Carbides of groups IV through VI (Ti, V and Cr groups) have long been proposed as substitutes for noble metal-based electrocatalysts in polymer electrolyte fuel cells. However, their catalytic activity has been extremely limited because of the low density and stability of catalytically active sites. Here we report the excellent performance of a niobium-carbon structure for catalysing the cathodic oxygen reduction reaction. A large number of single niobium atoms and ultra small clusters trapped in graphitic layers are directly identified using state-of-the-art aberration-corrected scanning transmission electron microscopy. This structure not only enhances the overall conductivity for accelerating the exchange of ions and electrons, but it suppresses the chemical/thermal coarsening of the active particles. Experimental results coupled with theory calculations reveal that the single niobium atoms incorporated within the graphitic layers produce a redistribution of d-band electrons and become surprisingly active for O 2 adsorption and dissociation, and also exhibit high stability.
Two-dimensional (2D) ultrathin silicon nanosheets (Si NSs) were synthesized by DC arc discharge method and investigated as anode material for Li-ion batteries. The 2D ultrathin characteristics of Si NSs is confirmed by means of transmission electron microscopy (TEM) and atomic force microscopy (AFM). The average size of Si NSs is about 20 nm, with thickness less than 2.5 nm. The characteristic Raman peak of Si NSs is found to have an appreciable (20 nm) shift to low frequency, presumably due to the size effect. The synergistic effects of Ar(+) and H(+) lead to 2D growth of Si NSs under high temperature and energy. Electrochemical analyses reveal that Si NSs anode possesses stable cycling performance and fast diffusion of Li-ions with insertion/extraction processes. Such Si NSs might be a promising candidate for anode of Li-ion batteries.
Accurate monitoring of grassland biomass at high spatial and temporal resolutions is important for the effective utilization of grasslands in ecological and agricultural applications. However, current remote sensing data cannot simultaneously provide accurate monitoring of vegetation changes with fine temporal and spatial resolutions. We used a data-fusion approach, namely the spatial and temporal adaptive reflectance fusion model (STARFM), to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. This provided observations at fine temporal (8-d) and medium spatial (30 m) resolutions. Based on field-sampled aboveground biomass (AGB), synthetic NDVI and support vector machine (SVM) techniques were integrated to develop an AGB estimation model (SVM-AGB) for Xilinhot in Inner Mongolia, China. Compared with model generated from MODIS-NDVI (R 2 = 0.73, root-mean-square error (RMSE) = 30.61 g/m 2 ), the SVM-AGB model we developed can not only ensure the accuracy of estimation (R 2 = 0.77, RMSE = 17.22 g/m 2 ), but also produce higher spatial (30 m) and temporal resolution (8-d) biomass maps. We then generated the time-series biomass to detect biomass anomalies for grassland regions. We found that the synthetic NDVI-derived estimations contained more details on the distribution and severity of vegetation anomalies compared with MODIS NDVI-derived AGB estimations. This is the first time that we have generated time series of grassland biomass with 30-m and 8-d intervals data through combined use of a data-fusion method and the SVM-AGB model. Our study will be useful for near real-time and accurate (improved resolutions) monitoring of grassland conditions, and the data have implications for arid and semi-arid grasslands management.
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