Tsinghua University

About: Tsinghua University is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 129978 authors who have published 200506 publications receiving 4549561 citations. The organization is also known as: Tsinghua & THU.

The first high-resolution meteorological forcing dataset for land process studies over China

Abstract: The China Meteorological Forcing Dataset (CMFD) is the first high spatial-temporal resolution gridded near-surface meteorological dataset developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis datasets and in-situ station data. Its record begins in January 1979 and is ongoing (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in the CMFD, including 2-meter air temperature, surface pressure, and specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate. Validations against observations measured at independent stations show that the CMFD is of superior quality than the GLDAS (Global Land Data Assimilation System); this is because a larger number of stations are used to generate the CMFD than are utilised in the GLDAS. Due to its continuous temporal coverage and consistent quality, the CMFD is one of the most widely-used climate datasets for China.

RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images

Abstract: This paper studies the problem of simultaneously aligning a batch of linearly correlated images despite gross corruption (such as occlusion). Our method seeks an optimal set of image domain transformations such that the matrix of transformed images can be decomposed as the sum of a sparse matrix of errors and a low-rank matrix of recovered aligned images. We reduce this extremely challenging optimization problem to a sequence of convex programs that minimize the sum of l1-norm and nuclear norm of the two component matrices, which can be efficiently solved by scalable convex optimization techniques with guaranteed fast convergence. We verify the efficacy of the proposed robust alignment algorithm with extensive experiments with both controlled and uncontrolled real data, demonstrating higher accuracy and efficiency than existing methods over a wide range of realistic misalignments and corruptions.

Nitrogen-doped graphene/carbon nanotube hybrids : in situ formation on bifunctional catalysts and their superior electrocatalytic activity for oxygen evolution/reduction reaction

Abstract: There is a growing interest in oxygen electrode catalysts for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), as they play a key role in a wide range of renewable energy technologies such as fuel cells, metal-air batteries, and water splitting. Nevertheless, the development of highly-active bifunctional catalysts at low cost for both ORR and OER still remains a huge challenge. Herein, we report a new N-doped graphene/single-walled carbon nanotube (SWCNT) hybrid (NGSH) material as an efficient noble-metal-free bifunctional electrocatalyst for both ORR and OER. NGSHs were fabricated by in situ doping during chemical vapor deposition growth on layered double hydroxide derived bifunctional catalysts. Our one-step approach not only provides simultaneous growth of graphene and SWCNTs, leading to the formation of three dimensional interconnected network, but also brings the intrinsic dispersion of graphene and carbon nanotubes and the dispersion of N-containing functional groups within a highly conductive scaffold. Thus, the NGSHs possess a large specific surface area of 812.9 m(2) g(-1) and high electrical conductivity of 53.8 S cm(-1) . Despite of relatively low nitrogen content (0.53 at%), the NGSHs demonstrate a high ORR activity, much superior to two constituent components and even comparable to the commercial 20 wt% Pt/C catalysts with much better durability and resistance to crossover effect. The same hybrid material also presents high catalytic activity towards OER, rendering them high-performance cheap catalysts for both ORR and OER. Our result opens up new avenues for energy conversion technologies based on earth-abundant, scalable, noble-metal-free catalysts.