Intertwined Dirac cones induced by anisotropic coupling in antiferromagnetic topological insulator

Antiferromagnetic topological insulators (AFM TIs), which host magnetically gapped Dirac-cone surface states and exhibit many exotic physical phenomena, have attracted great attention. The coupling between the top and bottom surface states becomes significant and plays a crucial role in its low-energy physics, as the thickness of an AFM TI film decreases. Here, we find that the coupled surface states can be intertwined to give birth to a set of $2n$ brand new Dirac cones, dubbed \emph{intertwined Dirac cones}, through the anisotropic coupling due to the $n$-fold crystalline rotation symmetry $C_{nz}$ ($n=2, 3, 4, 6$) in the presence of an out-of-plane electric field. Interestingly, we also find that the warping effect further drives the intertwined Dirac-cone state into a quantum anomalous Hall phase with a high Chern number ($C=n$). Then, we demonstrate the emergent six intertwined Dirac cones and the corresponding Chern insulating phase with a high Chern number ($C=3$) in MnBi$_2$Te$_4$$/$(Bi$_2$Te$_3$)$_{\mathrm{m}}/$MnBi$_2$Te$_4$ heterostructures through first-principles calculations. This work discovers a new intertwined Dirac-cone state in AFM TI thin films and also reveals a new mechanism for designing the quantum anomalous Hall state with a high Chern number.Comment: 7 pages, 4 figures+supplemental material

Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

In recent decades, land cover change detection (LCCD) using very high-spatial resolution (VHR) remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM) post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing) techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution) which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution) and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution), respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors’ knowledge, this type of post-processing framework is first proposed here for the field of LCCD using VHR remote sensing images.This work was supported by the National Science Foundation China (61701396 and D010701), the Science Foundation of Hunan Province (Grant No. 2016JJ6100), the Natural Science Foundation of Shaan Xi Province (2017JQ4006), and the project from the China Postdoctoral Science Foundation (2015M572658XB).Peer Reviewe

Ultrathin Ta2O5 electron-selective contacts for high efficiency InP solar cells

Heterojunction solar cells with transition-metal-oxide-based carrier-selective contacts have been gaining considerable research interest owing to their amenability to low-cost fabrication methods and elimination of parasitic absorption and complex semiconductor doping process. In this work, we propose tantalum oxide (Ta2O5) as a novel electron-selective contact layer for photo-generated carrier separation in InP solar cells. We confirm the electron-selective properties of Ta2O5 by investigating band energetics at the InP-Ta2O5 interface using X-ray photoelectron spectroscopy. Time-resolved photoluminescence and power dependent photoluminescence reveal that the Ta2O5 inter-layer also mitigates parasitic recombination at the InP/transparent conducting oxide interface. With an 8 nm Ta2O5 layer deposited using an atomic layer deposition (ALD) system, we demonstrate a planar InP solar cell with an open circuit voltage, Voc, of 822 mV, a short circuit current density, Jsc, of 30.1 mA/cm2, and a fill factor of 0.77, resulting in an overall device efficiency of 19.1%. The Voc is the highest reported value to date for an InP heterojunction solar cells with carrier-selective contacts. The proposed Ta2O5 material may be of interest not only for other solar cell architectures including perovskite cells and organic solar cells, but also across a wide range of optoelectronics applications including solid state emitting devices, photonic crystals, planar light wave circuits etc

In situ recombination junction between p-Si and TiO2 enables high-efficiency monolithic perovskite/Si tandem cells

Increasing the power conversion efficiency of silicon (Si) photovoltaics is a key enabler for continued reductions in the cost of solar electricity. Here, we describe a two-terminal perovskite/Si tandem design that increases the Si cell’s output in the simplest possible manner: by placing a perovskite cell directly on top of the Si bottom cell. The advantageous omission of a conventional interlayer eliminates both optical losses and processing steps and is enabled by the low contact resistivity attainable between n-type TiO2 and Si, established here using atomic layer deposition. We fabricated proof-of-concept perovskite/Si tandems on both homojunction and passivating contact heterojunction Si cells to demonstrate the broad applicability of the interlayer-free concept. Stabilized efficiencies of 22.9 and 24.1% were obtained for the homojunction and passivating contact heterojunction tandems, respectively, which could be readily improved by reducing optical losses elsewhere in the device. This work highlights the potential of emerging perovskite photovoltaics to enable low-cost, high-efficiency tandem devices through straightforward integration with commercially relevant Si solar cells

Interface passivation using ultrathin polymer–fullerene films for high-efficiency perovskite solar cells with negligible hysteresis

Interfacial carrier recombination is one of the dominant loss mechanisms in high efficiency perovskite solar cells, and has also been linked to hysteresis and slow transient responses in these cells. Here we demonstrate an ultrathin passivation layer consisting of a PMMA:PCBM mixture that can effectively passivate defects at or near to the perovskite/TiO2 interface, significantly suppressing interfacial recombination. The passivation layer increases the open circuit voltage of mixed-cation perovskite cells by as much as 80 mV, with champion cells achieving Voc ∼ 1.18 V. As a result, we obtain efficient and stable perovskite solar cells with a steady-state PCE of 20.4% and negligible hysteresis over a large range of scan rates. In addition, we show that the passivated cells exhibit very fast current and voltage response times of less than 3 s under cyclic illumination. This new passivation approach addresses one of the key limitations of current perovskite cells, and paves the way to further efficiency gains through interface engineering.Australian Renewable Energy Agency; Australian Research Council; MSTC (Grant No. 2016YFA0301300), NNSFC (Grant No. 11674402) and GSTP (Grant No. 201607010044, 201607020023

A Framework for Spatiotemporal Analysis of Regional Economic Agglomeration Patterns

Understanding regional economic agglomeration patterns is critical for sustainable economic development, urban planning and proper utilization of regional resources. Taking Guangdong Province of China as the study area, this paper introduces a comprehensive research framework for analyzing regional economic agglomeration patterns and understanding their spatiotemporal characteristics. First, convergence and autocorrelation methods are applied to understand the economic spatial patterns. Then, the intercity spatial interaction model (ISIM) is proposed to measure the strength of interplay among cities, and social network analysis (SNA) based on the ISIM is utilized, which is designed to reveal the network characteristics of economic agglomerations. Finally, we perform a spatial panel data analysis to comprehensively interpret the influences of regional economic agglomerations. The results indicate that from 2001 to 2016, the economy in Guangdong showed a double-core/peripheral pattern of convergence, with strengthened intercity interactions. The strength and external spillover effects of Guangzhou and Shenzhen enhanced, while Foshan and Dongguan had relatively strong absorptive abilities. Moreover, expanding regional communication and cooperation is key to enhancing vigorous economic agglomerations and regional network ties in Guangdong by spatial panel data analysis. Our results show that this is a suitable method of reflecting regional economic agglomeration process and its spatiotemporal pattern

An Object-Oriented Deep Multi-Sphere Support Vector Data Description Method for Impervious Surfaces Extraction Based on Multi-Sourced Data

The effective extraction of impervious surfaces is critical to monitor their expansion and ensure the sustainable development of cities. Open geographic data can provide a large number of training samples for machine learning methods based on remote-sensed images to extract impervious surfaces due to their advantages of low acquisition cost and large coverage. However, training samples generated from open geographic data suffer from severe sample imbalance. Although one-class methods can effectively extract an impervious surface based on imbalanced samples, most of the current one-class methods ignore the fact that an impervious surface comprises varied geographic objects, such as roads and buildings. Therefore, this paper proposes an object-oriented deep multi-sphere support vector data description (OODMSVDD) method, which takes into account the diversity of impervious surfaces and incorporates a variety of open geographic data involving OpenStreetMap (OSM), Points of Interest (POIs), and trajectory GPS points to automatically generate massive samples for model learning, thereby improving the extraction of impervious surfaces with varied types. The feasibility of the proposed method is experimentally verified with an overall accuracy of 87.43%, and its superior impervious surface classification performance is shown via comparative experiments. This provides a new, accurate, and more suitable extraction method for complex impervious surfaces

Integrating Gaussian Mixture Dual-Clustering and DBSCAN for Exploring Heterogeneous Characteristics of Urban Spatial Agglomeration Areas

Exploring the heterogeneous characteristics of the urban expansion process is essential for understanding the dynamics of the urban spatial structure. Many studies focused on depicting the spatio-temporal characteristics based on urban expansion patches. However, measuring heterogeneous characteristics of urban expansion from agglomeration areas comprising the expanded urban construction land patches have not been adequately explored. This study presents a novel approach and two improved indices for characterizing the heterogeneity of urban spatial agglomeration areas during urban expansion. Firstly, we proposed a Gaussian mixture model considering multiple constrains and density-based spatial clustering of applications with noise (DBSCAN) integration method to identify and extract the urban agglomeration areas automatically. Secondly, the gradient analysis and the compact index using the inverse “S” function are introduced to explore the spatio-temporal characteristics from a macrocosmic perspective. Finally, the compactness index (NCI) and normalized dispersion index (NDIS) are improved based on agglomeration area data. The microcosmic heterogeneous characteristics are measured by these two improved indices and the positional offset characteristics indices (POCIS). The method was implemented in the urban area of Changsha, Hunan Province, China in 2005, 2010, and 2015. The results show that (1) compared to that in the Changsha City Master Plan (2003–2020), the recognition rate was higher in the agglomeration areas than others. (2) The overall expansion trend in Changsha transitioned toward decentralization, making Changsha a polycentric city. (3) The agglomeration of urban expansion in the east-west direction became compact; that in the north-south direction became looser; most clusters expanded to the west and a new sub-center would appear. The proposed method can effectively characterize their heterogeneity, which can provide valuable references for urban planning and policymaking

Use of GMM and SCMS for Accurate Road Centerline Extraction from the Classified Image

The extraction of road centerline from the classified image is a fundamental image analysis technology. Common problems encountered in road centerline extraction include low ability for coping with the general case, production of undesired objects, and inefficiency. To tackle these limitations, this paper presents a novel accurate centerline extraction method using Gaussian mixture model (GMM) and subspace constraint mean shift (SCMS). The proposed method consists of three main steps. GMM is first used to partition the classified image into several clusters. The major axis of the ellipsoid of each cluster is extracted and deemed to be taken as the initial centerline. Finally, the initial result is adjusted using SCMS to produce precise road centerline. Both simulated and real datasets are used to validate the proposed method. Preliminary results demonstrate that the proposed method provides a comparatively robust solution for accurate centerline extraction from a classified image