|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Constructing highly dispersed 0D Co3S4 quantum dots/2D g-C3N4 nanosheets nanocomposites for excellent photocatalytic performance显示文摘The development of noble-metal-free catalysts with high efficiency photocatalytic properties is critical to the heterogeneous catalysis. Herein, zero-dimensional(0 D) metal sulfide quantum dots/two-dimensional(2 D) g-C3N4 nanosheets(Co3S4/CNNS) nanocomposites are synthesized by a two-step method, including the ways of in-situ deposition and water bath. The highly dispersed Co3S4 quantum dots(particle size is2–4 nm) are evenly and tightly fixed on CNNS, which can be used as co-catalyst to effectively replace noble metals to improve the photocatalytic properties of CNNS. Co3S4/CNNS-900 has the apparent quantum efficiency, which is up to 7.85% at 400 nm. At the same time, the H2 evolution rate of Co3S4/CNNS-900 is 20,536.4 lmol gà1 hà1, which is 555 times than CNNS. The excellent photocatalytic performance is due to the highly dispersed Co3S4 quantum dots on 2 D CNNS, which facilitate the formation of more active sites, Co3S4/CNNS promotes the separation and migration of photogenerated carriers, shortens the migration distance of photogenerated carriers, and eventually leads to an increase of the photocatalytic performance. | Hongcen Yang Jiangmei Yin Ruya Cao Pengxiao Sun Shouwei Zhang Xijin Xu | 2019 | Science Bulletin2019，64，20： | 7 |
| 2 | Nickel/cobalt based materials for supercapacitors显示文摘The electrode materials as the key component of supercapacitors have attracted considerable research interests, especially for nickel/cobalt based materials by virtue of their superior electrochemical performance with multiple oxidation states for richer redox reactions, abundant natural resources, lower prices and toxicity. There are many advanced electrodes based on the nickel/cobalt materials exploited for the application of supercapacitors, however, some controversial statements have induced some confusion. Herein, we refine the mechanism of energy storage for the nickel/cobalt based materials for supercapacitors and reclassify them into battery-type materials with the corresponding devices named as hybrid supercapacitors. | Chenggang Wang Pengxiao Sun Guangmeng Qu Jiangmei Yin Xijin Xu | 2018 | Chinese Chemical Letters2018，29，12： | 2 |
| 3 | An overview of the switching parameter variation of RRAM显示文摘Resistive random access memory(RRAM) has been considered as one of the most promising candidates for next-generation nonvolatile memory, due to its advantages of simple device structure, excellent scalability, fast operation speed and low power consumption. Deeply understanding the physical mechanism and effectively controlling the statistical variation of switching parameters are the basis of fostering RRAM into commercial application. In this paper, based on the deep understanding on the mechanism of the formation and rupture of conductive filament, we summarize the methods of analyzing and modeling the statistics of switching parameters such as SET/RESET voltage, current, speed or time. Then, we analyze the distributions of switching parameters and the influencing factors. Additionally, we also sum up the analytical model of resistive switching statistics composed of the cell-based percolation model and SET/RESET switching dynamics. The results of the model can successfully explain the experimental distributions of switching parameters of the Ni O- and Hf O2-based RRAM devices. The model also provides theoretical guide on how to improve the uniformity and reliability such as disturb immunity. Finally, some experimental approaches to improve the uniformity of switching parameters are discussed. | Meiyun Zhang Shibing Long Guoming Wang Yang Li Xiaoxin Xu Hongtao Liu Ruoyu Liu Ming Wang Congfei Li Pengxiao Sun Haitao Sun Qi Liu Hangbing L Ming Liu | 2014 | Chinese Science Bulletin2014，59，36： | 1 |
| 4 | Hydrological Response to Climate and Land Use Changes in the Dry–Warm Valley of the Upper Yangtze River显示文摘The hydrological process in the dry–warm valley of the mountainous area of southwest China has unique characteristics and has attracted scientific attention worldwide.Given that this is an area with fragile ecosystems and intensive water resource conflicts in the upper reaches of the Yangtze River,a systematic identification of its hydrological responses to climate and land use variations needs to be performed.In this study,MIKE SHE was employed and calibrated for the Anning River Basin in the dry–warm valley.Subsequently,a deep learning neural network model of the long short-term memory(LSTM)and a traditional multi-model ensemble mean(MMEM)method were used for an ensemble of 31 global climate models(GCMs)for climate projection.The cellular automata–Markov model was implemented to project the spatial pattern of land use considering climatic,social,and economic conditions.Four sets of climate projections and three sets of land use projections were generated and fed into the MIKE SHE to project hydrologic responses from 2021 to 2050.For the calibration and first validation periods of the daily simulation,the coefficients of determination(R)were 0.85 and 0.87 and the Nash–Sutcliffe efficiency values were 0.72 and 0.73,respectively.The advanced LSTM performed better than the traditional MMEM method for daily temperature and monthly precipitation.The average monthly temperature projection under representative concentration pathway 8.5(RCP8.5)was expected to be slightly higher than that under RCP4.5;this is contrary to the average monthly precipitation from June to October.The variations in streamflow and actual evapotranspiration(ET)were both more sensitive to climate change than to land use change.There was no significant relationship between the variations in streamflow and the ET in the study area.This work could provide general variation conditions and a range of hydrologic responses to complex and changing environments,thereby assisting with stochastic uncertainty and optimizing water resource management in critical regions. | Congcong Li Yanpeng Cai Zhong Li Qianqian Zhang Lian Sun Xinyi Li Pengxiao Zhou | 2022 | Engineering2022，，12： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员