Optimization of microcavity OLED by varying the thickness of multi-layered mirror

We optimized the emission efficiency from a microcavity OLEDs consisting of widely used organic materials, N,N'-di(naphthalene-1-yl)-N,N'-diphenylbenzidine (NPB) as a hole transport layer and tris (8-hydroxyquinoline) (Alq(3)) as emitting and electron transporting layer. LiF/Al was considered as a cathode, while metallic Ag anode was used. TiO2 and Al2O3 layers were stacked on top of the cathode to alter the properties of the top mirror. The electroluminescence emission spectra, electric field distribution inside the device, carrier density, recombination rate and exciton density were calculated as a function of the position of the emission layer. The results show that for certain TiO2 and Al2O3 layer thicknesses, light output is enhanced as a result of the increase in both the reflectance and transmittance of the top mirror. Once the optimum structure has been determined, the microcavity OLED devices can be fabricated and characterized, and comparisons between experiments and theory can be made.

A distributed equivalent magnetic current based FDTD method for the calculation of E-fields induced by gradient coils

This paper evaluates a new, low-frequency finite-difference time-domain method applied to the problem of induced E-fields/eddy currents in the human body resulting from the pulsed magnetic field gradients in MRI. In this algorithm, a distributed equivalent magnetic current is proposed as the electromagnetic source and is obtained by quasistatic calculation of the empty coil's vector potential or measurements therein. This technique circumvents the discretization of complicated gradient coil geometries into a mesh of Yee cells, and thereby enables any type of gradient coil modelling or other complex low frequency sources. The proposed method has been verified against an example with an analytical solution. Results are presented showing the spatial distribution of gradient-induced electric fields in a multi-layered spherical phantom model and a complete body model. (C) 2004 Elsevier Inc. All rights reserved.

Emergent anisotropy and flow alignment in viscous rock

A novel class of nonlinear, visco-elastic rheologies has recently been developed by MUHLHAUS et al. (2002a, b). The theory was originally developed for the simulation of large deformation processes including folding and kinking in multi-layered visco-elastic rock. The orientation of the layer surfaces or slip planes in the context of crystallographic slip is determined by the normal vector the so-called director of these surfaces. Here the model (MUHLHAUS et al., 2002a, b) is generalized to include thermal effects; it is shown that in 2-D steady states the director is given by the gradient of the flow potential. The model is applied to anisotropic simple shear where the directors are initially parallel to the shear direction. The relative effects of textural hardening and thermal softening are demonstrated. We then turn to natural convection and compare the time evolution and approximately steady states of isotropic and anisotropic convection for a Rayleigh number Ra=5.64x10(5) for aspect ratios of the experimental domain of 1 and 2, respectively. The isotropic case has a simple steady-state solution, whereas in the orthotropic convection model patterns evolve continuously in the core of the convection cell, which makes only a near-steady condition possible. This near-steady state condition shows well aligned boundary layers, and the number of convection cells which develop appears to be reduced in the orthotropic case. At the moderate Rayleigh numbers explored here we found only minor influences in the change from aspect ratio one to two in the model domain.

Tracking the states of a nonlinear system in the weight-space of a feed-forward neural network

Nonlinear, non-stationary signals are commonly found in a variety of disciplines such as biology, medicine, geology and financial modeling. The complexity (e.g. nonlinearity and non-stationarity) of such signals and their low signal to noise ratios often make it a challenging task to use them in critical applications. In this paper we propose a new neural network based technique to address those problems. We show that a feed forward, multi-layered neural network can conveniently capture the states of a nonlinear system in its connection weight-space, after a process of supervised training. The performance of the proposed method is investigated via computer simulations.

Anisotropy model for mantle convection

The paper presents a new theory for modeling flow in anisotropic, viscous rock. This theory has originally been developed for the simulation of large deformation processes including folding and kinking in multi-layered visco-elastic rock. The orientation of slip planes in the context of crystallographic slip is determined by the normal vector, the so-called director of these surfaces. The model is applied to simulate anisotropic natural mantle convection. We compare the evolution of the director and approximately steady states of isotropic and anisotropic convection. The isotropic case has a simple steady state solution, whereas the orthotropic convection model produces a continuously evolving patterning in tile core of the convection cell which makes only a near-steady condition possible, in which the thermal boundary layer appears to be well aligned with the flow and hence as observed in seismic tomomgraphy strong anistropic.