2,531 research outputs found

    Mode mixing induced by disorder in graphene PNP junction in a magnetic field

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    We study the electron transport through the graphene PNP junction under a magnetic field and show that modes mixing plays an essential role. By using the non-equilibrium Green's function method, the space distribution of the scattering state for a specific incident modes as well the elements of the transmission and reflection coefficient matrixes are investigated. All elements of the transmission (reflection) coefficient matrixes are very different for a perfect PNP junction, but they are same at a disordered junction due to the mode mixing. The space distribution of the scattering state for the different incident modes also exhibit the similar behaviors, that they distinctly differ from each other in the perfect junction but are almost same in the disordered junction. For a unipolar junction, when the mode number in the center region is less than that in the left and right regions, the fluctuations of the total transmission and reflection coefficients are zero, although each element has a large fluctuation. These results clearly indicate the occurrence of perfect mode mixing and it plays an essential role in a graphene PNP junction transport

    Charge ordering and phase separation in the infinite dimensional extended Hubbard model

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    We study the extended Hubbard model with both on-site (U) and nearest neighbor (V) Coulomb repulsion using the exact diagonalization method within the dynamical mean field theory. For a fixed U (U=2.0), the T-n phase-diagrams are obtained for V=1.4 and V=1.2, at which the ground states of n=1/2 system is charge-ordered and charge-disordered, respectively. In both cases, robust charge order is found at finite temperature and in an extended filling regime around n=1/2. The order parameter changes non-monotonously with temperature. For V=1.4, phase separation between charge-ordered and charge-disordered phases is observed in the low temperature and n < 0.5 regime. It is described by an "S"-shaped structure of the n-/mu curve. For V=1.2, the ground state is charge-disordered, and a reentrant charge-ordering transition is observed for 0.42 < n < 0.68. Relevance of our results to experiments for doped manganites is discussed.Comment: 9 pages, 7 figures, submitted to Phys. Rev.

    Mott-Hubbard transition in infinite dimensions

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    We analyze the unanalytical structure of metal-insulator transition (MIT) in infinite dimensions. By introducing a simple transformation into the dynamical mean-field equation of Hubbard model, a multiple-valued structure in Green's function and other thermodynamical quantities with respect to the interaction strength UU are found at low temperatures. A unified description of stable, metastable and unstable phases is obtained in the regime Uc1(T)<U<Uc2(T)U_{c1}(T)<U<U_{c2}(T), and the Maxwell construction is performed to evaluate the MIT line U∗(T)U^{\ast}(T). We show how the first-order MIT at U∗(T)U^{\ast}(T) for T>0T>0 evolves into second-order one at Uc2(0)U_{c2}(0) for T=0T=0 . The phase diagram near MIT is presented.Comment: 5 pages with 3 figures, text and figures revise

    Sensing Mechanisms of Carbon Nanotube Based NH3 Gas Detectors

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    Circularly Polarized Slotted/Slit-Microstrip Patch Antennas

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    Statistical analysis for a penalized EM algorithm in high-dimensional mixture linear regression model

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    The expectation-maximization (EM) algorithm and its variants are widely used in statistics. In high-dimensional mixture linear regression, the model is assumed to be a finite mixture of linear regression and the number of predictors is much larger than the sample size. The standard EM algorithm, which attempts to find the maximum likelihood estimator, becomes infeasible for such model. We devise a group lasso penalized EM algorithm and study its statistical properties. Existing theoretical results of regularized EM algorithms often rely on dividing the sample into many independent batches and employing a fresh batch of sample in each iteration of the algorithm. Our algorithm and theoretical analysis do not require sample-splitting, and can be extended to multivariate response cases. The proposed methods also have encouraging performances in numerical studies
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