Observation of Quantized Hall Effect and Shubnikov-de Hass Oscillations in Highly Doped Bi2Se3: Evidence for Layered Transport of Bulk Carriers

Bi2Se3 is an important semiconductor thermoelectric material and a prototype topological insulator. Here we report observation of Shubnikov-de Hass (SdH) oscillations accompanied by quantized Hall resistances (Rxy) in highly-doped n-type Bi2Se3 with bulk carrier concentrations of few 10^19 cm^-3. Measurements under tilted magnetic fields show that the magnetotransport is 2D-like, where only the c-axis component of the magnetic field controls the Landau level formation. The quantized step size in 1/Rxy is found to scale with the sample thickness, and average ~e2/h per quintuple layer (QL). We show that the observed magnetotransport features do not come from the sample surface, but arise from the bulk of the sample acting as many parallel 2D electron systems to give a multilayered quantum Hall effect. Besides revealing a new electronic property of Bi2Se3, our finding also has important implications for electronic transport studies of topological insulator materials.Comment: accepted by Physical Review Letters (2012

4,6-Dinitro­benzene-1,3-diamine

The mol­ecule of the title compound, C6H6N4O4, is almost planar, being stabilized by two intra­molecular N—H⋯O hydrogen bonds. Further N—H⋯O links lead to a sheet in the crystal structure

Deterministic Generation of Entangled Photons in Superconducting Resonator Arrays

We present a scheme for the deterministic generation of entangled photon pairs in a superconducting resonator array. The resonators form a Jaynes-Cummings lattice via the coupling to superconducting qubits, and the Kerr-like nonlinearity arises due to the coupling.We show that entangled photons can be generated on demand by applying spectroscopic techniques and exploiting the nonlinearity and symmetry in the resonators. The scheme is robust against small parameter spreads due to fabrication errors. Our findings can be used as a key element for quantum information processing in superconducting quantum circuits.Comment: 4 pages, 3 figure

A synthesis method for cobalt doped carbon aerogels with high surface area and their hydrogen storage properties

Carbon aerogels doped with nanoscaled Co particles were prepared by first coating activated carbon aerogels using a wet-thin layer coating process. The resulting metal-doped carbon aerogels had a higher surface area (1667 m2 g-1) and larger micropore volume (0.6 cm3 g-1) than metal-doped carbon aerogels synthesised using other methods suggesting their usefulness in catalytic applications. The hydrogen adsorption behaviour of cobalt doped carbon aerogel was evaluated, displaying a high w4.38 wt.% H2 uptake under 4.6 MPa at -196 C. The hydrogen uptake capacity with respect to unit surface area was greater than for pure carbon aerogel and resulted in 1.3 H2 (wt. %) per 500 m2 g-1. However, the total hydrogen uptake was slightly reduced as compared to pure carbon aerogel due to a small reduction in surface area associated with cobalt doping. The improved adsorption per unit surface area suggests that there is a stronger interaction between the hydrogen molecules and the cobalt doped carbon aerogel than for pure carbon aerogel

Million-scale Object Detection with Large Vision Model

Over the past few years, there has been growing interest in developing a broad, universal, and general-purpose computer vision system. Such a system would have the potential to solve a wide range of vision tasks simultaneously, without being restricted to a specific problem or data domain. This is crucial for practical, real-world computer vision applications. In this study, we focus on the million-scale multi-domain universal object detection problem, which presents several challenges, including cross-dataset category label duplication, label conflicts, and the need to handle hierarchical taxonomies. Furthermore, there is an ongoing challenge in the field to find a resource-efficient way to leverage large pre-trained vision models for million-scale cross-dataset object detection. To address these challenges, we introduce our approach to label handling, hierarchy-aware loss design, and resource-efficient model training using a pre-trained large model. Our method was ranked second in the object detection track of the Robust Vision Challenge 2022 (RVC 2022). We hope that our detailed study will serve as a useful reference and alternative approach for similar problems in the computer vision community. The code is available at https://github.com/linfeng93/Large-UniDet.Comment: This paper is revised by ChatGP