Strong Graphene 3D Assemblies with High Elastic Recovery and Hardness

The rational design and construction of 3D graphene assemblies is a crucial step to extend the graphene properties for practical applications. Here, a novel interfacially reactive self‐assembling process is reported to prepare well‐organized 3D honeycomb‐like graphene assemblies with unique polygonal nanopores interconnected by silicon‐oxygen chemical bonds. The newly developed silicate‐bridged graphene assembly (SGA) exhibits an exceptionally high hardness of 13.09 GPa, outperforming all existing 3D graphene materials, while maintains high Young's modulus (162.96 GPa), elastic recovery (75.27%), and superb thermal stability (600 °C in air). The observed unusual merits are resulted from unique pore structure combining the mechanical stability of the trihedral‐nanopore structure and the deformability of the other polygonal nanopores. As a filling material, a merely 0.05% (w/w) addition of SGA could double the impact resistance of unsaturated resins (e.g., polyester). While SGA is attractive for various applications, including body armors, wearable electronics, space elevators, and multifunctional reinforcement fibers for automobiles, and aerospace vehicles, the novel liquid sodium–water interfacial reactive self‐assembling developed in this study could open avenues for further development of various well‐defined 3D assemblies from graphene and many other materials.     

剪力墙结构高层住宅楼爆破拆除数值模拟研究

剪力墙结构是高层建筑的主要形式之一,此类结构整体刚度大,在实际爆破拆除中需要布置的炮孔多且爆破网路复杂,对爆破拆除提出很高要求。茅台三栋高层住宅楼均为钢筋混凝土剪力墙结构,采用单向双折叠倒塌以及多段延时起爆技术,为预测分析其倒塌效果,利用ANSYS/LS-DYNA有限元程序建立三栋楼的分离式共节点模型,对爆破拆除倒塌过程进行数值模拟。由数值模拟效果与实际爆破效果的对比分析中得出:分离式共节点模型可以准确展现结构倒塌过程中的旋转形态以及承重部位混凝土的破碎失效过程,模拟的爆堆范围与形态与实际的爆破效果也非常吻合。可见基于分离式共节点模型的数值模拟方法对于预测建筑物的爆破拆除过程及分析其效果有很好的借鉴指导意义,具有显著的工程应用价值。     

玄武岩纤维布增强树脂基复合材料约束高温损伤混凝土轴压力学性能

对36个玄武岩纤维布增强树脂基复合材料（BFRP）约束加固的高温损伤混凝土圆柱体和15个不同高温损伤的对比试件进行了轴压试验。试验表明,BFRP侧向约束能显著改变混凝土圆柱体的破坏形态,提高混凝土圆柱体的轴压强度和变形能力。其中二层BFRP包裹的200℃、400℃、600℃和800℃高温损伤混凝土圆柱体的轴压强度分别提高了56%、82%、234%和250%,轴向变形分别提高了328%、198%、232%和136%。采用典型的纤维增强复合材料约束常温未损伤混凝土轴压强度和变形计算模型预测纤维增强复合材料约束高温损伤混凝土轴压极限强度和极限变形时存在较大的偏差。基于本文试验数据,确定了BFRP约束高温损伤混凝土极限应力和极限应变计算模型中与温度相关的参量,建议了适用于预测纤维增强复合材料约束高温损伤混凝土的极限应力计算模型和极限应变计算模型。     

Visualizing the Anomalous Charge Density Wave States in Graphene/NbSe2 Heterostructures

Metallic layered transition metal dichalcogenides (TMDs) host collective many-body interactions, including the competing superconducting and charge density wave (CDW) states. Graphene is widely employed as a heteroepitaxial substrate for the growth of TMD layers and as an ohmic contact, where the graphene/TMD heterostructure is naturally formed. The presence of graphene can unpredictably influence the CDW order in 2D CDW conductors. This work reports the CDW transitions of 2H-NbSe₂ layers in graphene/NbSe₂ heterostructures. The evolution of Raman spectra demonstrates that the CDW phase transition temperatures (T_CDW) of NbSe₂ are dramatically decreased when capped by graphene. The induced anomalous short-range CDW state is confirmed by scanning tunneling microscopy measurements. The findings propose a new criterion to determine the T_CDW through monitoring the line shape of the A_1g mode. Meanwhile, the 2D band is also discovered as an indicator to observe the CDW transitions. First-principles calculations imply that interfacial electron doping suppresses the CDW states by impeding the lattice distortion of 2H-NbSe₂. The extraordinary random CDW lattice suggests deep insight into the formation mechanism of many collective electronic states and possesses great potential in modulating multifunctional devices.     

微纳米尺度生物体温度测量方法及其展望

温度是描述凝聚态物质系统热力学状态和演变的基本参数。微纳米级的温度传感在生物体(细胞)、芯片、低维人工材料等领域中有着重要的应用前景,可作为定量化观测生物体活动能力、化学反应、生命演变的技术途径。鉴于该类型温度传感器的种类繁多、计量性能差异明显,主要从测温技术特点和计量性能两个方面,对应用于微纳米尺度生物体测温的微纳热电偶、热电阻、红外热像仪、磁性纳米粒子和荧光发光等类型的温度测量方法进行了综述和比较分析,并展望了微纳米尺度生物体温度测量领域的发展前景。     

Air Pollution Prediction Via Graph Attention Network and Gated Recurrent Unit

PM2.5 concentration prediction is of great significance to environmental protection and human health. Achieving accurate prediction of PM2.5 concentration has become an important research task. However, PM2.5 pollutants can spread in the earth’s atmosphere, causing mutual influence between different cities. To effectively capture the air pollution relationship between cities, this paper proposes a novel spatiotemporal model combining graph attention neural network (GAT) and gated recurrent unit (GRU), named GAT-GRU for PM2.5 concentration prediction. Specifically, GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities, and GRU is to extract the temporal dependence of the long-term data series. The proposed model integrates the learned spatio-temporal dependencies to capture long-term complex spatio-temporal features. Considering that air pollution is related to the meteorological conditions of the city, the knowledge acquired from meteorological data is used in the model to enhance PM2.5 prediction performance. The input of the GAT-GRU model consists of PM2.5 concentration data and meteorological data. In order to verify the effectiveness of the proposed GAT-GRU prediction model, this paper designs experiments on real-world datasets compared with other baselines. Experimental results prove that our model achieves excellent performance in PM2.5 concentration prediction.     

基于有限元模型修正的土木结构损伤识别方法

基于有限元模型修正的结构损伤识别方法计算过程直观、物理意义明确,能同步识别结构损伤位置和损伤程度,是结构损伤评估最直接的依据。该文介绍了基于有限元模型修正的损伤识别方法基本原理与过程,总结近三十年来有限元模型修正技术的发展。土木工程结构模型复杂性(包含大量单元、节点、待修正参数等)导致有限元模型修正过程效率低,详细介绍了基于子结构有限元模型修正的土木工程损伤识别方法在提高计算效率方面的优势。将两种有限元模型修正方法应用于一栋超高层建筑数值模型的损伤识别,说明两类方法在土木结构损伤识别中的特点。土木工程尺度大而结构损伤通常发生在局部区域,子结构方法将整体结构的分析转换为对若干独立子结构的分析,通过少数局部子结构的模型修正实现损伤识别,避免重复分析大尺寸整体结构,为大型结构基于有限元模型修正的损伤识别开辟高精度和高效率的途径。     

确定主应力方向的简捷方法

给出了确定主应力方向的简捷方法,并论证了该方法的理论根据。     

车联网络通过两级量化自适应卡尔曼滤波实现车辆状态预测

随着城市化和机动化的快速发展,交通安全越来越受到人们的关注。利用车载网络系统获取车载数据来预测车辆下一时刻的车载状态,对于提高运输路段的交通安全起着重要作用。文中提出一种基于自回归滑动平均(Auto-Regressice Mo-ving Average,ARMA)模型的两级量化自适应卡尔曼滤波算法,来预测车辆的行车状态(行驶的方向、行驶的车道、车辆的速度和加速度)。首先,开发了一个车载网络系统,通过交换车载单元(On-Board Unit,OBU)和路边单元(Roadside Unit,RSU)之间的交通数据来获取车辆数据;然后,通过配置在路边单元的边缘云服务器来预测车辆状态;最后,边缘服务器把预测到的状态信息广播给其他路边单元,以便交叉口其他车辆获取车辆信息。实验结果验证了用于预测加速度的自回归移动平均模型的有效性。此外,文中还评估了所提算法的有效性。与其他3种预测算法相比,所提算法的速度预测精度分别提高了90.62%,89.81%,82.76%,这说明该算法在车载网络中能有效预测车辆状态。