Water vapor volatilization and oxidation induced surface cracking of environmental barrier coating systems: A numerical approach

A numerical model is developed for surface crack propagation in brittle ceramic coatings, aiming at the intrinsic failure of rare-earth silicate environmental barrier coating systems (EBCs) under combustion conditions in advanced gas turbines. The main features of progressive degradation of EBCs in such conditions are captured, including selective silica vaporization in the top coat due to exposure to water vapor, diffusion path-dependent bond coat oxidation, as well as crack propagation during cyclic thermal loading. In light of these features, user-defined subroutines are implemented in finite element analysis, where surface crack growth is simulated by node separation. Numerical results are validated by existing experimental data, in terms of monosilicate layer thickening, thermal oxide growth, and fracture behaviors. The experimentally observed quasi-linear oxidation in the early stage is also elucidated. Furthermore, it is suggested that surface crack undergoes rapid propagation in the late stage of extended thermal cycling in water vapor and leads to catastrophic failure, driven by both thermal mismatch and oxide growth stresses. The latter is identified as the dominant mechanism of penetration. Based on detailed analyses of failure mechanisms, the optimization strategy of EBCs composition is proposed, balancing the trade-off between mechanical compliance and erosion resistance.     

A novel approach for bulk micromachining of 4H-SiC by tool-based electrolytic plasma etching in HF-free aqueous solution

Bulk micromachining of single-crystal SiC has been challenging due to its extreme stability both mechanically and chemically. To address this issue, a novel tool-based electrolytic plasma etching method is proposed, with which micropatterns and micro-holes are fabricated in SiC in a hydrofluoric acid-free aqueous solution with no need for masks. The material removal is the result of the combined effects of electrolytic plasma chemistry and physics. The chemistry refers to the reaction of Si with hydroxyl radical to form various SiO_x and with H to form silanes, and the reactions of C to form volatile carbon oxides or hydrocarbons, all of which are accomplished and enhanced under the electrolytic plasma atmosphere. Besides, the local high temperature of plasma thermally promotes the evaporation or dissolution of SiO₂ in NaOH solution. The tool-based electrolytic plasma etching method provides alternative approaches for the fabrication of SiC-based MEMS and devices.     

氮含量及固溶温度对21-6-9奥氏体不锈钢组织和硬度的影响

为了探讨氮含量及固溶温度对21-6-9不锈钢组织和硬度的影响,分别在950、1000、1050和1100 ℃对3种不同氮含量的热轧态21-6-9不锈钢进行1 h固溶处理,通过光学显微镜观察其组织结构,结合Thermo-Calc热力学计算对试验钢的微观组织进行分析,并对其进行硬度测试。结果表明,0.20%~0.28%N的21-6-9不锈钢热轧后沿轧制方向析出铁素体,且钢中铁素体经950~1100 ℃固溶处理可消除,当N含量达到0.34%时,试验钢中不再出现铁素体。随着固溶处理温度的升高,21-6-9不锈钢的晶粒组织长大,硬度降低。N含量的增加可显著提高固溶态21-6-9不锈钢的硬度,其增加程度随固溶处理温度的升高而减弱。     

Research on the formation mechanism of big data technology cooperation networks: empirical evidence from China

Scientometrics - The purpose of the present paper is to investigate the formation mechanism of big data technology cooperation networks by considering the combined effect of three key factors,...     

磁齿轮复合永磁电机拓扑及应用综述

磁齿轮复合电机（MGM）是一种自带减速效应的新型多气隙磁场调制型永磁电机，因其转矩密度大、功率因数高，近年来在混动/电动汽车、低速大转矩直驱、航空航天等领域的应用前景获得了业界的广泛关注；MGM非接触传动的特点使其在医药食品、新能源发电、石油化工等方面具有发展潜力。该文首先介绍磁力齿轮及多种磁齿轮复合电机的工作原理，介绍和比较磁齿轮与电机常见的复合方式及其特点；按照单元排布方式、调制结构、电机及磁齿轮类型等分类梳理近年来MGM在拓扑结构上的研究和创新，介绍各类磁通方向及运行方式MGM的研究。然后介绍MGM近年来在高减速比、低成本、无级变速等一些关键问题上的进展。最后总结磁齿轮复合电机的业界应用现状，并对其应用前景进行展望。     

Improving gelling properties of myofibrillar proteins incorporating with cellulose micro/nanofibres

The effects of cellulose microfibres (CMFs, Average size: 100 ± 5 μm) and cellulose nanofibres (CNFs, Average size: 60 ± 3 nm) on the properties of myofibrillar protein (MP) gels from duck breast meat were studied. The results demonstrated that CMFs and CNFs were mostly connected to MP by non-covalent bonds, the diffusion and cross-linking of MP molecules was promoted, and a denser and more complete gel network was formed. With the increases of CMFs and CNFs concentration (0–10%), the hardness was increased by 13.15% and 19.78% for CMFs10% and CNFs10% gels, respectively, and the elasticity was increased by 40% and 80%, respectively. At the same concentration (0–10%), the increase in gel hardness, viscoelasticity and immobilised water content was greater in the CNFs-MP group than in the CMFs-MP group. The CNFs-MP group had a tighter gel network, and CNFs had a better potential to improve the gelation performance of MP.     

Zinc-Deficiency Induced g-C3N4 Nanosheets: Photocatalytic Nitrogen Fixation Study and Carrier Dynamics

Catalysis Letters - g-C3N4 has great application prospect in the field of photocatalytic nitrogen fixation owing to the advantages of abundant raw materials, low toxicity, low consumption, high...     

基于组合赋权-改进可拓云的装甲车辆PHM系统性能评估

针对装甲车辆PHM系统性能评估过程中主观性强、评语等级界限分明等问题，采用主客观组合赋权和改进可拓云理论相结合的评估方法，增强了评估结果的客观性和准确性；首先介绍了装甲车辆PHM系统组成及其性能评估指标体系，然后采用基于“AHP-熵值”的组合赋权法确定指标权重系数，采用基于最优云熵的改进可拓云理论确定各指标云关联度值，最后采用模糊综合评判方法计算了4套装甲车辆PHM系统性能。结果表明，改进评估方法在不失准确性的同时，能够有效克服评估主观性和评语等级界限分明性，为装甲车辆PHM系统性能的优化提供了借鉴和参考。     

Ensemble relation network with multi-level measure

Fine-grained few-shot learning is a difficult task in image classification. The reason is that the discriminative features of fine-grained images are often located in local areas of the image, while most of the existing few-shotlearning image classification methods only use top-level features and adopt a single measure. In that way, the localfeatures of the sample cannot be learned well. In response to this problem, ensemble relation network with multi-level measure (ERN-MM) is proposed in this paper. It adds the relation modules in the shallow feature space tocompare the similarity between the samples in the local features, and finally integrates the similarity scores from thefeature spaces to assign the label of the query samples. So the proposed method ERN-MM can use local details andglobal information of different grains. Experimental results on different fine-grained datasets show that the proposedmethod achieves good classification performance and also proves its rationality.