Focusing RKKY interaction by graphene P-N junction

The carrier-mediated RKKY interaction between local spins plays an important role for the application of magnetically doped graphene in spintronics and quantum computation. Previous studies largely concentrate on the influence of electronic states of uniform systems on the RKKY interaction. Here we reveal a very different way to manipulate the RKKY interaction by showing that the anomalous focusing - a well-known electron optics phenomenon in graphene P-N junctions - can be utilized to refocus the massless Dirac electrons emanating from one local spin to the other local spin. This gives rise to rich spatial interference patterns and symmetry-protected non-oscillatory RKKY interaction with a strongly enhanced magnitude. It may provide a new way to engineer the long-range spin-spin interaction in graphene.Comment: 9 pages, 4 figure

Slip of fluid molecules on solid surfaces by surface diffusion

The mechanism of fluid slip on a solid surface has been linked to surface diffusion, by which mobile adsorbed fluid molecules perform hops between adsorption sites. However, slip velocity arising from this surface hopping mechanism has been estimated to be significantly lower than that observed experimentally. In this paper, we propose a re-adsorption mechanism for fluid slip. Slip velocity predictions via this mechanism show the improved agreement with experimental measurements

Deterministic Constructions of Binary Measurement Matrices from Finite Geometry

Deterministic constructions of measurement matrices in compressed sensing (CS) are considered in this paper. The constructions are inspired by the recent discovery of Dimakis, Smarandache and Vontobel which says that parity-check matrices of good low-density parity-check (LDPC) codes can be used as {provably} good measurement matrices for compressed sensing under $\ell_1$-minimization. The performance of the proposed binary measurement matrices is mainly theoretically analyzed with the help of the analyzing methods and results from (finite geometry) LDPC codes. Particularly, several lower bounds of the spark (i.e., the smallest number of columns that are linearly dependent, which totally characterizes the recovery performance of $\ell_0$-minimization) of general binary matrices and finite geometry matrices are obtained and they improve the previously known results in most cases. Simulation results show that the proposed matrices perform comparably to, sometimes even better than, the corresponding Gaussian random matrices. Moreover, the proposed matrices are sparse, binary, and most of them have cyclic or quasi-cyclic structure, which will make the hardware realization convenient and easy.Comment: 12 pages, 11 figure