Exact eigenstate analysis of finite-frequency conductivity in graphene

We employ the exact eigenstate basis formalism to study electrical conductivity in graphene, in the presence of short-range diagonal disorder and inter-valley scattering. We find that for disorder strength, $W \ge$ 5, the density of states is flat. We, then, make connection, using the MRG approach, with the work of Abrahams \textit{et al.} and find a very good agreement for disorder strength, $W$ = 5. For low disorder strength, $W$ = 2, we plot the energy-resolved current matrix elements squared for different locations of the Fermi energy from the band centre. We find that the states close to the band centre are more extended and falls of nearly as $1/E_l^{2}$ as we move away from the band centre. Further studies of current matrix elements versus disorder strength suggests a cross-over from weakly localized to a very weakly localized system. We calculate conductivity using Kubo Greenwood formula and show that, for low disorder strength, conductivity is in a good qualitative agreement with the experiments, even for the on-site disorder. The intensity plots of the eigenstates also reveal clear signatures of puddle formation for very small carrier concentration. We also make comparison with square lattice and find that graphene is more easily localized when subject to disorder.Comment: 11 pages,15 figure

Magnonic Metamaterials

A large proportion of the recent growth of the volume of electromagnetics research has been associated with the emergence of so called electromagnetic metamaterials1 and the discovered ability to design their unusual properties by tweaking the geometry and structure of the constituent “meta-atoms”. For example, negative permittivity and negative permeability can be achieved, leading to negative refractive index metamaterials. The negative permeability could be obtained via geometrical control of high frequency currents, e.g. in arrays of split ring resonators, or alternatively one could rely on spin resonances in natural magnetic materials, as was suggested by Veselago. The age of nanotechnology therefore sets an intriguing quest for additional benefits to be gained by structuring natural magnetic materials into so called magnonic metamaterials, in which the frequency and strength of resonances based on spin waves (magnons) are determined by the geometry and magnetization configuration of meta-atoms. Spin waves can have frequencies of up to hundreds of GHz (in the exchange dominated regime) and have already been shown to play an important role in the high frequency magnetic response of composites. Moreover, in view of the rapid advances in the field of magnonics, which in particular promises devices employing propagating spin waves, the appropriate design of magnonic metamaterials with properties defined with respect to propagating spin waves rather than electromagnetic waves acquires an independent and significant importance

Electronic and hole minibands in quantum wire arrays of different crystallographic structure

We consider quantum wire arrays consisting of GaAs rods embedded in Al$_{x}$Ga$_{1-x}$As and disposed in sites of a square or triangular lattice. The electronic and hole spectra around the conduction band bottom and the valence band top are examined versus geometry of the lattice formed by the rods, concentration of Al in the matrix material, and structural parameters including the filling fraction and the lattice constant. Our calculations use the envelope function and are based on the effective-mass approximation. We show that the electronic and hole spectra resulting from the periodicity of the heterostructure, depend on the factors considered and that the effect of lattice geometry varies substantially with lattice constant. For low lattice constant values the minigaps are significantly wider in the case of triangular lattice, while for high lattice constant values wider minigaps occur in the square lattice-based arrays. We analyse the consequences of our findings for the efficiency of solar cells based on quantum wire arrays.Comment: 20 pages, 10 figure

Spin wave dispersion in permalloy antidot array with alternating holes diameter

10.1109/TMAG.2013.2240659IEEE Transactions on Magnetics4973093-3096IEMG

Overconfidence and trading volume

Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors. Approximately 3,000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, volatility estimates, better than average effect). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance (but who did not have above average performance in the past) trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. In connection with other recent findings, we conclude that the usual way of motivating and modeling overconfidence which is mainly based on the calibration literature has to be treated with caution. Moreover, our way of empirically evaluating behavioral finance models—the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data—seems to be a promising way to better understand which psychological phenomena actually drive economic behavior. Copyright The Geneva Association 2007Overconfidence, Differences of opinion, Trading volume, Individual investors, Investor behavior, Correlation of economic and psychological variables, Combination of psychometric measures of judgment biases and field data, D8, G1,