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| 1 | FeCo alloy@N-doped graphitized carbon as an efficient cocatalyst for enhanced photocatalytic H2 evolution by inducing accelerated charge transfer显示文摘Cocatalysts play important roles in improving the activity and stability of most photocatalysts.It is of great significance to develop economical,efficient and stable cocatalysts.Herein,using Na2CoFe(CN)6 complex as precursor,a novel noble-metal-free FeCo@NGC cocatalyst(nano-FeCo alloy@N-doped graphitized carbon) is fabricated by a simple pyrolysis method.Coupling with g-C3 N4, the optimal FeCo@NGC/g-C3N4 receives a boosted visible light driven photocatalytic H2 evolution rate of 42.2 μmol h-1, which is even higher than that of 1.0 wt% Pt modified g-C3N4 photocatalyst.Based on the results of density functional theory(DFT) calculations and practical experiment measurements,such outstanding photocatalytic performance of FeCo@NGC/g-C3N4 is mainly attributed to two aspects.One is the accelerated charge transfer behavior,induced by a photogene rated electrons secondary transfer performance on the surface of FeCo alloy nanoparticles.The other is related to the adjustment of H adsorption energy(approaching the standard hydrogen electrode potential) by the presence of external NGC thin layer.Both factors play key roles in the H2 evolution reaction.Such outstanding performance highlights an enormous potential of developing noble-metal-free bimetallic nano-alloy as inexpensive and efficient cocatalysts for solar applications. | Sibo Chen Yun Hau Ng Jihai Liao Qiongzhi Gao Siyuan Yang Feng Peng Xinhua Zhong Yueping Fang Shengsen Zhang | 2021 | Journal of Energy Chemistry2021，30，1： | 3 |
| 2 | Online Video Popularity Regression Prediction Model with Multichannel Dynamic Scheduling Based on User Behavior显示文摘Popularity prediction of online video is widely used in many different scenarios.It can not only help video service providers to schedule video web sites,but also bring considerable profits on investment for both providers and advertisers if popularity of online video is predicted accurately.However,online video popularity prediction still cannot have a satisfactory result,due to the complexity of many crucial factors especially of video distribution network.In this article,we extract seven factors from huge amounts of data about user behavior,establishing a new multiple linear regression model to initially predict online video popularity.After that,a multichannel video popularity dynamic scheduling model is proposed to schedule videos on which channel and what time to be broadcast,according to its popularity predicted by multiple linear regression model,ensuring that maximum the sum value of online video popularity of each channel.Experimental results on dataset obtained from Sohu Video,a video service provider in China,and real-world video flow in Sohu Video demonstrate that the proposed model is robust and has promising performance in predicting online video popularity,which is helpful for video service providers to schedule videos on web sites effectively in the future. | QIAO Sibo PANG Shanchen WANG Min ZHAI Xue DAI Feng | 2021 | Chinese Journal of Electronics2021，30，5： | 1 |
| 3 | Dynamic cooperations between lattice oxygen and oxygen vacancies for photocatalytic ethane dehydrogenation by a self-restoring LaVO_(4)catalyst显示文摘Thermocatalytic nonoxidative ethane dehydrogenation(EDH)is a promising strategy for ethene production but suffers from intense energy consumption and poor catalyst durability;exploring technology that permits efficient EDH by solar energy remains a giant challenge.Herein,we present that an oxygen vacancy(O_v)-rich LaVO_(4)(LaVO_(4)-O_v)catalyst is highly active and stable for photocatalytic EDH,through a dynamic lattice oxygen(O_(latt.))and O_(v)co-mediated mechanism.Irradiated by simulated sunlight at mild conditions,LaVO_(4)-O_(v)effectively dehydrogenates undiluted ethane to produce C_(2)H_(4)and CO with a conversion of 2.3%.By loading a small amount of Pt cocatalyst,the evolution and selectivity of C_(2)H_(4)are enhanced to 275μmol h^(-1)g^(-1)and 96.8%.Of note,LaVO_(4)-O_(v)appears nearly no carbon deposition after the reaction.The isotope tracked reactions reveal that the consumed O_(latt.)recuperates by exposing the used catalyst with O_(2),thus establishing a dynamic cycle of O_(latt.)and achieving a facile catalyst regeneration to preserve its intrinsic activity.The refreshed LaVO_(4)-O_(v)exhibits superior reusability and delivers a turnover number of about 305.The O_(v)promotes photo absorption,boosts ethane adsorption/activation,and accelerates charge separation/transfer,thus improving the photocatalytic efficiency.The possible photocatalytic EDH mechanism is proposed,considering the key intermediates predicted by density functional theory(DFT)and monitored by in-situ diffuse reflectance infrared Fourier transform spectroscopy(DRIFTS). | Fen Wei Weichao Xue Zhiyang Yu Xue Feng Lu Sibo Wang Wei Lin Xinchen Wang | 2024 | Chinese Chemical Letters2024，35，3： | 0 |
| 4 | Position-selected cocatalyst modification on a Z-scheme Cd_(0.5)Zn_(0.5)S/NiTiO_(3) photocatalyst for boosted H_(2) evolution显示文摘Photocatalytic water splitting by semiconductors is a promising technology to produce clean H_(2) fuel,but the efficiency is restrained seriously by the high overpotential of the H_(2)-evolution reaction together with the high recombination rate of photoinduced charges.To enhance H_(2) production,it is highly desirable yet challenging to explore an efficient reductive cocatalyst and place it precisely on the right sites of the photocatalyst surface to work the proton reduction reaction exclusively.Herein,the metalloid NixP cocatalyst is exactly positioned on the Z-scheme Cd_(0.5)Zn_(0.5)S/NiTiO_(3)(CZS/NTO)heterostructure through a facile photodeposition strategy,which renders the cocatalyst form solely at the electron-collecting locations.It is revealed that the directional transfer of photoexcited electrons from Cd_(0.5)Zn_(0.5)S to Ni_(x)P suppresses the quenching of charge carriers.Under visible light,the CZS/NTO hybrid loaded with the Ni_(x)P cocatalyst exhibits an optimal H_(2) yield rate of 1103μmol h^(-1)(i.e.,27.57 mmol h^(-1)g^(-1)),which is about twofold of pristine CZS/NTO and comparable to the counterpart deposited with the Pt cocatalyst.Besides,the high apparent quantum yield(AQY)of 56%is reached at 400 nm.Further,the mechanisms of the cocatalyst formation and the H2 generation reaction are discussed in detail. | Bifang Li Wenyu Guo Xue Feng Lu Yidong Hou Zhengxin Ding Sibo Wang | 2023 | Materials Reports(Energy)2023，3，4： | 0 |
| 5 | Pulsar candidate selection using ensemble networks for FAST drift-scan survey显示文摘The Commensal Radio Astronomy Five-hundred-meter Aperture Spherical radio Telescope(FAST) Survey(CRAFTS) utilizes the novel drift-scan commensal survey mode of FAST and can generate billions of pulsar candidate signals. The human experts are not likely to thoroughly examine these signals, and various machine sorting methods are used to aid the classification of the FAST candidates. In this study, we propose a new ensemble classification system for pulsar candidates. This system denotes the further development of the pulsar image-based classification system(PICS), which was used in the Arecibo Telescope pulsar survey, and has been retrained and customized for the FAST drift-scan survey. In this study, we designed a residual network model comprising 15 layers to replace the convolutional neural networks(CNNs) in PICS. The results of this study demonstrate that the new model can sort >96% of real pulsars to belong the top 1% of all candidates and classify >1.6 million candidates per day using a dual-GPU and 24-core computer. This increased speed and efficiency can help to facilitate real-time or quasi-real-time processing of the pulsar-search data stream obtained from CRAFTS. In addition, we have published the labeled FAST data used in this study online, which can aid in the development of new deep learning techniques for performing pulsar searches. | HongFeng Wang WeiWei Zhu Ping Guo Di Li SiBo Feng Qian Yin ChenChen Miao ZhenZhao Tao ZhiChen Pan Pei Wang Xin Zheng XiaoDan Deng ZhiJie Liu XiaoYao Xie XuHong Yu ShanPing You Hui Zhang FAST Collaboration | 2019 | Science China(Physics,Mechanics & Astronomy)2019，62，5： | 0 |
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