Institution
Xiamen University
Education•Amoy, Fujian, China•
About: Xiamen University is a education organization based out in Amoy, Fujian, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 50472 authors who have published 54480 publications receiving 1058239 citations. The organization is also known as: Amoy University & Xiàmén Dàxué.
Topics: Catalysis, Population, Graphene, Raman spectroscopy, Anode
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the thermal conductivity of a graphene monolayer grown by chemical vapor deposition and suspended over holes with different diameters ranging from 2.9 to 9.7 μm was measured in vacuum, thereby eliminating errors caused by heat loss to the surrounding gas.
Abstract: Using micro-Raman spectroscopy, the thermal conductivity of a graphene monolayer grown by chemical vapor deposition and suspended over holes with different diameters ranging from 2.9 to 9.7 μm was measured in vacuum, thereby eliminating errors caused by heat loss to the surrounding gas. The obtained thermal conductivity values of the suspended graphene range from (2.6 ± 0.9) to (3.1 ± 1.0) × 103 Wm−1K−1 near 350 K without showing the sample size dependence predicted for suspended, clean, and flat graphene crystal. The lack of sample size dependence is attributed to the relatively large measurement uncertainty as well as grain boundaries, wrinkles, defects, or polymeric residue that are possibly present in the measured samples. Moreover, from Raman measurements performed in air and CO2 gas environments near atmospheric pressure, the heat transfer coefficient for air and CO2 was determined and found to be (2.9 +5.1/−2.9) and (1.5 +4.2/−1.5) × 104 Wm−2K−1, respectively, when the graphene temperature was heat...
498 citations
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TL;DR: In this article, the authors adopted the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework as a starting point and re-estimated the relationship using different panel date models.
Abstract: Urbanization and industrialization have significant impacts on energy consumption and CO2 emissions, but their relationship varies at different stages of economic development. Taking cognizance of heterogeneity and the “ratchet effect,” this paper adopts the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework as a starting point and re-estimates the relationship using different panel date models. The main results are obtained by dynamic panel threshold regression models, which divide a balanced panel dataset of 73 countries over the period of 1971–2010 into four groups according to their annual income levels. The key results are: (1) in the low-income group, urbanization decreases energy consumption but increases CO2 emissions; (2) in the middle-/low-income and high-income groups, industrialization decreases energy consumption but increases CO2 emissions, while urbanization significantly increases both energy consumption and CO2 emissions; (3) for the middle-/high-income group, urbanization does not significantly affect energy consumption, but does hinder the growth of emissions; while industrialization was found to have an insignificant impact on energy consumption and CO2 emissions; (4) from the population perspective, it produces positive effects on energy consumption, and also increases emissions except for the high-income group. These novel methodology and findings reveal that different development strategies of urbanization and industrialization should be pursued depending on the levels of income in a bid to conserve energy and reduce emissions.
496 citations
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TL;DR: Mental health outcomes were statistically positively correlated with skin lesion and negatively correlated with self-efficacy, resilience, social support, and frontline work willingness, and future interventions at the national and organisational levels are needed.
494 citations
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TL;DR: Results clearly indicate that CTAC worked much better than CTAB as a capping agent in both the syntheses of Au seeds and Au@Ag core-shell nanocubes, which could be converted into Au-based hollow nanostructures containing the original Au seeds in the interiors through a galvanic replacement reaction.
Abstract: NSF [DMR 0804088, ECS 0335765]; NIH [1R01 CA138527]; Ministry of Education Science and Technology [R32 20031]; China Scholarship Council
490 citations
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TL;DR: In this paper, the simultaneous entrapment of biological macromolecules and nanostructured silica-coated magnetite in sol−gel materials using a reverse-micelle technique leads to a bioactive, mechanically stable, nanometer-sized, and magnetically separable particles.
Abstract: The simultaneous entrapment of biological macromolecules and nanostructured silica-coated magnetite in sol−gel materials using a reverse-micelle technique leads to a bioactive, mechanically stable, nanometer-sized, and magnetically separable particles. These spherical particles have a typical diameter of 53 ± 4 nm, a large surface area of 330 m2/g, an average pore diameter of 1.5 nm, a total pore volume of 1.427 cm3/g and a saturated magnetization (MS) of 3.2 emu/g. Peroxidase entrapped in these particles shows Michaelis−Mentan kinetics and high activity. The catalytic reaction will take place immediately after adding these particles to the reaction solution. These enzyme entrapping particles catalysts can be easily separated from the reaction mixture by simply using an external magnetic field. Experiments have proved that these catalysts have a long-term stability toward temperature and pH change, as compared to free enzyme molecules. To further prove the application of this novel magnetic biomaterial in...
489 citations
Authors
Showing all 50945 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Lei Jiang | 170 | 2244 | 135205 |
Yang Gao | 168 | 2047 | 146301 |
William A. Goddard | 151 | 1653 | 123322 |
Rui Zhang | 151 | 2625 | 107917 |
Xiaoyuan Chen | 149 | 994 | 89870 |
Fuqiang Wang | 145 | 1518 | 95014 |
Galen D. Stucky | 144 | 958 | 101796 |
Shu-Hong Yu | 144 | 799 | 70853 |
Wei Huang | 139 | 2417 | 93522 |
Bin Liu | 138 | 2181 | 87085 |
Jie Liu | 131 | 1531 | 68891 |
Han Zhang | 130 | 970 | 58863 |
Lei Zhang | 130 | 2312 | 86950 |
Jian Zhou | 128 | 3007 | 91402 |