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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 & Adsorption. The organization has 85961 authors who have published 105504 publications receiving 2289036 citations. The organization is also known as: NJU & Nanking University.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the state-of-the-art progress of semiconductor/semiconductor heterostructured photocatalysts with diverse models, including type-I and type-II heterojunctions, Z-scheme system, p-n heterojunction, and homojunction band alignments, were explored for effective improvement of photocatalysis activity through increase of the visible-light absorption, promotion of separation, and transportation of the photoinduced charge carries.
Abstract: Semiconductor photocatalysts have received much attention in recent years due to their great potentials for the development of renewable energy technologies, as well as for environmental protection and remediation. The effective harvesting of solar energy and suppression of charge carrier recombination are two key aspects in photocatalysis. The formation of heterostructured photocatalysts is a promising strategy to improve photocatalytic activity, which is superior to that of their single component photocatalysts. This Feature Article concisely summarizes and highlights the state-of-the-art progress of semiconductor/semiconductor heterostructured photocatalysts with diverse models, including type-I and type-II heterojunctions, Z-scheme system, p–n heterojunctions, and homojunction band alignments, which were explored for effective improvement of photocatalytic activity through increase of the visible-light absorption, promotion of separation, and transportation of the photoinduced charge carries, and enhancement of the photocatalytic stability.

680 citations

Journal ArticleDOI
TL;DR: A design principle is proposed to realize achromatic metasurface devices which successfully eliminate the chromatic aberration over a continuous wavelength region from 1200 to 1680 nm for circularly-polarized incidences in a reflection scheme.
Abstract: Among various flat optical devices, metasurfaces have presented their great ability in efficient manipulation of light fields and have been proposed for variety of devices with specific functionalities. However, due to the high phase dispersion of their building blocks, metasurfaces significantly suffer from large chromatic aberration. Here we propose a design principle to realize achromatic metasurface devices which successfully eliminate the chromatic aberration over a continuous wavelength region from 1200 to 1680 nm for circularly-polarized incidences in a reflection scheme. For this proof-of-concept, we demonstrate broadband achromatic metalenses (with the efficiency on the order of ∼12%) which are capable of focusing light with arbitrary wavelength at the same focal plane. A broadband achromatic gradient metasurface is also implemented, which is able to deflect wide-band light by the same angle. Through this approach, various flat achromatic devices that were previously impossible can be realized, which will allow innovation in full-color detection and imaging. Metasurfaces suffer from large chromatic aberration due to the high phase dispersion of their building blocks, limiting their applications. Here, Wang et al. design achromatic metasurface devices which eliminate the chromatic aberration over a continuous region from 1200 to 1680 nm in a reflection schleme.

679 citations

Journal ArticleDOI
Peng-Fei Chen1
TL;DR: In this paper, a review on each stage of the CME phenomenon is presented, including their pre-eruption structure, their triggering mechanisms and the precursors indicating the initiation process, their acceleration and propagation.
Abstract: Coronal mass ejections (CMEs) are the largest-scale eruptive phenomenon in the solar system, expanding from active region-sized nonpotential magnetic structure to a much larger size. The bulk of plasma with a mass of ∼ 1011,1013 kg is hauled up all the way out to the interplanetary space with a typical velocity of several hundred or even more than 1000 km s−1, with a chance to impact our Earth, resulting in hazardous space weather conditions. They involve many other much smaller-sized solar eruptive phenomena, such as X-ray sigmoids, filament/prominence eruptions, solar flares, plasma heating and radiation, particle acceleration, EIT waves, EUV dimmings, Moreton waves, solar radio bursts, and so on. It is believed that, by shedding the accumulating magnetic energy and helicity, they complete the last link in the chain of the cycling of the solar magnetic field. In this review, I try to explicate our understanding on each stage of the fantastic phenomenon, including their pre-eruption structure, their triggering mechanisms and the precursors indicating the initiation process, their acceleration and propagation. Particular attention is paid to clarify some hot debates, e.g., whether magnetic reconnection is necessary for the eruption, whether there are two types of CMEs, how the CME frontal loop is formed, and whether halo CMEs are special.

679 citations

Proceedings ArticleDOI
19 Aug 2017
TL;DR: In this article, a decision tree ensemble approach with performance highly competitive to deep neural networks in a broad range of tasks is proposed, and the training process of gcForest is efficient, and users can control training cost according to computational resource available.
Abstract: In this paper, we propose gcForest, a decision tree ensemble approach with performance highly competitive to deep neural networks in a broad range of tasks. In contrast to deep neural networks which require great effort in hyper-parameter tuning, gcForest is much easier to train; even when it is applied to different data across different domains in our experiments, excellent performance can be achieved by almost same settings of hyper-parameters. The training process of gcForest is efficient, and users can control training cost according to computational resource available. The efficiency may be further enhanced because gcForest is naturally apt to parallel implementation. Furthermore, in contrast to deep neural networks which require largescale training data, gcForest can work well even when there are only small-scale training data.

678 citations

Journal ArticleDOI
M. Ablikim, M. N. Achasov1, Xiaocong Ai, O. Albayrak2  +365 moreInstitutions (50)
TL;DR: In this article, the process e(+)e(-) -> pi(+)pi(-) J/psi at a center-of-mass energy of 4.260 GeV using a 525 pb(-1) data sample collected with the BESIII detector operating at the Beijing Electron Positron Collider was studied.
Abstract: We study the process e(+)e(-) -> pi(+)pi(-) J/psi at a center-of-mass energy of 4.260 GeV using a 525 pb(-1) data sample collected with the BESIII detector operating at the Beijing Electron Positron Collider. The Born cross section is measured to be (62.9 +/- 1.9 +/- 3.7) pb, consistent with the production of the Y(4260). We observe a structure at around 3.9 GeV/c(2) in the pi(+/-) J/psi mass spectrum, which we refer to as the Z(c)(3900). If interpreted as a new particle, it is unusual in that it carries an electric charge and couples to charmonium. A fit to the pi(+/-) J/psi invariant mass spectrum, neglecting interference, results in a mass of (3899.0 +/- 3.6 +/- 4.9) MeV/c(2) and a width of (46 +/- 10 +/- 20) MeV. Its production ratio is measured to be R = (sigma(e(+)e(-) -> pi(+/-) Z(c)(3900)(-/+) -> pi(+)pi(-) J/psi)/sigma(e(+)e(-) -> pi(+)pi(-) J/psi)) = (21.5 +/- 3.3 +/- 7.5)%. In all measurements the first errors are statistical and the second are systematic.

677 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
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20242
2023276
20221,089
20219,130
20208,684
20198,203