Consistent picture for the electronic structure around a vortex core in iron-based superconductors

Based on a two-orbital model and taking into account the presence of the impurity, we studied theoretically the electronic structure in the vortex core of the iron-Pnictide superconducting materials. The vortex is pinned when the impurity is close to the vortex core. The bound states shows up for the unpinned vortex and are wiped out by a impurity. Our results are in good agreement with recent experiments and present a consistent explanation for the different electronic structure of vortex core revealed by experiments on different materials.Comment: 4 pages, 5 figure

Quasiparticle states around a nonmagnetic impurity in electron-doped iron-based superconductors with spin-density-wave order

The quasiparticle states around a nonmagnetic impurity in electron-doped iron-based superconductors with spin-density-wave (SDW) order are investigated as a function of doping and impurity scattering strength. In the undoped sample, where a pure SDW state exists, two impurity-induced resonance peaks are observed around the impurity site and they are shifted to higher (lower) energies as the strength of the positive (negative) scattering potential (SP) is increased. For the doped samples where the SDW order and the superconducting order coexist, the main feature is the existence of sharp in-gap resonance peaks whose positions and intensity depend on the strength of the SP and the doping concentration. In all cases, the local density of states exhibits clear $C_2$ symmetry. We also note that in the doped cases, the impurity will divide the system into two sublattices with distinct values of magnetic order. Here we use the band structure of a two-orbital model, which considers the asymmetry of the As atoms above and below the Fe-Fe plane. This model is suitable to study the properties of the surface layers in the iron-pnictides and should be more appropriate to describe the scanning tunneling microscopy experiments.Comment: 11 pages, 18 figure

Recent Advances in Ambipolar Transistors for Functional Applications

Ambipolar transistors represent a class of transistors where positive (holes) and negative (electrons) charge carriers both can transport concurrently within the semiconducting channel. The basic switching states of ambipolar transistors are comprised of common offâ state and separated onâ state mainly impelled by holes or electrons. During the past years, diverse materials are synthesized and utilized for implementing ambipolar charge transport and their further emerging applications comprising ambipolar memory, synaptic, logic, and lightâ emitting transistors on account of their special bidirectional carrierâ transporting characteristic. Within this review, recent developments of ambipolar transistor field involving fundamental principles, interface modifications, selected semiconducting material systems, device structures, ambipolar characteristics, and promising applications are highlighted. The existed challenges and prospective for researching ambipolar transistors in electronics and optoelectronics are also discussed. It is expected that the review and outlook are well timed and instrumental for the rapid progress of academic sector of ambipolar transistors in lighting, display, memory, as well as neuromorphic computing for artificial intelligence.Ambipolar transistors represent transistors that allow synchronous transport of electrons and holes and their accumulation within semiconductors. This review provides a comprehensive summary of recent advances in various semiconducting materials realized in ambipolar transistors and their functional memory, synapse, logic, as well as lightâ emitting applications.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151885/1/adfm201902105_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151885/2/adfm201902105.pd

A Summary Review of Correlations between Temperatures and Vibration Properties of Long-Span Bridges

The shift of modal parameters induced by temperature fluctuation may mask the changes of vibration properties caused by structural damage and result in false structural condition identification. Thoroughly understanding the temperature effects on vibration properties of long-span bridges becomes an especially important issue before vibration-based damage detection methodologies are applied in real bridges. This paper presents an overview of current research activities and developments in the field of correlations between temperatures and vibration properties of long-span bridges. The theoretical derivation methods using classical structural dynamics and closed-form formulations are first briefly introduced. Then the trend analysis methods that are intended to extract the degree of variability in vibration property under temperature variation for different bridges by numerical analysis, laboratory test, or field monitoring are reviewed in detail. Following that, the development of quantitative models to quantify the temperature influence on vibration properties is discussed including the linear model, nonlinear model, and learning model. Finally, some promising research efforts for promoting the study of correlations between temperatures and vibration properties of long-span bridges are suggested

MetaDIP: Accelerating Deep Image Prior with Meta Learning

Deep image prior (DIP) is a recently proposed technique for solving imaging inverse problems by fitting the reconstructed images to the output of an untrained convolutional neural network. Unlike pretrained feedforward neural networks, the same DIP can generalize to arbitrary inverse problems, from denoising to phase retrieval, while offering competitive performance at each task. The central disadvantage of DIP is that, while feedforward neural networks can reconstruct an image in a single pass, DIP must gradually update its weights over hundreds to thousands of iterations, at a significant computational cost. In this work we use meta-learning to massively accelerate DIP-based reconstructions. By learning a proper initialization for the DIP weights, we demonstrate a 10x improvement in runtimes across a range of inverse imaging tasks. Moreover, we demonstrate that a network trained to quickly reconstruct faces also generalizes to reconstructing natural image patches