Despite much anticipation of valleytronics as a candidate to replace the aging complementary metal-oxidesemiconductor (CMOS) based information processing, its progress is severely hindered by the lack of practical ways to manipulate valley polarization all electrically in an electrostatic setting. Here, we propose a class of all-electric-controlled valley filter, valve, and logic gate based on the valley-contrasting transport in a merging Dirac cones system. The central mechanism of these devices lies on the pseudospin-assisted quantum tunneling which effectively quenches the transport of one valley when its pseudospin configuration mismatches that of a gate-controlled scattering region. The valley polarization can be abruptly switched into different states and remains stable over semi-infinite gate-voltage windows. Colossal tunneling valleypseudomagnetoresistance ratio of over 10000% can be achieved in a valley-valve setup. We further propose a valleytronic-based logic gate capable of covering all 16 types of two-input Boolean logics. Remarkably, the valley degree of freedom can be harnessed to resurrect logical reversibility in two-input universal Boolean gate. The (2+1) polarization states (two distinct valleys plus a null polarization) reestablish one-to-one inputto-output mapping, a crucial requirement for logical reversibility, and significantly reduce the complexity of reversible circuits. Our results suggest that the synergy of valleytronics and digital logics may provide new paradigms for valleytronic-based information processing and reversible computing. Despite much anticipation of valleytronics as a candidate to replace the aging complementary metal-oxidesemiconductor (CMOS) based information processing, its progress is severely hindered by the lack of practical ways to manipulate valley polarization all electrically in an electrostatic setting. Here, we propose a class of all-electric-controlled valley filter, valve, and logic gate based on the valley-contrasting transport in a merging Dirac cones system. The central mechanism of these devices lies on the pseudospin-assisted quantum tunneling which effectively quenches the transport of one valley when its pseudospin configuration mismatches that of a gate-controlled scattering region. The valley polarization can be abruptly switched into different states and remains stable over semi-infinite gate-voltage windows. Colossal tunneling valley-pseudomagnetoresistance ratio of over 10 000% can be achieved in a valley-valve setup. We further propose a valleytronic-based logic gate capable of covering all 16 types of two-input Boolean logics. Remarkably, the valley degree of freedom can be harnessed to resurrect logical reversibility in two-input universal Boolean gate. The (2 + 1) polarization states (two distinct valleys plus a null polarization) reestablish one-to-one input-to-output mapping, a crucial requirement for logical reversibility, and significantly reduce the complexity of reversible circuits. Our results suggest that the synergy of va...
We are interested in exploring the limit in using deep learning (DL) to study the electromagnetic (EM) response for complex and random metasurfaces, without any specific applications in mind. For simplicity, we focus on a simple pure reflection problem of a broadband EM plane wave incident normally on such complex metasurfaces in the frequency regime of 2–12 GHz. In doing so, we create a DL-based framework called the metasurface design deep convolutional neural network (MSDCNN) for both forward and inverse designs of three different classes of complex metasurfaces: (a) arbitrary connecting polygons, (b) basic pattern combination, and (c) fully random binary patterns. The performance of each metasurface is evaluated and cross-benchmarked. Dependent on the type of complex metasurfaces, sample size, and DL algorithms used, the MSDCNN is able to provide good agreement and can be a faster design tool for complex metasurfaces than the traditional full-wave EM simulation methods. However, no single universal deep convolutional neural network model can work well for all metasurface classes based on detailed statistical analysis (such as mean, variance, kurtosis, and mean-squared error). Our findings report important information on the advantages and limitations of current DL models in designing these ultimately complex metasurfaces.
. (2016). Nonlocal transistor based on pure crossed Andreev reflection in a EuO-graphene/ superconductor hybrid structure. Physical Review B: Condensed Matter and Materials Physics, 93 (4), 041422-1-041422-5.Nonlocal transistor based on pure crossed Andreev reflection in a EuOgraphene/superconductor hybrid structure AbstractWe study the interband transport in a superconducting device composed of graphene with EuO-induced exchange interaction. We show that pure crossed Andreev reflection can be generated exclusively without the parasitic local Andreev reflection and elastic cotunnelling over a wide range of bias and Fermi levels in an EuO-graphene/superconductor/EuO-graphene device. The pure nonlocal conductance exhibits rapid on-off switching and oscillatory behavior when the Fermi levels in the normal and the superconducting leads are varied. The oscillation reflects the quasiparticle propagation in the superconducting lead and can be used as a tool to probe the subgap quasiparticle mode in superconducting graphene, which is inaccessible from the current-voltage characteristics. Our results suggest that the device can be used as a highly tunable transistor that operates purely in the nonlocal and spin-polarized transport regime. We study the interband transport in a superconducting device composed of graphene with EuO-induced exchange interaction. We show that pure crossed Andreev reflection can be generated exclusively without the parasitic local Andreev reflection and elastic cotunnelling over a wide range of bias and Fermi levels in an EuO-graphene/superconductor/EuO-graphene device. The pure nonlocal conductance exhibits rapid on-off switching and oscillatory behavior when the Fermi levels in the normal and the superconducting leads are varied. The oscillation reflects the quasiparticle propagation in the superconducting lead and can be used as a tool to probe the subgap quasiparticle mode in superconducting graphene, which is inaccessible from the current-voltage characteristics. Our results suggest that the device can be used as a highly tunable transistor that operates purely in the nonlocal and spin-polarized transport regime.
There are, recently, remarkable achievements in turning light-matter interactions into strong coupling quantum regime. In particular, room temperature plexcitonic strong coupling in plasmon-exciton hybrid systems can bring promising benefits for fundamental and applied physics. Herein we will review theoretical insights and recent experimental achievements in plexcitonic strong coupling and divide this review into two main parts. The first part will briefly introduce the general field of strong coupling, including its origin and history, physical mechanisms and theoretical models, as well as recent advanced applications of strong coupling, such as the quantum or biochemical devices enabled by optical strong coupling. The second part will zoom in and concentrate on plexcitonic strong coupling by introducing its unique features and new potentials (such as single-particle ultrastrong coupling, strong coupling dynamics in femtosecond scale) and discussing the limitations and challenges of plexcitonic strong coupling, which will also be accompanied by potential solutions such as the microcavity-engineered plexcitonics, spectral hold burning effects, and metamaterial-based strong coupling. Finally, we will summarize and conclude this review, highlighting the future research directions and promising applications.
Metal‐organic frameworks (MOFs) have attracted considerable attention in numerous applications due to their large surface areas, tunable pore size, and chemical versatility. However, the performance of most MOFs and their related derivatives in applications are still hindered due to their unoptimized form. Hierarchical nano‐micromacropore MOF structure constructed by 3D printing has been shown to guide working species transportation routes, accelerates ion transportation, and increases the accessible area of MOF, thus leading to improved kinetics and enhanced application performances. Nevertheless, there is a lack of a review on 3D‐printed MOFs and their applications that summarizes and promotes this field's progress. This review first introduces the progress of preparing and embedding MOF into structures via 1) MOF coating processes, 2) in situ growth of MOFs, and 3) using presynthesized MOFs to tune the nano‐ and microstructure of MOF. Subsequently, based on various 3D printing technologies, the principles behind various feedstock material preparation and their related printing processes are discussed with respect to the macrostructure. Thereafter, the advances and recent progress of various devices by 3D‐printed MOFs are summarized with detailed analyses and discussion. Finally, an outlook in this promising field is proposed to provide a progression route for 3D‐printed MOFs.
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