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
Papers
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TL;DR: A gold nanoparticle-based detection strategy for ATP assays shows high resistance to salt-induced aggregation, and induces a duplex-to-aptamer structural switching, liberating a random coil-like ssDNA.
Abstract: A gold nanoparticle-based detection strategy for ATP assays. In the absence of ATP, gold nanoparticles are not stabilized by the rigid duplex, thus they are readily aggregated by salt (solution displaying blue colors); in contrast induces a duplex-to-aptamer structural switching, liberating a random coil-like ssDNA. Gold nanoparticles are stabilized by the liberated ssDNA, showing high resistance to salt-induced aggregation (solution staying in red).
395 citations
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TL;DR: The authors' van der Waals heterojunction photodetectors not only exemplify black arsenic phosphorus as a promising candidate for MIR optoelectronic applications but also pave the way for a general strategy to suppress 1/f noise in photonic devices.
Abstract: The mid-infrared (MIR) spectral range, pertaining to important applications, such as molecular "fingerprint" imaging, remote sensing, free space telecommunication, and optical radar, is of particular scientific interest and technological importance. However, state-of-the-art materials for MIR detection are limited by intrinsic noise and inconvenient fabrication processes, resulting in high-cost photodetectors requiring cryogenic operation. We report black arsenic phosphorus-based long-wavelength IR photodetectors, with room temperature operation up to 8.2 μm, entering the second MIR atmospheric transmission window. Combined with a van der Waals heterojunction, room temperature-specific detectivity higher than 4.9 × 109 Jones was obtained in the 3- to 5-μm range. The photodetector works in a zero-bias photovoltaic mode, enabling fast photoresponse and low dark noise. Our van der Waals heterojunction photodetectors not only exemplify black arsenic phosphorus as a promising candidate for MIR optoelectronic applications but also pave the way for a general strategy to suppress 1/f noise in photonic devices.
395 citations
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01 Dec 2005TL;DR: The results reveal that S-Isomap excels compared to Isomap and WeightedIso in classification, and it is highly competitive with those well-known classification methods.
Abstract: When performing visualization and classification, people often confront the problem of dimensionality reduction. Isomap is one of the most promising nonlinear dimensionality reduction techniques. However, when Isomap is applied to real-world data, it shows some limitations, such as being sensitive to noise. In this paper, an improved version of Isomap, namely S-Isomap, is proposed. S-Isomap utilizes class information to guide the procedure of nonlinear dimensionality reduction. Such a kind of procedure is called supervised nonlinear dimensionality reduction. In S-Isomap, the neighborhood graph of the input data is constructed according to a certain kind of dissimilarity between data points, which is specially designed to integrate the class information. The dissimilarity has several good properties which help to discover the true neighborhood of the data and, thus, makes S-Isomap a robust technique for both visualization and classification, especially for real-world problems. In the visualization experiments, S-Isomap is compared with Isomap, LLE, and WeightedIso. The results show that S-Isomap performs the best. In the classification experiments, S-Isomap is used as a preprocess of classification and compared with Isomap, WeightedIso, as well as some other well-established classification methods, including the K-nearest neighbor classifier, BP neural network, J4.8 decision tree, and SVM. The results reveal that S-Isomap excels compared to Isomap and WeightedIso in classification, and it is highly competitive with those well-known classification methods.
394 citations
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TL;DR: The polarization of the ferroelectric BiFeO3 subjected to different electrical boundary conditions by heterointerfaces was imaged with atomic resolution using a spherical aberration-corrected transceiver as discussed by the authors.
Abstract: The polarization of the ferroelectric BiFeO3 sub-jected to different electrical boundary conditions by heterointerfaces is imaged with atomic resolution using a spherical aberration-corrected trans...
393 citations
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01 Oct 2014TL;DR: A novel saliency method that works on depth images based on anisotropic center-surround difference is proposed, which measures the saliency of a point by how much it outstands from surroundings, which takes the global depth structure into consideration.
Abstract: Most previous works on saliency detection are dedicated to 2D images. Recently it has been shown that 3D visual information supplies a powerful cue for saliency analysis. In this paper, we propose a novel saliency method that works on depth images based on anisotropic center-surround difference. Instead of depending on absolute depth, we measure the saliency of a point by how much it outstands from surroundings, which takes the global depth structure into consideration. Besides, two common priors based on depth and location are used for refinement. The proposed method works within a complexity of O(N) and the evaluation on a dataset of over 1000 stereo images shows that our method outperforms state-of-the-art.
392 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 |