at room temperature in the studied range of fiber volume fraction.     

基于乙酰胆碱酯酶和氧化应激研究海胆酮对阿尔茨海默症的作用机制

海胆酮是一种酮式类胡萝卜素，主要从海胆及藻类等海洋生物中提取。本文研究海胆酮对乙酰胆碱酯酶（acetylcholinesterase，AChE）的抑制作用，应用酶动力学、荧光光谱、圆二色光谱和分子对接技术研究海胆酮对AChE的抑制机理，并用淀粉样β蛋白片段25～35（amyloid beta-peptide 25-35，Aβ25-35）诱导大鼠肾上腺嗜铬细胞瘤细胞（PC12细胞）建立阿尔茨海默症（Alzheimer’s disease，AD）模型，研究海胆酮对AD细胞模型氧化应激损伤的作用。结果表明，海胆酮有很强的AChE抑制活性，其半抑制质量浓度为（16.29±0.97）μg/mL，抑制常数Ki为3.82 μg/mL，表现为竞争性抑制；海胆酮可诱导AChE二级结构改变，更容易与AChE活性中心氨基酸Ser200、His440、Trp84和Tyr121结合，阻碍底物碘代硫代乙酰胆碱（acetylthiocholine iodide，ATCI）与酶结合，从而引起酶活力降低。海胆酮能有效抑制Aβ25-35诱导PC12细胞的AChE活力，降低丙二醛含量，增加超氧化物歧化酶、过氧化氢酶和谷胱甘肽过氧化物酶活力，减轻Aβ25-35诱导的PC12细胞氧化应激损伤。本研究基于AChE和氧化应激阐明了海胆酮对AD的潜在作用机制，为海胆酮在功能食品、生物医药等领域的应用提供了数据支持和理论根据。     

The effect of hydrolysed tragacanth gum and inulin on the probiotic viability and quality characteristics of low‐fat yoghurt

This study investigates the effect of supplementation of tragacanth gum (GT, 0.05% w/v), low molecular weight gum tragacanth (LMWGT, 0.5% w/v) and inulin (0.5% w/v) on the viability of Bifidobacterium bifidum and the quality parameters of low‐fat yoghurt during a three‐week storage period. The count of probiotics was found to be 7.8 log cfu/g in inulin, and LMWGT enriched yoghurts at the end of the storage period. The minimum water holding capacity and the maximum syneresis values were also obtained in the low‐fat yoghurt enriched with GT throughout the storage time. The samples containing inulin and LMWGT revealed sensory attributes that were judged superior compared to those in GT.     

Destructive and non-destructive evaluation of concrete for optimum sand to aggregate volume ratio

Aggregates are the biggest contributor to concrete volume and are a crucial parameter in dictating its mechanical properties. As such, a detailed experimental investigation was carried out to evaluate the effect of sand-to-aggregate volume ratio (s/a) on the mechanical properties of concrete utilizing both destructive and non-destructive testing (employing UPV (ultrasonic pulse velocity) measurements). For investigation, standard cylindrical concrete samples were made with different s/a (0.36, 0.40, 0.44, 0.48, 0.52, and 0.56), cement content (340 and 450 kg/m³), water-to-cement ratio (0.45 and 0.50), and maximum aggregate size (12 and 19 mm). The effect of these design parameters on the 7, 14, and 28 d compressive strength, tensile strength, elastic modulus, and UPV of concrete were assessed. The careful analysis demonstrates that aggregate proportions and size need to be optimized for formulating mix designs; optimum ratios of s/a were found to be 0.40 and 0.44 for the maximum aggregate size of 12 and 19 mm, respectively, irrespective of the W/C (water-to-cement) and cement content.     

Resource potential for wind-hydrogen power in Ukraine

The paper provides an assessment of the current wind energy potential in Ukraine, and discusses developmental prospects for wind-hydrogen power generation in the country. Hydrogen utilization is a highly promising option for Ukraine's energy system, environment, and business. In Ukraine, an optimal way towards clean zero-carbon energy production is through the development of the wind-hydrogen sector. In order to make it possible, the energy potential of industrial hydrogen production and use has to be studied thoroughly.Ukraine possesses huge resources for wind energy supply. At the beginning of 2020, the total installed capacity of Ukrainian wind farms was 1.17 GW. Wind power generation in Ukraine has significant advantages in comparison to the use of traditional sources such as thermal and nuclear energy.In this work, an assessment of the wind resource potential in Ukraine is made via the geographical approach suggested by the authors, and according to the «Methodical guidelines for the assessment of average annual power generation by a wind turbine based on the long-term wind speed observation data». The paper analyses the long-term dynamics of average annual wind speed at 40 Ukrainian weather stations that provide valid data. The parameter for the vertical wind profile model is calculated based on the data reanalysis for 10 m and 50 m altitudes. The capacity factor (CF) for modern wind turbine generators is determined. The CF spatial distribution for an average 3 MW wind turbine and the power generation potential for the wind power plants across the territory of Ukraine are mapped.Based on the wind energy potential assessment, the equivalent possible production of water electrolysis-derived green hydrogen is estimated. The potential average annual production of green hydrogen across the territory of Ukraine is mapped.It is concluded that Ukraine can potentially establish wind power plants with a total capacity of 688 GW on its territory. The average annual electricity production of this system is supposed to reach up to 2174 bln kWh. Thus, it can provide an average annual production of 483 billion Nm³ (43 million tons) of green hydrogen by electrolysis. The social efficiency of investments in wind-hydrogen electricity is presented.     

Validating the efficiency of γ-Al2O3/La0.6Sr0.4Co0.2Fe0.8O3-δ double-layer electrolyte for low temperature solid oxide fuel cell

Perovskite La_0.6Sr_0.4Co_0.2Fe_0.8O_3+δ (LSCF) as a promising cathode material possessed overwhelming electronic conduction along with certain ionic conductivity. Its strong electron conduction capability hinder the application of pure-phase LSCF as electrolyte in semiconductor membrane fuel cell (SMFC). In order to constrain the electron transport and take advantage of the decent ion conduction of LSCF, a thin layer of γ-Al₂O₃ with insulating property was added as an electron barrier layer and combine with LSCF to form a two-layer structure electrolyte. Through adjusting the weight ratio of LSCF/γ-Al₂O₃ to optimize the thickness of double layers, an open circuit voltage of 0.98 V and a maximum power density of 690 mW/cm² was received at 550 °C. At the same time, SEM, EIS and other characterization technology had proven that the LSCF/γ-Al₂O₃ bi-layer electrolyte can work efficiently at low temperature. The advantage of this work is the application of double-layer (γ-Al₂O₃/LSCF) structure electrolyte to instead of mixed material electrolyte in low-temperature solid oxide fuel cells. Structural innovation and the using of insulating materials provided clues for the further development of SMFC.     

Slow strain rate technique for studying hydrogen induced cracking in 34CrMo4 high strength steel

Alloy hardened steels offer excellent combination of mechanical properties, hardenability and corrosion resistance. 34CrMo4 is a medium carbon, low alloy steel widely used due to a good combination of high-strength, toughness and wear resistance. However, this steel experiences hydrogen embrittlement (HE), a complex phenomenon depending on the composition and microstructure. This work estimates de loss of the mechanical properties caused by hydrogen in electrochemically H-charged specimens in absence of mechanical stress but also, at low strain rate and constant load. H-charging for 2 and 6 h induce YS losses of about 40% and 71% and UTS losses of 39% and 59%, respectively. The synergistic effect of the stress and the H-charging process leads to a higher loss, 91%, and a faster brittle fracture even though hydrogen content is similar to those firstly H-charged and then tested in air.     

Pore-scale study of effects of different Pt loading reduction schemes on reactive transport processes in catalyst layers of proton exchange membrane fuel cells

Reducing the Platinum (Pt) loading while maintaining the performance is highly desired for promoting the commercial use of proton exchange membrane fuel cells (PEMFCs). Different methods have been adopted to fabricate catalyst layers (CLs) with low Pt loading, including utilizing lower Pt/C catalysts (MA), mixing high Pt/C catalysts with bare carbon black particles (MB), and reducing CL thickness while maintaining high Pt/C ratio (MC). In this study, self-developed pore-scale model is adopted to investigate the performance of the three Pt reduction methods. It is found that MA shows the best performance while MB shows the worst. Then, effects of Pt dispersion are further explored. The results show that denser Pt sites will result in higher local oxygen flux and thus higher local transport resistance. Therefore, MA method, which shows the better Pt dispersion, leads to improved performance. Third, CLs with quasi-realistic structures are investigated. Higher tortuosity resulting from the random pores produces higher bulk resistance along the thickness direction, while MA still exhibits the best performance. Finally, improved CL structures are investigated by designing perforated CL structures. It is found that adding perforations can significantly reduce the bulk transport resistance and can improve the CL performance. It is demonstrated that CL structure plays important roles on performance, and there are still huge potentials to further improve CL performance by increasing Pt dispersion and optimizing CL structures.     

Predicting elastic modulus of porous La0.6Sr0.4Co0.2Fe0.8O3-δ cathodes from microstructures via FEM and deep learning

In this work, a deep learning accelerated homogenization framework is developed for prediction of elastic modulus of porous materials directly from their inner microstructures. The finite element method (FEM) and the homogenization theory are used to obtain the macroscopic properties of materials based on their microstructures. Based on a large dataset consisting of various microstructures and corresponding elastic properties via FEM, a deep convolutional neural network (CNN) is trained to capture the nonlinear functional relationship between the microstructure features and their macroscopic elastic properties. The deep learning model is finally well validated against extra new samples with excellent predictive performances. This demonstrates that the CNN deep learning model can be trusted as a surrogate model for the FEM based homogenization method, with the computation time being reduced by several orders of magnitude. The proposed deep learning framework is highly extendable for prediction of various macroscopic properties from microstructures.