OPTIMIZATION OF THE SLOW-MODE PLASMON POLARITON IN LIGHT-EMITTING TUNNEL-JUNCTIONS

Light emitted from metal/oxide/metal tunnel junctions can originate from the slow-mode surface plasmon polariton supported in the oxide interface region. The effective radiative decay of this mode is constrained by competition with heavy intrinsic damping and by the need to scatter from very small scale surface roughness; the latter requirement arises from the mode's low phase velocity and the usual momentum conservation condition in the scattering process. Computational analysis of conventional devices shows that the desirable goals of decreased intrinsic damping and increased phase velocity are influenced, in order of priority, by the thickness and dielectric function of the oxide layer, the type of metal chosen for each conducting electrode, and temperature. Realizable devices supporting an optimized slow-mode plasmon polariton are suggested. Essentially these consist of thin metal electrodes separated by a dielectric layer which acts as a very thin (a few nm) electron tunneling barrier but a relatively thick (several 10's of nm) optically lossless region. (C) 1995 American Institute of Physics.

CALCULATION OF SLOW MODE SURFACE-PLASMON POLARITON PROPERTIES RELATED TO EXPERIMENTAL-OBSERVATIONS

Recent experimental results definitively showed, for the first time, optical radiation mediated by the slow mode surface plasmon polariton of metal-oxide-metal tunnel junctions. Here, dispersion curves for this mode are calculated. They are consistent with first-order grating coupling to light at the energies of the experimental emission peaks. The curves are then used to analyze second-order and high-energy (> 2.35 eV) grating coupling of the polaritons to radiation. Finally, variation of slow mode damping as a function of energy is used to explain qualitatively the relative experimental peak emission intensities and the absence of radiation peaks above 2.35 eV.

Optimisation of a two-liquid component pre-filled acrylic bone cement system: A design of experiments approach to optimise cement final properties

The initial composition of acrylic bone cement along with the mixing and delivery technique used can influence its final properties and therefore its clinical success in vivo. The polymerisation of acrylic bone cement is complex with a number of processes happening simultaneously. Acrylic bone cement mixing and delivery systems have undergone several design changes in their advancement, although the cement constituents themselves have remained unchanged since they were first used. This study was conducted to determine the factors that had the greatest effect on the final properties of acrylic bone cement using a pre-filled bone cement mixing and delivery system. A design of experiments (DoE) approach was used to determine the impact of the factors associated with this mixing and delivery method on the final properties of the cement produced. The DoE illustrated that all factors present within this study had a significant impact on the final properties of the cement. An optimum cement composition was hypothesised and tested. This optimum recipe produced cement with final mechanical and thermal properties within the clinical guidelines and stated by ISO 5833 (International Standard Organisation (ISO), International standard 5833: implants for surgery—acrylic resin cements, 2002), however the low setting times observed would not be clinically viable and could result in complications during the surgical technique. As a result further development would be required to improve the setting time of the cement in order for it to be deemed suitable for use in total joint replacement surgery.

Mapping neutral, ion and electron number densities within laser ablated plasma plumes

Spatially and temporally varying neutral, ion and electron number densities have been mapped out within laser ablated plasma plumes expanding into vacuum. Ablation of a magnesium target was performed using a KrF laser, 30 ns pulse duration and 248 nm wavelength. During the initial stage of plasma expansion (t <EQ 100 ns) interferometry has been used to obtain line averaged electron number densities, for laser power densities on target in the range 1.3 - 3.0 X 108 W/cm2. Later in the plasma expansion (t equals 1 microsecond(s) ) simultaneous absorption and laser induced fluorescence spectroscopy has been used to determine 3D neutral and ion number densities, for a power density equal to 6.7 X 107 W/cm2. Two distinct regions within the plume were identified. One is a fast component (approximately 106 cm-1) consisting of ions and neutrals with maximum number densities observed to be approximately 30 and 4 X 1012 cm-3 respectively, and the second consists of slow moving neutral material at a number density of up to 1015 cm-3. Additionally a Langmuir probe has been used to obtain ion and electron number densities at very late times in the plasma expansion (1 microsecond(s) <EQ t <EQ 15 microsecond(s) ). A copper target was ablated using a Nd:YAG laser, 7.5 ns duration and 532 nm (2 (omega) ) wavelength, with a power density on target equal to 6 X 108 W/cm2. Two regions within the plume with different velocities were observed. Within a fast component (approximately 3 X 106 cms-1) electron and ion number densities of the order 5 X 1012 cm-3 were observed and within the second slower component (approximately 106 cms-1) electron and ion number densities of the order 1 - 2 X 1013 cm-3 were determined.

Energy and energy flux in axisymmetric slow and fast waves

Aims: We aim to calculate the kinetic, magnetic, thermal, and total energy densities and the flux of energy in axisymmetric sausage modes. The resulting equations should contain as few parameters as possible to facilitate applicability for different observations. 

Methods: The background equilibrium is a one-dimensional cylindrical flux tube model with a piecewise constant radial density profile. This enables us to use linearised magnetohydrodynamic equations to calculate the energy densities and the flux of energy for axisymmetric sausage modes. 

Results: The equations used to calculate the energy densities and the flux of energy in axisymmetric sausage modes depend on the radius of the flux tube, the equilibrium sound and Alfvén speeds, the density of the plasma, the period and phase speed of the wave, and the radial or longitudinal components of the Lagrangian displacement at the flux tube boundary. Approximate relations for limiting cases of propagating slow and fast sausage modes are also obtained. We also obtained the dispersive first-order correction term to the phase speed for both the fundamental slow body mode under coronal conditions and the slow surface mode under photospheric conditions.

Massive stars exploding in a He-rich circumstellar medium - V. Observations of the slow-evolving SN Ibn OGLE-2012-SN-006

We present optical observations of the peculiar Type Ibn supernova (SN Ibn) OGLE-2012-SN-006, discovered and monitored by the Optical Gravitational Lensing Experiment-IV survey, and spectroscopically followed by Public ESO Spectroscopic Survey of Transient Objects (PESSTO) at late phases. Stringent pre-discovery limits constrain the explosion epoch with fair precision to JD = 245 6203.8 +/- 4.0. The rise time to the I-band light-curve maximum is about two weeks. The object reaches the peak absolute magnitude M-I = -19.65 +/- 0.19 on JD = 245 6218.1 +/- 1.8. After maximum, the light curve declines for about 25 d with a rate of 4 mag (100 d)(-1). The symmetric I-band peak resembles that of canonical Type Ib/c supernovae (SNe), whereas SNe Ibn usually exhibit asymmetric and narrower early-time light curves. Since 25 d past maximum, the light curve flattens with a decline rate slower than that of the Co-56-Fe-56 decay, although at very late phases it steepens to approach that rate. However, other observables suggest that the match with the Co-56 decay rate is a mere coincidence, and the radioactive decay is not the main mechanism powering the light curve of OGLE-2012-SN-006. An early-time spectrum is dominated by a blue continuum, with only a marginal evidence for the presence of He I lines marking this SN type. This spectrum shows broad absorptions bluewards than 5000 angstrom, likely O II lines, which are similar to spectral features observed in superluminous SNe at early epochs. The object has been spectroscopically monitored by PESSTO from 90 to 180 d after peak, and these spectra show the typical features observed in a number of SN 2006jc-like events, including a blue spectral energy distribution and prominent and narrow (v(FWHM) approximate to 1900 km s(-1)) He I emission lines. This suggests that the ejecta are interacting with He-rich circumstellar material. The detection of broad (10(4) km s(-1)) O I and Ca II features likely produced in the SN ejecta (including the [OI] lambda lambda 6300,6364 doublet in the latest spectra) lends support to the interpretation of OGLE-2012-SN-006 as a core-collapse event.

Received Signal Characteristics of the Indoor Off-Body Communications Channel at 5.8 GHz

In this paper we investigate the received signal characteristics of a mobile chest-worn transmitter at 5.8 GHz within a high multipath indoor environment. The off-body channel measurements considered both the co- and cross-polarized received signal for both line-of-sight (LOS) and non-LOS (NLOS) conditions. A straightforward channel model based upon the estimated path loss, a lognormal slow fading component and Ricean small-scale fading contribution is developed and used to perform simulations which allow the generation of first order received signal power characteristics.

Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.