scispace - formally typeset
Search or ask a question
Institution

Nankai University

EducationTianjin, China
About: Nankai University is a education organization based out in Tianjin, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 42964 authors who have published 51866 publications receiving 1127896 citations. The organization is also known as: Nánkāi Dàxué.


Papers
More filters
Journal ArticleDOI
TL;DR: Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties, and complement source models that estimate concentrations from emissions inventories and transport meteorology.

286 citations

Proceedings Article
21 Aug 2003
TL;DR: An "Iterative Feature Selection (IF)" method is proposed that addresses the unavailability of label problem by utilizing effective supervised feature selection method to iteratively select features and perform clustering.
Abstract: Feature selection methods have been successfully applied to text categorization but seldom applied to text clustering due to the unavailability of class label information. In this paper, we first give empirical evidence that feature selection methods can improve the efficiency and performance of text clustering algorithm. Then we propose a new feature selection method called "Term Contribution (TC)" and perform a comparative study on a variety of feature selection methods for text clustering, including Document Frequency (DF), Term Strength (TS), Entropy-based (En), Information Gain (IG) and χ2 statistic (CHI). Finally, we propose an "Iterative Feature Selection (IF)" method that addresses the unavailability of label problem by utilizing effective supervised feature selection method to iteratively select features and perform clustering. Detailed experimental results on Web Directory data are provided in the paper.

286 citations

Journal ArticleDOI
TL;DR: A facile method is demonstrated to engineer the eg filling of perovskite cobaltite LaCoO3 for improving the oxygen evolution reaction activity, comparable to those of recently reported cobalt oxides with eg∼1.2 configurations.
Abstract: The activity of electrocatalysts exhibits a strongly dependence on their electronic structures. Specifically, for perovskite oxides, Shao-Horn and co-workers have reported a correlation between the oxygen evolution reaction activity and the eg orbital occupation of transition-metal ions, which provides guidelines for the design of highly active catalysts. Here we demonstrate a facile method to engineer the eg filling of perovskite cobaltite LaCoO3 for improving the oxygen evolution reaction activity. By reducing the particle size to ∼80 nm, the eg filling of cobalt ions is successfully increased from unity to near the optimal configuration of 1.2 expected by Shao-Horn’s principle. Consequently, the activity is significantly enhanced, comparable to those of recently reported cobalt oxides with eg∼1.2 configurations. This enhancement is ascribed to the emergence of spin-state transition from low-spin to high-spin states for cobalt ions at the surface of the nanoparticles, leading to more active sites with increased reactivity. The activity of electrocatalysts exhibits a strong dependence on their electronic structures. Here, the authors manipulate the eg filling of perovskite cobaltite LaCoO3nanoparticles by changing particle size and show improved oxygen evolution activity with increased numbers of surface high-spin cobalt ions.

285 citations

Journal ArticleDOI
Jing-Min Zhou1, Huanhuan Li1, Huan Zhang1, Hui-Min Li1, Wei Shi1, Peng Cheng1 
TL;DR: A new bimetallic lanthanide metal-organic framework exhibits high-sensitivity luminescent sensing of mixtures of organic compounds and can work over a large range of volume ratios.
Abstract: A new bimetallic lanthanide metal-organic framework [Eu0.5 Tb1.5 (FDA)3 ] (H2 FDA = 2,5-furandicarboxylic acid) exhibits high-sensitivity luminescent sensing of mixtures of organic compounds and can work over a large range of volume ratios. The self-calibrating behavior of this color-gradient luminescent sensor is presented for the first time.

284 citations

Journal ArticleDOI
Qinghong Wang1, Lifang Jiao1, Hongmei Du1, Yuchang Si1, Yijing Wang1, Huatang Yuan1 
TL;DR: In this paper, a novel nanocomposite of Co3S4 hollow nanospheres grown on reduced graphene oxide (rGO) has been synthesized by a facile two-step method and used as an advanced electrode material for supercapacitors.
Abstract: A novel nanocomposite of Co3S4 hollow nanospheres grown on reduced graphene oxide (rGO) has been synthesized by a facile two-step method and used as an advanced electrode material for supercapacitors. The intriguing formation and attachment mechanism of these Co3S4 hollow nanospheres on graphene are investigated. More importantly, it is found that the electrochemical performance of the as-prepared nanocomposite could be effectively improved by the chemical interaction between rGO and Co3S4. Specifically, it exhibits a high specific discharge capacitance of 675.9 F g−1 at 0.5 A g−1 and 521.7 F g−1 at 5 A g−1. These results suggest the great promise of fabricating graphene-supported hybrid materials for high-performance energy applications.

284 citations


Authors

Showing all 43397 results

NameH-indexPapersCitations
Yi Chen2174342293080
Peidong Yang183562144351
Jie Zhang1784857221720
Yang Yang1712644153049
Qiang Zhang1611137100950
Bin Liu138218187085
Jun Chen136185677368
Hui Li1352982105903
Jie Liu131153168891
Han Zhang13097058863
Jian Zhou128300791402
Chao Zhang127311984711
Wei Chen122194689460
Xuan Zhang119153065398
Yang Li117131963111
Network Information
Related Institutions (5)
Nanjing University
105.5K papers, 2.2M citations

95% related

University of Science and Technology of China
101K papers, 2.4M citations

94% related

Chinese Academy of Sciences
634.8K papers, 14.8M citations

94% related

Zhejiang University
183.2K papers, 3.4M citations

94% related

Fudan University
117.9K papers, 2.6M citations

93% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023186
2022927
20215,274
20204,645
20194,261
20183,520