Polarization-Sensitive Self-Powered Schottky Photodetector with High Photovoltaic Performance Induced by Geometry-Asymmetric Contacts.

Polarization-sensitive photodetectors have attracted considerable attention owing to their potential application prospects in navigation, optical switching, and communication. However, it remains challenging to develop a facile and effective strategy to simultaneously meet the demands of low power consumption, high performance, and excellent polarization sensitivity. Herein, a series of low-symmetry two-dimensional (2D) ReSe2 Schottky photodetectors with geometry-asymmetric contacts are constructed. These devices exhibit excellent photoelectrical performance and impressive polarization sensitivity in the self-powered mode owing to the difference in the Schottky barrier height induced by the asymmetric contact areas, interfacial states, and thickness difference. Particularly, an outstanding responsivity of 379 mA/W, a decent specific detectivity of 6.8 × 1011 Jones, and a high light on/off ratio (Ilight/Idark) of over 105 under 635 nm light illumination are achieved. Scanning photocurrent mapping (SPCM) measurements further confirm that the ReSe2/drain overlapped region (corresponding to the smaller contact area side) with a higher Schottky barrier height plays a dominant role in the generation of photocurrent. Furthermore, the proposed device displays impressive polarization ratios (PRs) of 3.1 and 3.6 at zero bias under 635 and 808 nm irradiation, respectively. The high-resolution single-pixel imaging capability is also demonstrated. This work reveals the great potential of the ReSe2 Schottky photodetector with geometry-asymmetric contacts for high-performance, self-powered, and polarization-sensitive photodetection.

Knowledge-aware Multisite Adaptive Graph Transformer for Brain Disorder Diagnosis.

Brain disorder diagnosis via resting-state functional magnetic resonance imaging (rs-fMRI) is usually limited due to the complex imaging features and sample size. For brain disorder diagnosis, the graph convolutional network (GCN) has achieved remarkable success by capturing interactions between individuals and the population. However, there are mainly three limitations: 1) The previous GCN approaches consider the non-imaging information in edge construction but ignore the sensitivity differences of features to non-imaging information. 2) The previous GCN approaches solely focus on establishing interactions between subjects (i.e., individuals and the population), disregarding the essential relationship between features. 3) Multisite data increase the sample size to help classifier training, but the inter-site heterogeneity limits the performance to some extent. This paper proposes a knowledge-aware multisite adaptive graph Transformer to address the above problems. First, we evaluate the sensitivity of features to each piece of non-imaging information, and then construct feature-sensitive and feature-insensitive subgraphs. Second, after fusing the above subgraphs, we integrate a Transformer module to capture the intrinsic relationship between features. Third, we design a domain adaptive GCN using multiple loss function terms to relieve data heterogeneity and to produce the final classification results. Last, the proposed framework is validated on two brain disorder diagnostic tasks. Experimental results show that the proposed framework can achieve state-of-the-art performance.

Negative Magnetoresistance in the GeSn Strip.

Discovery of topological materials associated with an exotic phenomenon has attracted increasing attention in modern condensed matter physics. A typical example is the chiral anomaly proposed in the Dirac or Weyl semimetals. In addition to the well-known topological semimetals, such as TaAs and Na3Bi, recently, group IV GeSn alloys were also proposed to be Dirac semimetals in theory, demonstrating potential applications compatible with current Si-based technology. Here, we report the observation of large negative magnetoresistance (MR) that is sensitive to the orientation of the magnetic and electric field in the GeSn strip. This negative MR emerges only when the applied magnetic field is parallel to the electric field, which is consistent with the chiral anomaly in topological semimetals. This work paves a new way toward exploring the negative MR behavior and underlying mechanism in a new class of Dirac semimetals.