A comparative study of two molecular mechanics models based on harmonic potentials

We show that the two molecular mechanics models, the stick-spiral and the beam models, predict considerably different mechanical properties of materials based on energy equivalence. The difference between the two models is independent of the materials since all parameters of the beam model are obtained from the harmonic potentials. We demonstrate this difference for finite width graphene nanoribbons and a single polyethylene chain comparing results of the molecular dynamics (MD) simulations with harmonic potentials and the finite element method with the beam model. We also find that the difference strongly depends on the loading modes, chirality and width of the graphene nanoribbons, and it increases with decreasing width of the nanoribbons under pure bending condition. The maximum difference of the predicted mechanical properties using the two models can exceed 300% in different loading modes. Comparing the two models with the MD results of AIREBO potential, we find that the stick-spiral model overestimates and the beam model underestimates the mechanical properties in narrow armchair graphene nanoribbons under pure bending condition.Comment: 40 pages, 21 figure

Magnetic Proximity Effect and Interlayer Exchange Coupling of Ferromagnetic/Topological Insulator/Ferromagnetic Trilayer

Magnetic proximity effect between topological insulator (TI) and ferromagnetic insulator (FMI) is considered to have great potential in spintronics. However, a complete determination of interfacial magnetic structure has been highly challenging. We theoretically investigate the interlayer exchange coupling of two FMIs separated by a TI thin film, and show that the particular electronic states of the TI contributing to the proximity effect can be directly identified through the coupling behavior between two FMIs, together with a tunability of coupling constant. Such FMI/TI/FMI structure not only serves as a platform to clarify the magnetic structure of FMI/TI interface, but also provides insights into designing the magnetic storage devices with ultrafast response.Comment: 7 pages, 4 figure

A deep learning method using SDA combined with dropout for bearing fault diagnosis

The fault diagnosis of a rolling bearing is at present very important to ensure the steadiness of rotating machinery. According to the non-stationary and non-liner characteristics of bearing vibration signals, a large number of approaches for feature extraction and fault classification have been developed. An effective unsupervised self-learning method is proposed to achieve the complicated fault diagnosis of rolling bearing in this paper, which uses stacked denoising autoencoder (SDA) to learn useful feature representations and improve fault pattern classification robustness by corrupting the input data, meanwhile employs “dropout” to prevent the overfitting of hidden units. Finally the high-level feature representations extracted are set as the inputs of softmax classifier to achieve fault classification. Experiments indicate that the deep learning method of SDA combined with dropout has an advantage in fault diagnosis of bearing, and can be applied widely in future

Exceptional high Seebeck Coefficient and Gas-Flow-Induced Voltage in Multilayer Graphene

Monolayer graphene shows Seebeck coefficient several times and gas-flow-induced voltage twenty times higher than that of bulk graphite. Here we find that the Seebeck coefficient of multilayer graphene increases monotonically with increasing layer and reaches its peak value at hexa-layer ~77% higher than for monolayer and then decreases, although the electric resistance decreases monotonically with increasing layer. The flow-induced voltage is significantly higher in 2, 4, 5, 6, 7 layered graphene than in 1, 3, 8 layered one, against the prevailing view that it should be proportional to Seebeck coefficient. These thickness effects are also in sharp contrast to that in continuous aluminum nanofilms.Comment: 5 figures,20pages,conferenc

STUDY ON THE SEISMIC PERFORMANCE OF RECYCLED AGGREGATE CONCRETE-FILLED LIGHTWEIGHT STEEL TUBE FRAME WITH DIFFERENT ASSEMBLY JOINTS

In order to investigate the construction of column-to-beam joints and to understand the effect of recycled aggregate concrete (RAC) and cross-section of beams and columns on the seismic performance of recycled aggregate concrete-filled light steel tubular frame structure, four new types of assembly joints were proposed in this paper. A low cyclic loading test of six frame specimens was carried out. The failure characteristic, load bearing capacity, hysteresis property, ductility, strength and stiffness degradation, and energy dissipation were analysed. The damage process of the specimen was simulated using the ABAQUS software, and the results agreed well with those obtained from the experiments. The results showed that the construction pathway of the joints exhibited significant influence on the seismic performance of the frame. The proposed reinforced joint (using angle steel and stiffeners) significantly improved the bearing capacity, stiffness and energy dissipation capacity of the recycled aggregate concrete-filled steel tube frame. The seismic performance of the steel frame was improved, while the energy dissipation capacity increased by 635.7% using RAC filled in the steel tubes. Finally, by appropriately increasing the size of cross-section on the beams and columns can improve the bearing capacity, stiffness and ductility of the structure

Graphdiyne-metal contacts and graphdiyne transistors

Graphdiyne is prepared on metal surface, and making devices out of it also inevitably involves contact with metals. Using density functional theory with dispersion correction, we systematically studied for the first time the interfacial properties of graphdiyne contacting with a series of metals (Al, Ag, Cu, Au, Ir, Pt, Ni, and Pd). Graphdiyne is in an n-type Ohmic or quasi-Ohmic contact with Al, Ag, and Cu, while it is in a Schottky contact with Au (at source/drain interface), Pd, Pt, Ni, and Ir (at source/drain-channel interface), with high Schottky barrier heights of 0.39, 0.21 (n-type), 0.30, 0.41, and 0.45 (p-type) eV, respectively. A graphdiyne field effect transistor (FET) with Al electrodes is simulated by using quantum transport calculations. This device exhibits an on-off ratio up to 104 and a very large on-state current of 1.3 * 104 mA/mm in a 10 nm channel length. Thus, a new prospect is opened up for graphdiyne in high performance nanoscale devices.Comment: 27 pages, 9 figure