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The Weyl semimetal has emerged as a new topologically nontrivial phase of matter, hosting low-energy excitations of massless Weyl fermions. Here, we present a comprehensive study of a type-II Weyl semimetal WP2. Transport studies show a butterfly-like magnetoresistance at low temperature, reflecting the anisotropy of the electron Fermi surfaces. This four-lobed feature gradually evolves into a two-lobed variant with an increase in temperature, mainly due to the reduced relative contribution of electron Fermi surfaces compared to hole Fermi surfaces for magnetoresistance. Moreover, an angle-dependent Berry phase is also discovered, based on quantum oscillations, which is ascribed to the effective manipulation of extremal Fermi orbits by the magnetic field to feel nearby topological singularities in the momentum space. The revealed topological character and anisotropic Fermi surfaces of the WP2 substantially enrich the physical properties of Weyl semimetals, and show great promises in terms of potential topological electronic and Fermitronic device applications. [ABSTRACT FROM AUTHOR]

The Weyl semimetal has emerged as a new topologically nontrivial phase of matter, hosting low-energy excitations of massless Weyl fermions. Here, we present a comprehensive study of a type-II Weyl semimetal WP2. Transport studies show a butterfly-like magnetoresistance at low temperature, reflecting the anisotropy of the electron Fermi surfaces. This four-lobed feature gradually evolves into a two-lobed variant with an increase in temperature, mainly due to the reduced relative contribution of electron Fermi surfaces compared to hole Fermi surfaces for magnetoresistance. Moreover, an angle-dependent Berry phase is also discovered, based on quantum oscillations, which is ascribed to the effective manipulation of extremal Fermi orbits by the magnetic field to feel nearby topological singularities in the momentum space. The revealed topological character and anisotropic Fermi surfaces of the WP2 substantially enrich the physical properties of Weyl semimetals, and show great promises in terms of potential topological electronic and Fermitronic device applications. [ABSTRACT FROM AUTHOR]

Large‐scale production of hydrogen from water‐alkali electrolyzers is impeded by the sluggish kinetics of hydrogen evolution reaction (HER) electrocatalysts. The hybridization of an acid‐active HER catalyst with a cocatalyst at the nanoscale helps boost HER kinetics in alkaline media. Here, it is demonstrated that 1T–MoS2 nanosheet edges (instead of basal planes) decorated by metal hydroxides form highly active edge1T‐MoS2/edgeNi(OH)2 heterostructures, which significantly enhance HER performance in alkaline media. Featured with rich edge1T‐MoS2/edgeNi(OH)2 sites, the fabricated 1T–MoS2 QS/Ni(OH)2 hybrid (quantum sized 1T–MoS2 sheets decorated with Ni(OH)2 via interface engineering) only requires overpotentials of 57 and 112 mV to drive HER current densities of 10 and 100 mA cm−2, respectively, and has a low Tafel slope of 30 mV dec−1 in 1 m KOH. So far, this is the best performance for MoS2‐based electrocatalysts and the 1T–MoS2 QS/Ni(OH)2 hybrid is among the best‐performing non‐Pt alkaline HER electrocatalysts known. The HER process is durable for 100 h at current densities up to 500 mA cm−2. This work not only provides an active, cost‐effective, and robust alkaline HER electrocatalyst, but also demonstrates a design strategy for preparing high‐performance catalysts based on edge‐rich 2D quantum sheets for other catalytic reactions. [ABSTRACT FROM AUTHOR]

Designing half-metallic ferromagnetism at atomic scale is always one of the hottest topics for the scientific community due to its potential in spintronics applications. Although the band alignment strategy is broadly adopted in semiconductor-based research, it has not touched the field of low-dimensional spintronics. Here, taking graphitic C3N4 (g-C3N4) as an example, we show that perfect half-metallic ferromagnetism can be realized through a band alignment strategy. According to the alignment of the valence band maximum (VBM) and the conduction band minimum (CBM) of g-C3N4, and the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) of superhalogens, BF4 is predicted to be a promising building unit which induces half-metallic ferromagnetism in the g-C3N4 sheet by forming hybrid structures. Verified by density functional simulations, we found that a net magnetic moment of 1 μB per BF4 is induced in the g-C3N4 sheet. The magnetic moment distributes evenly on the double-coordinated nitrogen atoms with ferromagnetic coupling, and dynamics simulations confirmed that the structure of g-C3N4 is well protected. Thus, our study proposes a promising way to design half-metallic ferromagnetism at atomic scale, which will evoke further experimental interest. [ABSTRACT FROM AUTHOR]

• By 2020, China will have a stock of 2 billion used mobile phones., but only 2% of these will have been recycled through formal channels. • Automatic color recognition of used and end-of-life mobile phones is essential to enable accurate and rapid recycling. • This study establishes a high-dimensional spatial color conversion deep convolutional neural network (HSCCNet) for mobile phone color recognition, which has a significant impact on the recycling of used mobile phones. Rapid development of telecommunication technology in China has led to a prosperous market of smart phones, as well as an increase number of used phones. Nevertheless, there are key factors affecting the used phone recycling, one of which is the phone color. To realize an accurate automatic color recognition of used phones to enhance the recycling process, a high-dimensional spatial color conversion deep convolutional neural network (HSCCNet) is proposed in this paper. First, we established a common dataset for the field of used electronic devices. Second, the phone color is converted to the high-dimensional space of hue, saturation and value (HSV), which generates richer expressions of color features and improves the model sensitivity. Finally, a deep convolutional structure for HSV features is designed, where color feature conversion are implemented, resulting in enhanced color feature expressions. Promising results are obtained through the comparison between the proposed HSCCNet and the state-of-the-art models. [Display omitted] [ABSTRACT FROM AUTHOR]

Sequential recommendation becomes a critical task in many application scenarios, since people's online activities are increasing. In order to predict the next item that users may be interested, it is necessary to take both general and dynamic preferences of the user into account. Existing approaches typically integrate the user–item or item–item feature interactions directly without considering the dynamic changes of the user's long-term and short-term preferences, which also limits the capability of the model. To address these issues, we propose a novel unified framework for sequential recommendation task, modeling users' long and short-term sequential behaviors at each time step and capturing item-to-item dependencies in higher-order by hierarchical attention mechanism. The proposed model considers the dynamic long and short-term user preferences simultaneously, and a joint learning mechanism is introduced to fuse them for better recommendation. We extensively evaluate our model with several state-of-the-art methods by different validation metrics on three real-world datasets. The experimental results demonstrate the significant improvement of our approach over other compared models. • A novel unified framework for sequential recommendation is proposed. • Multi-head self-attention captures both the general and dynamic user preferences. • Hierarchical attention obtains the item–item interaction features in higher order. • A joint learning mechanism fuses the long and short-term features effectively. • The proposed model achieves significant improvement over other related method. [ABSTRACT FROM AUTHOR]