scispace - formally typeset
Search or ask a question
Institution

Nanjing University

EducationNanjing, 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.


Papers
More filters
Journal ArticleDOI
04 Jun 2007-Polymer
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Proceedings Article
27 Jul 2014
TL;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

Journal ArticleDOI
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

NameH-indexPapersCitations
Yi Chen2174342293080
H. S. Chen1792401178529
Zhenan Bao169865106571
Gang Chen1673372149819
Peter G. Schultz15689389716
Xiang Zhang1541733117576
Rui Zhang1512625107917
Yi Yang143245692268
Markku Kulmala142148785179
Jian Yang1421818111166
Wei Huang139241793522
Bin Liu138218187085
Jun Lu135152699767
Hui Li1352982105903
Lei Zhang135224099365
Network Information
Related Institutions (5)
Peking University
181K papers, 4.1M citations

97% related

Chinese Academy of Sciences
634.8K papers, 14.8M citations

95% related

Zhejiang University
183.2K papers, 3.4M citations

95% related

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

95% related

Fudan University
117.9K papers, 2.6M citations

95% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20242
2023276
20221,087
20219,130
20208,684
20198,203