Institution
Southwest University of Science and Technology
Education•Mianyang, China•
About: Southwest University of Science and Technology is a education organization based out in Mianyang, China. It is known for research contribution in the topics: Adsorption & Graphene. The organization has 10017 authors who have published 8923 publications receiving 94850 citations. The organization is also known as: Xīnán Kējìdàxué.
Topics: Adsorption, Graphene, Catalysis, Microstructure, Chemistry
Papers published on a yearly basis
Papers
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TL;DR: A novel method to exploit the latent discriminative features of low rank embedding by utilizing an orthogonal matrix to hold the main energy of the original data and introducing an $\ell _{2,1}$ -norm term to encourage the features to be more compact, discrim inative and interpretable.
Abstract: To defy the curse of dimensionality, the inputs are always projected from the original high-dimensional space into the target low-dimension space for feature extraction. However, due to the existence of noise and outliers, the feature extraction task for corrupted data is still a challenging problem. Recently, a robust method called low rank embedding (LRE) was proposed. Despite the success of LRE in experimental studies, it also has many disadvantages: 1) The learned projection cannot quantitatively interpret the importance of features. 2) LRE does not perform data reconstruction so that the features may not be capable of holding the main energy of the original “clean” data. 3) LRE explicitly transforms error into the target space. 4) LRE is an unsupervised method, which is only suitable for unsupervised scenarios. To address these problems, in this paper, we propose a novel method to exploit the latent discriminative features. In particular, we first utilize an orthogonal matrix to hold the main energy of the original data. Next, we introduce an $\ell _{2,1}$ -norm term to encourage the features to be more compact, discriminative and interpretable. Then, we enforce a columnwise $\ell _{2,1}$ -norm constraint on an error component to resist noise. Finally, we integrate a classification loss term into the objective function to fit supervised scenarios. Our method performs better than several state-of-the-art methods in terms of effectiveness and robustness, as demonstrated on six publicly available datasets.
59 citations
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TL;DR: The results indicate that the model considering the daily infections has the highest prediction accuracy and stability, and it is proved that the proposed model has great potential in real-world applications.
59 citations
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TL;DR: A new type of bioinspired ultrastrong, highly biocompatible, and bioactive konjac glucomannan/graphene oxide nanocomposite film is fabricated on a large scale by a simple solution-casting method, showing great potential in the fields of tissue engineering and food package.
Abstract: Tough and biocompatible nanocomposite films: A new type of bioinspired ultrastrong, highly biocompatible, and bioactive konjac glucomannan (KGM)/graphene oxide (GO) nanocomposite film is fabricated on a large scale by a simple solution-casting method. Such KGM-GO composite films exhibit much enhanced mechanical properties under the strong hydrogen-bonding interactions, showing great potential in the fields of tissue engineering and food package.
59 citations
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TL;DR: A novel oligomer (PFDCHQ) based on 9,10-dihydro-9-oxa-10-phosphaphenanthrene −10-oxide (DOPO) and ferrocene groups was synthesized successfully, aiming at improving the flame retardant efficiency of diglycidyl ether of bisphenol A epoxy resin (DGEBA) as mentioned in this paper.
59 citations
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TL;DR: Under irradiation of all kinds of light sources, the Au@Ca TiO3 composites, particularly the 4.3%Au@CaTiO3 composite, exhibit greatly enhanced photocatalytic performance when compared with bare CaTiO 3 NCs.
Abstract: Using P25 as the titanium source and based on a hydrothermal route, we have synthesized CaTiO3 nanocuboids (NCs) with the width of 0.3–0.5 μm and length of 0.8–1.1 μm, and systematically investigated their growth process. Au nanoparticles (NPs) of 3–7 nm in size were assembled on the surface of CaTiO3 NCs via a photocatalytic reduction method to achieve excellent Au@CaTiO3 composite photocatalysts. Various techniques were used to characterize the as-prepared samples, including X-ray powder diffraction (XRD), scanning/transmission electron microscopy (SEM/TEM), diffuse reflectance spectroscopy (UV-vis DRS), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). Rhodamine B (RhB) in aqueous solution was chosen as the model pollutant to assess the photocatalytic performance of the samples separately under simulated-sunlight, ultraviolet (UV) and visible-light irradiation. Under irradiation of all kinds of light sources, the Au@CaTiO3 composites, particularly the 4.3%Au@CaTiO3 composite, exhibit greatly enhanced photocatalytic performance when compared with bare CaTiO3 NCs. The main roles of Au NPs in the enhanced photocatalytic mechanism of the Au@CaTiO3 composites manifest in the following aspects: (1) Au NPs act as excellent electron sinks to capture the photoexcited electrons in CaTiO3, thus leading to an efficient separation of photoexcited electron/hole pairs in CaTiO3; (2) the electromagnetic field caused by localized surface plasmon resonance (LSPR) of Au NPs could facilitate the generation and separation of electron/hole pairs in CaTiO3; and (3) the LSPR-induced electrons in Au NPs could take part in the photocatalytic reactions.
59 citations
Authors
Showing all 10090 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Yi Yang | 143 | 2456 | 92268 |
Jian Zhou | 128 | 3007 | 91402 |
Wei Zhang | 104 | 2911 | 64923 |
Lei Wang | 95 | 1486 | 44636 |
Ray L. Frost | 86 | 1356 | 41053 |
Tao Chen | 86 | 820 | 27714 |
Yong Zhou | 84 | 688 | 29569 |
Yuan Hu | 83 | 747 | 27774 |
Xuemei Chen | 76 | 281 | 24252 |
Xiangxue Wang | 67 | 145 | 13052 |
Zhong-Ming Li | 66 | 489 | 17514 |
Ke Li | 62 | 654 | 15407 |
Hui Zhang | 58 | 717 | 14386 |
Ning Hu | 57 | 593 | 14125 |