Wuhan University

About: Wuhan University is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Computer science & Population. The organization has 92849 authors who have published 92882 publications receiving 1691049 citations. The organization is also known as: WHU & Wuhan College.

Efficient hole-blocking layer-free planar halide perovskite thin-film solar cells.

Abstract: Lead halide perovskite solar cells use hole-blocking layers to allow a separate collection of positive and negative charge carriers and to achieve high-operation voltages. Here, the authors demonstrate efficient lead halide perovskite solar cells that avoid using this extra layer.

Sandwich-type functionalized graphene sheet-sulfur nanocomposite for rechargeable lithium batteries

Abstract: A functionalized graphene sheet-sulfur (FGSS) nanocomposite was synthesized as the cathode material for lithium–sulfur batteries. The structure has a layer of functionalized graphene sheets/stacks (FGS) and a layer of sulfur nanoparticles creating a three-dimensional sandwich-type architecture. This unique FGSS nanoscale layered composite has a high loading (70 wt%) of active material (S), a high tap density of ∼0.92 g cm−3, and a reversible capacity of ∼505 mAh g−1 (∼464 mAh cm−3) at a current density of 1680 mA g−1 (1C). When coated with a thin layer of cation exchange Nafion film, the migration of dissolved polysulfide anions from the FGSS nanocomposite was effectively reduced, leading to a good cycling stability of 75% capacity retention over 100 cycles. This sandwich-structured composite conceptually provides a new strategy for designing electrodes in energy storage applications.

Unsupervised Spectral–Spatial Feature Learning With Stacked Sparse Autoencoder for Hyperspectral Imagery Classification

Abstract: In this letter, different from traditional methods using original spectral features or handcraft spectral–spatial features, we propose to adaptively learn a suitable feature representation from unlabeled data. This is achieved by learning a feature mapping function based on stacked sparse autoencoder. Considering that hyperspectral imagery (HSI) is intrinsically defined in both the spectral and spatial domains, we further establish two variants of feature learning procedures for sparse spectral feature learning and multiscale spatial feature learning. Finally, we embed the learned spectral–spatial feature into a linear support vector machine for classification. Experiments on two hyperspectral images indicate the following: 1) the learned spectral–spatial feature representation is more discriminative for HSI classification compared to previously hand-engineered spectral–spatial features, especially when the training data are limited and 2) the learned features appear not to be specific to a particular image but general in that they are applicable to multiple related images (e.g., images acquired by the same sensor but varying with location or time).