Bipolar supercurrent in graphene

Graphene--a recently discovered form of graphite only one atomic layer thick--constitutes a new model system in condensed matter physics, because it is the first material in which charge carriers behave as massless chiral relativistic particles. The anomalous quantization of the Hall conductance, which is now understood theoretically, is one of the experimental signatures of the peculiar transport properties of relativistic electrons in graphene. Other unusual phenomena, like the finite conductivity of order 4e(2)/h (where e is the electron charge and h is Planck's constant) at the charge neutrality (or Dirac) point, have come as a surprise and remain to be explained. Here we experimentally study the Josephson effect in mesoscopic junctions consisting of a graphene layer contacted by two closely spaced superconducting electrodes. The charge density in the graphene layer can be controlled by means of a gate electrode. We observe a supercurrent that, depending on the gate voltage, is carried by either electrons in the conduction band or by holes in the valence band. More importantly, we find that not only the normal state conductance of graphene is finite, but also a finite supercurrent can flow at zero charge density. Our observations shed light on the special role of time reversal symmetry in graphene, and demonstrate phase coherent electronic transport at the Dirac point.     

Vegetation change detection using artificial neural networks with ancillary data in Xishuangbanna,Yunnan Province,China

Timely and accurate change detection of the Earth’s surface features provides the foundation for better planning, management and environmental studies. In this study ANN change detection was used to perform vegetation change detection, and was compared with post-classification method. Before the post-classification was performed the ANN classification was used to yield multitemporal vegetation maps. ANN were also used to perform a one-pass classification for the images in 2003 and 2004. DEM and slope were used as two extra channels. During the training stage, the training data was separated into 82 subclasses including 36 change subclasses and 46 no change subclasses. Moreover NDVI differencing methods were used to develop the change mask. The result showed that combining the NDVI differencing method with visual interpretation when identifying reference areas can produce more accurate change detection results for the ANN one pass change classification. Moreover, it is effective to use elevation and slope as extra channels together with PCA components, to perform ANN-based change detection in mountainous study areas. It is also important to separate the vegetation transition classes into subclasses based on spectral response patterns, especially for mountainous terrains. This processing can reduce the topographic effect and improve the change detection accuracy.