Comparison of a subrank to a full-rank time-reversal operator in a dynamic ocean

This paper investigates the application of time-reversal techniques to the detection and ensonification of a target of interest. The focusing method is based on a generalization of time-reversal operator techniques. A subrank time-reversal operator is derived and implemented using a discrete set of transmission beams to ensonify a region of interest. In a dynamic ocean simulation, target focusing using a subrank matrix is shown to be superior to using a full-rank matrix, specifically when the subrank matrix is captured in a period shorter than the coherence time of the modeled environment. Backscatter from the point target was propagated to a vertical 64-element source-receiver array and processed to form the sub-rank time-reversal operator matrix. The eigenvector corresponding to the strongest eigenvalue of the time-reversal operator was shown to focus energy on the target in simulation. Modeled results will be augmented by a limited at-sea experiment conducted on the New Jersey shelf in April-May 2004 measured low-frequency backscattered signal from an artificial target (echo repeater).     

Separation of enantiomers of ephedrine by capillary electrophoresis using cyclodextrins as chiral selectors: comparative CE, NMR and high resolution MS studies

The enantiomer migration order (EMO) of ephedrine was investigated in the presence of various CDs in CE. The molecular mechanisms of chiral recognition were followed for the ephedrine complexes with native α- and β-CD and heptakis(2,3-di-O-acetyl-6-O-sulfo)-β-CD (HDAS-β-CD) by CE, NMR spectroscopy and high-resolution MS. Minor structural differences were observed between the complexes of ephedrine with α- and β-CD although the migration order of enantiomers was opposite when these two CDs were applied as chiral selectors in CE. The EMO was also opposite between β-CD and HDAS-β-CD. Significant structural differences were observed between ephedrine complexes with the native CDs and HDAS-β-CD. The latter CD was advantageous as chiral CE selector not only due to its opposite electrophoretic mobility compared with that of the cationic chiral analyte, but also primarily due to its enhanced chiral recognition ability towards the enantiomers of ephedrine.     

Chemistry of 11-vertex rhodathiaboranes: reactions with monodentate phosphines

The reaction of [8,8-(PPh(3))(2)-nido-8,7-RhSB(9)H(10)] (1) with PR(3) in a 1:2 ratio affords mixtures that contain the mono-substituted bis-PR(3)-ligated rhodathiaboranes [8,8-(PPh(3))(L)-nido-8,7-RhSB(9)H(10)] [L = PMe(2)Ph (5), PMe(3) (6)] and the corresponding tris-PR(3)-ligated compounds [8,8,8-(L)(3)-nido-8,7-RhSB(9)H(10)] [L = PMe(2)Ph (7), PMe(3) (8)]. These latter species are more conveniently prepared from the reaction of 1 with three equivalents of the monodentate phosphines, PMe(2)Ph and PMe(3). Reaction between 1 and PMePh(2) in a 1:2 ratio yields the disubstituted rhodathiaborane [8,8-(PMePh(2))(2)-nido-8,7-RhSB(9)H(10)] (4), whereas the use of three equivalents of phosphine leads to the formation of B-ligated eleven-vertex [8,8,8-(PMePh(2))(2)(H)-nido-8,7-RhSB(9)H(9)-9-(PMePh(2))] (9). Compounds 4-9 have been characterized by NMR spectroscopy, and the structures of 8 and 9 confirmed by X-ray diffraction analyses. The characterization of the cluster compounds has been aided by the use of DFT calculations on some of the species. Variable-temperature NMR studies have demonstrated a lability of the PMePh(2) ligands in compounds 4 and 9, providing mechanistic insights about the ligand substitutional chemistry in these eleven-vertex rhodathiaboranes.     

Numerical methods for a fixed domain formulation of the glacier profile problem with alternative boundary conditions

In this paper we develop a set of numerical techniques for the simulation of the profile evolution of a valley glacier in the framework of isothermal shallow ice approximation models. The different mathematical formulations are given in terms of a highly nonlinear parabolic equation. A first nonlinearity comes from the free boundary problem associated with the unknown basal extension of the glacier region. This feature is treated using a fixed domain complementarity formulation which is solved numerically by a duality method. The nonlinear diffusive term is explicitly treated in the time marching scheme. A convection dominated problem arises, so a characteristic scheme is proposed for the time discretization, while piecewise linear finite elements are used for the spatial discretization. The presence of infinite slopes in polar regimes motivates an alternative formulation based on a prescribed flux boundary condition at the head of the glacier instead a homogeneous Dirichlet one. Finally, several numerical examples illustrate the performance of the proposed methods.     

Fast microwave-assisted synthesis of tailored mesoporous carbon xerogels

Resorcinol-formaldehyde carbon xerogels with several initial pH were synthesized using two different heating methods (conventional and microwave heating). The effect of the pH of the precursor solution and the method of synthesis employed on the textural and chemical properties of the final materials was evaluated. It was found that both methods produce tailored carbon xerogels depending on the initial pH and that the pores of the carbon xerogels become larger as the initial pH decreases. High pHs result in exclusively microporous carbon xerogels, while a decrease in the amount of NaOH added, i.e. lower pH, causes the materials to evolve firstly into micro-mesoporous samples and then into micro-macroporous carbon xerogels. The main difference between the two heating methods studied, apart from the duration of the synthesis (i.e. approximately 5 h for the microwave-assisted synthesis as opposed to several days by conventional methods) lies in the meso-macroporosity of the resulting materials, since microwave radiation produces mainly mesoporous carbon xerogels with a specific mesopore size over a wider range of pH than conventional synthesis. For example, the pH range for mesoporous MW samples is 4.5-6.5 while equivalent samples that are conventionally synthesized require an initial pH of between 5.8 and 6.5. This work also illustrates a simple and precise method for determining the gelation point (t(g)) of different pH resorcinol-formaldehyde mixtures, based on varying the energy consumed by the microwave device during the synthesis of organic gels, without the need for other more complicated techniques.     

Electrochemistry of Os(2,2'-bpy)2ClPyCH2NHCOPh tethered to Au electrodes by S-Au and C-Au junctions

Osmium pyridine-bipyridine redox centers have been tethered to Au electrodes by chemical modification through Au-S and Au-C bonds respectively. 4-Mercapto benzoic acid and the reduction product of the aryl diazonium salt of 4-amino benzoic acid were reacted on Au surfaces, with further post-functionalization by chemical reaction of the osmium complex amino-pyridine derivative with the surface carboxylates. The resulting modified Au surfaces were characterized by polarization modulated infrared reflection absorption spectroscopy (PM-IRRAS), scanning tunneling microscopy (STM), X-ray photoelectron spectroscopy (XPS), resonant raman spectroscopy and cyclic voltammetry.     

Modeling of a magnetorheological damper by recursive lazy learning

Nowadays dampers based on magnetorheological (MR) fluids are receiving significant attention specially for control of structural vibration and automotive suspensions systems. In most cases, it is necessary to develop an appropriate control strategy which is practically implementable when a suitable model for MR dampers is available. It is not a trivial task to model the dynamic of MR dampers because of their inherent non-linear and hysteretic dynamics. In this paper, a recursive lazy learning method based on neural networks is considered to model the MR damper behavior. The proposed method is validated by comparison with experimental obtained responses. Results show the estimated model correlates very well with the data obtained experimentally. The method proposed learns quickly that it is only necessarily a learning cycle, it can learn on-line and it is easy to select the network structure and calculate the model parameters.