Institution
University of Science and Technology Beijing
Education•Beijing, China•
About: University of Science and Technology Beijing is a education organization based out in Beijing, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 41558 authors who have published 44473 publications receiving 623229 citations. The organization is also known as: Beijing Steel and Iron Institute.
Topics: Microstructure, Alloy, Corrosion, Ultimate tensile strength, Austenite
Papers published on a yearly basis
Papers
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TL;DR: In this article, a p-type Cu2O/n-type TaON heterojunction nanorod array passivated with an ultrathin carbon sheath was produced via a solution-based process.
Abstract: Considerable efforts have been made to design and discover photoactive nanostructured (oxy)nitride materials that can be used as photoanodes for photoelectrochemical (PEC) water splitting. However, the high recombination rate of photoexcited electron–hole pairs and the poor photostability have greatly limited their practical applications. Herein, a p-type Cu2O/n-type TaON heterojunction nanorod array passivated with an ultrathin carbon sheath (carbon–Cu2O/TaON) as a surface protection layer was produced via a solution-based process. Due to the shape anisotropy and p–n heterojunction structure, the photocurrent density of carbon–Cu2O/TaON heterojunction nanorod arrays as the integrated photoanode, with a maximum IPCE of 59% at a wavelength of 400 nm, reached 3.06 mA cm−2 under AM 1.5G simulated sunlight at 1.0 V vs. RHE and remained at about 87.3% of the initial activity after 60 min irradiation. Not only is the onset potential negatively shifted but the photocurrent density and photostability are also significantly improved for this photoanode compared to those of TaON and Cu2O/TaON. These improvements are due to a high built-in potential in the p–n heterojunction device that is protected from the electrolyte by being encapsulated in an ultrathin graphitic carbon sheath. Our design introduces material components to provide a dedicated charge-transport pathway, alleviating the reliance on the materials' intrinsic properties, and therefore has the potential to greatly broaden where and how various existing materials can be used in energy-related applications.
163 citations
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163 citations
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TL;DR: This review introduces the reader to the overall framework of smart gas sensing technology, including three key points; gas sensor arrays made of different materials, signal processing for drift compensation and feature extraction, and gas pattern recognition including Support Vector Machine (SVM), Artificial Neural Network (ANN), and other techniques.
Abstract: With the development of the Internet-of-Things (IoT) technology, the applications of gas sensors in the fields of smart homes, wearable devices, and smart mobile terminals have developed by leaps and bounds. In such complex sensing scenarios, the gas sensor shows the defects of cross sensitivity and low selectivity. Therefore, smart gas sensing methods have been proposed to address these issues by adding sensor arrays, signal processing, and machine learning techniques to traditional gas sensing technologies. This review introduces the reader to the overall framework of smart gas sensing technology, including three key points; gas sensor arrays made of different materials, signal processing for drift compensation and feature extraction, and gas pattern recognition including Support Vector Machine (SVM), Artificial Neural Network (ANN), and other techniques. The implementation, evaluation, and comparison of the proposed solutions in each step have been summarized covering most of the relevant recently published studies. This review also highlights the challenges facing smart gas sensing technology represented by repeatability and reusability, circuit integration and miniaturization, and real-time sensing. Besides, the proposed solutions, which show the future directions of smart gas sensing, are explored. Finally, the recommendations for smart gas sensing based on brain-like sensing are provided in this paper.
163 citations
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TL;DR: In this article, major and trace element compositions, and O-C isotope data for Cenozoic carbonatites (WSC) in western Sichuan, east Tibet, China are presented.
163 citations
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TL;DR: In this article, the authors established relationships between rare-earth ions doping and intrinsic emission of lead-free double perovskite Cs2 AgInCl6 NCs to impart and tune the optical performances in the visible light region.
Abstract: The incorporation of impurity ions or doping is a promising method for controlling the electronic and optical properties and the structural stability of halide perovskite nanocrystals (NCs). Herein, we establish relationships between rare-earth ions doping and intrinsic emission of lead-free double perovskite Cs2 AgInCl6 NCs to impart and tune the optical performances in the visible light region. Tb3+ ions were incorporated into Cs2 AgInCl6 NCs and occupied In3+ sites as verified by both crystallographic analyses and first-principles calculations. Trace amounts of Bi doping endowed the characteristic emission (5 D4 →7 F6-3 ) of Tb3+ ions with a new excitation peak at 368 nm rather than the single characteristic excitation at 290 nm of Tb3+ . By controlling Tb3+ ions concentration, the emission colors of Bi-doped Cs2 Ag(In1-x Tbx )Cl6 NCs could be continuously tuned from green to orange, through the efficient energy-transfer channel from self-trapped excitons to Tb3+ ions. Our study provides the salient features of the material design of lead-free perovskite NCs and to expand their luminescence applications.
163 citations
Authors
Showing all 41904 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Yang Yang | 171 | 2644 | 153049 |
Jun Chen | 136 | 1856 | 77368 |
Jun Lu | 135 | 1526 | 99767 |
Jie Liu | 131 | 1531 | 68891 |
Shuai Liu | 129 | 1095 | 80823 |
Jian Zhou | 128 | 3007 | 91402 |
Chao Zhang | 127 | 3119 | 84711 |
Shaobin Wang | 126 | 872 | 52463 |
Tao Zhang | 123 | 2772 | 83866 |
Jian Liu | 117 | 2090 | 73156 |
Xin Li | 114 | 2778 | 71389 |
Jianhui Hou | 110 | 429 | 53265 |
Hong Wang | 110 | 1633 | 51811 |
Baoshan Xing | 109 | 823 | 48944 |