Photo- and exchange-field controlled line-type resonant peaks and enhanced spin and valley polarizations in a magnetic WSe2 junction

Existing resonant tunneling modes in the shape of line-type resonances can improve the transport properties of the junction. Motivated by the unique structural properties of monolayer WSe2 e.g. significant spin-orbit coupling (SOC) and large direct bandgap, the transport properties of a normal/ferromagnetic/normal (NFN) WSe2 junction with large incident angles in the presence of exchange field (h), off-resonance light (∆Ω) and gate voltage (U) is studied. In a certain interval of U, the transmission shows a gap with optically controllable width, while outside it, the spin and valley resolved transmissions have an oscillatory behavior with respect to U. By applying ∆Ω (h), an optically (electrically) switchable perfect spin and valley polarizations at all angles of incidence have been found. For large incident angles, the transmission resonances change to spin-valley-dependent separated ideal line-type resonant peaks with respect to U, resulting in switchable perfect spin and valley polarizations, simultaneously. Furthermore, even in the absence of U, applying h or ∆Ω at large incident angles can give some spin-valley dependent ideal transmission peaks, making h or ∆Ω a transmission valve capable of giving a switchable fully spinvalley filtering effect. These findings suggest some alternate methods for providing high-efficiency spin and valley filtering devices based on WSe2.

Edge Doping Engineering of High-Performance Graphene Nanoribbon Molecular Spintronic Devices

We study the quantum transport properties of graphene nanoribbons (GNRs) with a different edge doping strategy using density functional theory combined with nonequilibrium Green’s function transport simulations. We show that boron and nitrogen edge doping on the electrodes region can substantially modify the electronic band structures and transport properties of the system. Remarkably, such an edge engineering strategy effectively transforms GNR into a molecular spintronic nanodevice with multiple exceptional transport properties, namely: (i) a dual spin filtering effect (SFE) with 100% filtering efficiency; (ii) a spin rectifier with a large rectification ratio (RR) of 1.9 ×106; and (iii) negative differential resistance with a peak-to-valley ratio (PVR) of 7.1 ×105. Our findings reveal a route towards the development of high-performance graphene spintronics technology using an electrodes edge engineering strategy.

Preparation and Characterization of Thermoelectric PEDOT/Te Nanorod Array Composite Films

In this study, we prepared Te nanorod arrays via a galvanic displacement reaction (GDR) on a Si wafer, and their composite with poly(3,4-ethylenedioxythiophene) (PEDOT) were successfully synthesized by electrochemical polymerization with lithium perchlorate (LiClO4) as a counter ion. The thermoelectric performance of the composite film was optimized by adjusting the polymerization time. As a result, a maximum power factor (PF) of 235 µW/mK2 was obtained from a PEDOT/Te composite film electrochemically polymerized for 15 s at room temperature, which was 11.7 times higher than that of the PEDOT film, corresponding to a Seebeck coefficient (S) of 290 µV/K and electrical conductivity (σ) of 28 S/cm. This outstanding PF was due to the enhanced interface interaction and carrier energy filtering effect at the interfacial potential barrier between the PEDOT and Te nanorods. This study demonstrates that the combination of an inorganic Te nanorod array with electrodeposited PEDOT is a promising strategy for developing high-performance thermoelectric materials.

Investigation of Phase Segregation in p-Type Bi0.5Sb1.5Te3 Thermoelectric Alloys by In Situ Melt Spinning to Determine Possible Carrier Filtering Effect

One means of enhancing the performance of thermoelectric materials is to generate secondary nanoprecipitates of metallic or semiconducting properties in a thermoelectric matrix, to form proper band bending and, in turn, to induce a low-energy carrier filtering effect. However, forming nanocomposites is challenging, and proper band bending relationships with secondary phases are largely unknown. Herein, we investigate the in situ phase segregation behavior during melt spinning with various metal elements, including Ti, V, Nb, Mo, W, Ni, Pd, and Cu, in p-type Bi0.5Sb1.5Te3 (BST) thermoelectric alloys. The results showed that various metal chalcogenides were formed, which were related to the added metal elements as secondary phases. The electrical conductivity, Seebeck coefficient, and thermal conductivity of the BST composite with various secondary phases were measured and compared with those of pristine BST alloys. Possible band alignments with the secondary phases are introduced, which could be utilized for further investigation of a possible carrier filtering effect when forming nanocomposites.

Large-Area Full-Coverage Remote Sensing Image Collection Filtering Algorithm for Individual Demands

Remote sensing is the main technical means for urban researchers and planners to effectively observe targeted urban areas. Generally, it is difficult for only one image to cover a whole urban area and one image cannot support the demands of urban planning tasks for spatial statistical analysis of a whole city. Therefore, people often artificially find multiple images with complementary regions in an urban area on the premise of meeting the basic requirements for resolution, cloudiness, and timeliness. However, with the rapid increase of remote sensing satellites and data in recent years, time-consuming and low performance manual filter results have become more and more unacceptable. Therefore, the issue of efficiently and automatically selecting an optimal image collection from massive image data to meet individual demands of whole urban observation has become an urgent problem. To solve this problem, this paper proposes a large-area full-coverage remote sensing image collection filtering algorithm for individual demands (LFCF-ID). This algorithm achieves a new image filtering mode and solves the difficult problem of selecting a full-coverage remote sensing image collection from a vast amount of data. Additionally, this is the first study to achieve full-coverage image filtering that considers user preferences concerning spatial resolution, timeliness, and cloud percentage. The algorithm first quantitatively models demand indicators, such as cloudiness, timeliness, resolution, and coverage, and then coarsely filters the image collection according to the ranking of model scores to meet the different needs of different users for images. Then, relying on map gridding, the image collection is genetically optimized for individuals using a genetic algorithm (GA), which can quickly remove redundant images from the image collection to produce the final filtering result according to the fitness score. The proposed method is compared with manual filtering and greedy retrieval to verify its computing speed and filtering effect. The experiments show that the proposed method has great speed advantages over traditional methods and exceeds the results of manual filtering in terms of filtering effect.

Self-AdaptIve LOcal Relief Enhancer (SAILORE): A New Filter to Improve Local Relief Model Performances According to Local Topography

The use of Light Detection and Ranging (LiDAR) is becoming more and more common in different landscape exploration domains such as archaeology or geomorphology. In order to allow the detection of features of interest, visualization filters have to be applied to the raw Digital Elevation Model (DEM), to enhance small relief variations. Several filters have been proposed for this purpose, such as Sky View Factor, Slope, negative and positive Openness, or Local Relief Model (LRM). The efficiency of each of these methods is strongly dependent on the input parameters chosen in regard of the topography of the investigated area. The LRM has proved to be one of the most efficient, but it has to be parameterized in order to be adapted to the natural slopes characterizing the investigated area. Generally, this setting has a single value, chosen as the best compromise between optimal values for each relief configuration. As LiDAR is mainly used in wide areas, a large distribution of natural slopes is often encountered. The aim of this paper is to propose a Self AdaptIve LOcal Relief Enhancer (SAILORE) based on the Local Relief Model approach. The filtering effect is adapted to the local slope, allowing the detection at the same time of low-frequency relief variation on flat areas, as well as the identification of high-frequency relief variation in the presence of steep slopes. First, the interest of this self-adaptive approach is presented, and the principle of the method, compared to the classical LRM method, is described. This new tool is then applied to a LiDAR dataset characterized by various terrain configurations in order to test its performance and compare it with the classical LRM. The results of this test show that SAILORE significantly increases the detection capability while simplifying it.

Theoretical Design of thermal spin molecular logic gates by using a combinational molecular junction

Based on the density functional theory combined with the non-equilibrium Green’s function methodology, we have studied the thermally-driven spin-dependent transport properties of a combinational molecular junction consisting of a planar four-coordinate Fe molecule and a 15,16-dinitrile dihydropyrene/cyclophanediene molecule with single-walled carbon nanotube bridge and electrode. Our results show that the magnetic field and light can effectively regulate the thermally-driven spin-dependent currents. Perfect thermal spin-filtering effect and good thermal switching effect are realized. The results are explained by the Fermi-Dirac distribution function, the spin-resolved transmission spectra, the spatial distribution of molecular projected self-consistent Hamiltonian orbitals, and the spin-resolved current spectra. On the basis of these thermally-driven spin-dependent transport properties, we further design three basic thermal spin molecular AND, OR and NOT gates.

Improved method of manufacturing carbon nanotube infused multifunctional 3D woven composites

This research work aimed to develop smart multifunctional composites via a process for uniformly dispersing carbon nanotubes (CNT) on an orthogonal three-dimensional (3D) woven glass fabric with minimised filtering effect. These smart composites could detect strain under tensile and flexural loading by the piezoresistive response of the infused CNT network. Conventional vacuum assisted resin transfer moulding was modified to control the infusion of 0.25 wt% CNT on the 3D woven glass fabric by varying the vacuum pressure. Results showed that at 101.3 kPa vacuum pressure, the CNT percolated through the thickness of the orthogonal 3D woven glass fabric while being marginally filtered by the fibres and were suitable for sensing tensile strain, whereas at 30.4 kPa, the CNT were deposited only on the surface of the fabric preform without getting filtered and were suitable for sensing flexural strain.