Realization of Artificial Ice Systems for Magnetic Vortices in a Superconducting MoGe Thin-film with Patterned Nanostructures

We report an anomalous matching effect in MoGe thin films containing pairs of circular holes arranged in such a way that four of those pairs meet at each vertex point of a square lattice. A remarkably pronounced fractional matching was observed in the magnetic field dependences of both the resistance and the critical current. At the half matching field the critical current can be even higher than that at zero field. This has never been observed before for vortices in superconductors with pinning arrays. Numerical simulations within the nonlinear Ginzburg-Landau theory reveal a square vortex ice configuration in the ground state at the half matching field and demonstrate similar characteristic features in the field dependence of the critical current, confirming the experimental realization of an artificial ice system for vortices for the first time.Comment: To appear in Phys. Rev. Let

Edge and Bulk Transport in the Mixed State of a Type-II Superconductor

By comparing the voltage-current (V-I) curves obtained before and after cutting a sample of 2H-NbSe2, we separate the bulk and edge contributions to the transport current at various dissipation levels and derive their respective V- I curves and critical currents. We find that the edge contribution is thermally activated across a current dependent surface barrier. By contrast the bulk V-I curves are linear, as expected from the free flux flow model. The relative importance of bulk and edge contributions is found to depend on dissipation level and sample dimensions. We further show that the peak effect is a sharp bulk phenomenon and that it is broadened by the edge contribution

Correlations among superconductivity, structural instability, and band filling in Nb1-xB2 at the critical point x=0.2

We performed an extensive investigation on the correlations among superconductivity, structural instability and band filling in Nb1-xB2 materials. Structural measurements reveal that a notable phase transformation occurs at x=0.2, corresponding to the Fermi level (EF) in the pseudogap with the minimum total density of states (DOS) as demonstrated by the first-principles calculations. Superconductivity in Nb1-xB2 generally becomes visible in the Nb-deficient materials with x=0.2. Electron energy-loss spectroscopy (EELS) measurements on B K-edge directly demonstrated the presence of a chemical shift arising from the structural transformation. Our systematical experimental results in combination with theoretical analysis suggest that the emergence of hole states in the sigma-bands plays an important role for understanding the superconductivity and structural transition in Nb1-xB2.Comment: 16 pages, 4 figure

Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning

Identification and characterization of new traits with sound physiological foundation is essential for crop breeding and production management. Deep learning has been widely used in image data analysis to explore spatial and temporal information on crop growth and development, thus strengthening the power of identification of physiological traits. Taking the advantage of deep learning, this study aims to develop a novel trait of canopy structure that integrate source and sink in japonica rice. We applied a deep learning approach to accurately segment leaf and panicle, and subsequently developed the procedure of GvCrop to calculate the leaf to panicle ratio (LPR) of rice canopy during grain filling stage. Images of training dataset were captured in the field experiments, with large variations in camera shooting angle, the elevation and the azimuth angles of the sun, rice genotype, and plant phenological stages. Accurately labeled by manually annotating the panicle and leaf regions, the resulting dataset were used to train FPN-Mask (Feature Pyramid Network Mask) models, consisting of a backbone network and a task-specific sub-network. The model with the highest accuracy was then selected to check the variations in LPR among 192 rice germplasms and among agronomical practices. Despite the challenging field conditions, FPN-Mask models achieved a high detection accuracy, with Pixel Accuracy being 0.99 for panicles and 0.98 for leaves. The calculated LPR displayed large spatial and temporal variations as well as genotypic differences. In addition, it was responsive to agronomical practices such as nitrogen fertilization and spraying of plant growth regulators. Deep learning technique can achieve high accuracy in simultaneous detection of panicle and leaf data from complex rice field images. The proposed FPN-Mask model is applicable to detect and quantify crop performance under field conditions. The newly identified trait of LPR should provide a high throughput protocol for breeders to select superior rice cultivars as well as for agronomists to precisely manage field crops that have a good balance of source and sink