ZIF‐8/ZIF‐67‐Derived Co‐Nx‐Embedded 1D Porous Carbon Nanofibers with Graphitic Carbon‐Encased Co Nanoparticles as an Efficient Bifunctional Electrocatalyst

Herein, an approach is reported for fabrication of Co‐N_x‐embedded 1D porous carbon nanofibers (CNFs) with graphitic carbon‐encased Co nanoparticles originated from metal–organic frameworks (MOFs), which is further explored as a bifunctional electrocatalyst for both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Electrochemical results reveal that the electrocatalyst prepared by pyrolysis at 1000 °C (CoNC‐CNF‐1000) exhibits excellent catalytic activity toward ORR that favors the four‐electron ORR process and outstanding long‐term stability with 86% current retention after 40 000 s. Meanwhile, it also shows superior electrocatalytic activity toward OER, reaching a lower potential of 1.68 V at 10 mA cm^?2 and a potential gap of 0.88 V between the OER potential (at 10 mA cm^?2) and the ORR half‐wave potential. The ORR and OER performance of CoNC‐CNF‐1000 have outperformed commercial Pt/C and most nonprecious‐metal catalysts reported to date. The remarkable ORR and OER catalytic performance can be mainly attributable to the unique 1D structure, such as higher graphitization degree beneficial for electronic mobility, hierarchical porosity facilitating the mass transport, and highly dispersed CoN_xC active sites functionalized carbon framework. This strategy will shed light on the development of other MOF‐based carbon nanofibers for energy storage and electrochemical devices.     

Crystal structure of Cr4AlB4: A new MAB phase compound discovered in Cr-Al-B system

In this communication, the crystal structure of Cr_4AlB_4, a new MAB phase compound(where M is a transition metal, A is Al or Si, B is boron) discovered in Cr-Al-B system is reported. This new MAB phase was synthesized from a mixture of CrB and Al powders at 1000?C and its crystal structure was determined by a combination of X-ray diffraction, first-principles calculations and energy dispersive X-ray spectroscopy(EDS). Cr_4AlB_4 crystallizes in an orthorhombic structure with Immm space group. The lattice constants are a = 2.9343(6) ?, b = 18.8911(0) ?, c = 2.9733(7) ?, and the atomic positions are Cr1 at 4 g(0, 0.2936(5),0), Cr2 at 4 h(0.5, 0.5859(7), 0), Al at 2 b(0, 0.5, 0.5), B1 at 4 h(0, 0.3839(8), 0.5) and B2 at 4 g(0.5, 0.6646(2),0.5).     

Prediction of stable high-pressure structures of tantalum nitride TaN2

Structure searches based on a combination of first-principles calculations and a particle swarm optimization technique unravel two new stable high-pressure structures (C2/m and Cmce) for TaN₂. The structural features, mechanical properties, formation enthalpies, electronic structure, and phase diagram of TaN₂ are fully investigated. Being mechanically and dynamically stable, the two phases could be made metastable experimentally at ambient conditions.     

超空泡射弹尾拍运动流固耦合动力学响应研究

基于ANSYS软件和CFX软件的双向隐式交错迭代法对超空泡射弹尾拍运动过程中的流固耦合响应进行了研究,结构响应仿真采用有限元法、流场仿真采用分相流模型和SST湍流模型,重点比较分析了流固耦合作用对射弹运动姿态和流体动力的影响,给出了尾拍过程中弹体应力的变化规律.     

谐振子的辛欧拉分析方法

针对理想简谐振子力学模型,研究了其守恒律,并利用辛欧拉格式分析简谐振子振动过程.首先给出了谐振子系统的平方守恒律、周期守恒律和相差守恒律.构造了谐振子的普通欧拉格式和辛欧拉格式,研究了两种格式下三种守恒律各自的保持情况.模拟结果显示:辛欧拉格式能够精确保持时域守恒律(平方守恒律),但无法保持频域守恒律(周期守恒律和相差守恒律).如要克服辛欧拉格式的不足,需按邢誉峰教授提出的方法进行校正.     

全组分木质活性炭球的制备及其乙烯吸附性能研究

目的 制备全组分木质活性炭球,应用于乙烯吸附,筛选出乙烯吸附效果最好的活性炭球制备工艺。方法 以木材液化物为原料,探究不同的固化时间对制备的全组分木质活性炭球微观形貌的影响,利用场发射扫描电子显微镜观察其微观结构,通过氮气吸附-解析等温线计算样品孔径分布及其比表面积;使用气相色谱仪,结合乙烯吸附标准曲线,分析时间及吸附剂用量对4种活性炭球吸附乙烯性能的影响。结果 随着固化时间的增加,碳球表面趋于光滑、内部结构越来越致密,石墨化程度逐渐提高,比表面积和孔容逐渐减小。当固化时间为0.5 h时,比表面积高达2 073 m²/g,乙烯吸附量高达197.99 mg/g,约3 h达到吸附平衡。结论 全组分木质活性炭球能有效吸附乙烯气体,固化时间太长,活性炭球内部孔隙减少,不利于吸附乙烯气体。活性炭球孔容越大,乙烯吸附效果越好。     

梁柱半刚性连接对钢结构整体抗震性能的影响研究

本文研究空间钢结构力学模型中,梁柱之间纯刚性连接和不同程度的半刚性连接对整体结构抗震性能的影响.以一阶横向振动周期、结构梁柱轴力比和层间位移限角为评价指标,在实测地震波的激励下,采用ANSYS软件分析横向弯曲和轴向扭转刚度对塔形钢结构整体抗震性能的影响.仿真分析表明,横向弯曲刚度对结构抗震性能的影响显著,从而为钢结构建筑的设计和分析提供参考.     

Vibration data-driven machine learning architecture for structural health monitoring of steel frame structures

A vibration data-based machine learning architecture is designed for structural health monitoring (SHM) of a steel plane frame structure. This architecture uses a Bag-of-Features algorithm that extracts the speeded-up robust features (SURF) from the time-frequency scalogram images of the registered vibration data. The discriminative image features are then quantised to a visual vocabulary using K-means clustering. Finally, a support vector machine (SVM) is trained to distinguish the undamaged and multiple damage cases of the frame structure based on the discriminative features. The potential of the machine learning architecture is tested for an unseen dataset that was not used in training as well as with some datasets from entirely new damages close to existing (i.e., trained) damage classes. The results are then compared with those obtained using three other combinations of features and learning algorithms—(i) histogram of oriented gradients (HOG) feature with SVM, (ii) SURF feature with k-nearest neighbours (KNN) and (iii) HOG feature with KNN. In order to examine the robustness of the approach, the study is further extended by considering environmental variabilities along with the localisation and quantification of damage. The experimental results show that the machine learning architecture can effectively classify the undamaged and different joint damage classes with high testing accuracy that indicates its SHM potential for such frame structures.