Institution
Nanjing University
Education•Nanjing, China•
About: Nanjing University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 85961 authors who have published 105504 publications receiving 2289036 citations. The organization is also known as: NJU & Nanking University.
Topics: Catalysis, Population, Adsorption, Magnetization, Graphene
Papers published on a yearly basis
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TL;DR: In this article, a kind of spherical cellulose nanocrystals was prepared by hydrolysis of microcrystalline cellulose with mixed acid and two methods were used: diminishing the acid sulfate groups by desulfation and neutralizing them by using NaOH solution.
554 citations
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TL;DR: It is found that the ORR activity of PmPDA-FeN(x)/C is not sensitive to CO and NO(x) but can be suppressed significantly by halide ions and low valence state sulfur-containing species in acid medium.
Abstract: High-temperature pyrolyzed FeNx/C catalyst is one of the most promising nonprecious metal electrocatalysts for oxygen reduction reaction (ORR). However, it suffers from two challenging problems: insufficient ORR activity and unclear active site structure. Herein, we report a FeNx/C catalyst derived from poly-m-phenylenediamine (PmPDA-FeNx/C) that possesses high ORR activity (11.5 A g–1 at 0.80 V vs RHE) and low H2O2 yield (<1%) in acid medium. The PmPDA-FeNx/C also exhibits high catalytic activity for both reduction and oxidation of H2O2. We further find that the ORR activity of PmPDA-FeNx/C is not sensitive to CO and NOx but can be suppressed significantly by halide ions (e.g., Cl–, F–, and Br–) and low valence state sulfur-containing species (e.g., SCN–, SO2, and H2S). This result reveals that the active sites of the FeNx/C catalyst contains Fe element (mainly as FeIII at high potentials) in acid medium.
552 citations
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TL;DR: In this article, the microscopic mechanisms of interface interaction, charge transfer and separation, as well as the influence on the photocatalytic activity of g-C3N4/NaNbO3 composite were systematic investigated.
Abstract: Visible-light-responsive g-C3N4/NaNbO3 nanowires photocatalysts were fabricated by introducing polymeric g-C3N4 on NaNbO3 nanowires. The microscopic mechanisms of interface interaction, charge transfer and separation, as well as the influence on the photocatalytic activity of g-C3N4/NaNbO3 composite were systematic investigated. The high-resolution transmission electron microscopy (HR-TEM) revealed that an intimate interface between C3N4 and NaNbO3 nanowires formed in the g-C3N4/NaNbO3 heterojunctions. The photocatalytic performance of photocatalysts was evaluated for CO2 reduction under visible-light illumination. Significantly, the activity of g-C3N4/NaNbO3 composite photocatalyst for photoreduction of CO2 was higher than that of either single-phase g-C3N4 or NaNbO3. Such a remarkable enhancement of photocatalytic activity was mainly ascribed to the improved separation and transfer of photogenerated electron–hole pairs at the intimate interface of g-C3N4/NaNbO3 heterojunctions, which originated from the...
551 citations
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27 Jul 2014TL;DR: A novel SMH method, called semantic correlation maximization (SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling, and experimental results show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability.
Abstract: Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing (SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method, called semantic correlation maximization (SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability.
550 citations
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TL;DR: A series of milestones and steady progress in the past decade have enabled our understanding of multiferroic physics substantially comprehensive and profound, which is further pushing forward the research frontier of this exciting area.
Abstract: Multiferroics are those materials with more than one ferroic order, and magnetoelectricity refers to the mutual coupling between magnetism (spins and/or magnetic field) and electricity (electric dipoles and/or electric field). In spite of the long research history in the whole twentieth century, the discipline of multiferroicity has never been so highly active as that in the first decade of the twenty-first century, and it has become one of the hottest disciplines of condensed matter physics and materials science. A series of milestones and steady progress in the past decade have enabled our understanding of multiferroic physics substantially comprehensive and profound, which is further pushing forward the research frontier of this exciting area. The availability of more multiferroic materials and improved magnetoelectric performance are approaching to make the applications within reach. While seminal review articles covering the major progress before 2010 are available, an updated review addressing the n...
549 citations
Authors
Showing all 86514 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yi Chen | 217 | 4342 | 293080 |
H. S. Chen | 179 | 2401 | 178529 |
Zhenan Bao | 169 | 865 | 106571 |
Gang Chen | 167 | 3372 | 149819 |
Peter G. Schultz | 156 | 893 | 89716 |
Xiang Zhang | 154 | 1733 | 117576 |
Rui Zhang | 151 | 2625 | 107917 |
Yi Yang | 143 | 2456 | 92268 |
Markku Kulmala | 142 | 1487 | 85179 |
Jian Yang | 142 | 1818 | 111166 |
Wei Huang | 139 | 2417 | 93522 |
Bin Liu | 138 | 2181 | 87085 |
Jun Lu | 135 | 1526 | 99767 |
Hui Li | 135 | 2982 | 105903 |
Lei Zhang | 135 | 2240 | 99365 |