32,709 research outputs found

    All-optically Control of Light Propagation in Valley-Hall Topological Waveguides of Graphene Metasurfaces

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    We study the influence of graphene Kerr effect on valley-Hall topological modes of a graphene plasmonic crystal waveguide. Extra air holes are introduced to break the spatial-inversion symmetry of the plasmonic metasurface, which can be performed using e-beam lithography. As a result, a gapless Dirac cone and topologically protected edge modes form inside the nontrivial frequency bandgap. Taking advantage of the fact that graphene is a nonlinear optical material possessing an extremely large Kerr coefficient, we demonstrate that an all-optical switch can be implemented in this topological photonic system by controlling an optical signal propagating in the waveguide via a pump beam injected into the bulk modes of the metasurface. This work may lead to new graphene-based active topological photonic nanodevices

    Valley-Hall Topological Transport in Graphene Plasmonic Crystal Waveguides

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    Due to immunity to disorder and structural imperfections, topologically-protected plasmonic modes have recently attracted increasing attention. Here, we introduce two different mechanisms to construct valley-Hall domain-wall interface waveguides in graphene plasmonic crystal to mimic the quantum valley-Hall effect. In the first case, we break the in-plane spatial inversion symmetry of a single-layer graphene plasmonic crystal waveguide to achieve valley-Hall topological characteristics, whereas in the second case, we break the out-of-plane spatial inversion symmetry of a bi-layer graphene plasmonic crystal waveguide to implement the analog quantum valley-Hall effect. A molecular sensor based on this valley-Hall topological transport phenomenon is also be presented

    Enhanced Second-Harmonic Generation in Monolayer MoS2 Driven by a BIC-based Nonlinear Metasurface

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    Dielectric metasurfaces have opened novel routes for nonlinear optics in recent years. In this work, we integrate a nonlinear metasurface with monolayer molybdenum disulfide (MoS2) to enhance second-harmonic generation (SHG) from atomically thin MoS2. By utilizing bound states in the continuum, we achieve about 600× of SHG enhancement from monolayer MoS2 on a resonant metasurface relative to suspended monolayer MoS2. Moreover, an eigenmode expansion approach is exploited to express second-harmonic power and the corresponding analytical results agree well with the rigorous calculations

    Evacuation Planning Based on the Contraflow Technique With Consideration of Evacuation Priorities and Traffic Setup Time

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    Evacuation planning with the contraflow technique is a complex planning problem. The problem is further complicated when more realistic situations such as evacuation priorities and the setup time for the contraflow operation are considered. Such a complex problem has yet to be discussed in the present literature. In this paper, we present a multipleobjective optimization model for this problem and a two-layer algorithm to solve this model. Experiments on three transportation networks with different network scales are presented to show the excellent performance of the proposed model and algorithm.published_or_final_versio

    Minimum thermal conductance in graphene and boron nitride superlattice

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    The minimum thermal conductance versus supercell size (dsd_{s}) is revealed in graphene and boron nitride superlattice with dsd_{s} far below the phonon mean free path. The minimum value is reached at a constant ratio of ds/L5d_{s}/L\approx 5%, where LL is the total length of the superlattice; thus the minimum point of dsd_{s} depends on LL. The phenomenon is attributed to the localization property and the number of confined modes in the superlattice. With the increase of dsd_{s}, the localization of the confined mode is enhanced while the number of confined modes decreases, which directly results in the minimum thermal conductance.Comment: accepted by AP

    ECoFFeS: A Software Using Evolutionary Computation for Feature Selection in Drug Discovery

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    Feature selection is of particular importance in the field of drug discovery. Many methods have been put forward for feature selection during recent decades. Among them, evolutionary computation has gained increasing attention owing to its superior global search ability. However, there still lacks a simple and efficient software for drug developers to take advantage of evolutionary computation for feature selection. To remedy this issue, in this paper, a user-friendly and standalone software, named ECoFFeS, is developed. ECoFFeS is expected to lower the entry barrier for drug developers to deal with feature selection problems at hand by using evolutionary algorithms. To the best of our knowledge, it is the first software integrating a set of evolutionary algorithms (including two modified evolutionary algorithms proposed by the authors) with various evaluation combinations for feature selection. Specifically, ECoFFeS considers both single-objective and multi-objective evolutionary algorithms, and both regression- and classification-based models to meet different requirements. Five data sets in drug discovery are collected in ECoFFeS. In addition, to reduce the total analysis time, the parallel execution technique is incorporated into ECoFFeS. The source code of ECoFFeS can be available from https://github.com/JiaweiHuang/ECoFFeS/
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