Nare Gabrielyan PhD

HIGHLY ACCESSED

The current paper is devoted to the fabrication and optimisation of ZnO nanowire (ZnONW) arrays for electrochemical glucose biosensor fabrication. The ZnO nanowires were fabricated by a two-step combination method. This includes radio-frequency (RF) sputtering of the ZnO seeding layer and hydrothermal growth of the nanowires in a solution containing zinc nitrate hexahydrate. Glucose oxidase has been immobilised on the nanowires, for use as the biorecognition molecule. The sensing… Mehr anzeigen

The current paper is devoted to the fabrication and optimisation of ZnO nanowire (ZnONW) arrays for electrochemical glucose biosensor fabrication. The ZnO nanowires were fabricated by a two-step combination method. This includes radio-frequency (RF) sputtering of the ZnO seeding layer and hydrothermal growth of the nanowires in a solution containing zinc nitrate hexahydrate. Glucose oxidase has been immobilised on the nanowires, for use as the biorecognition molecule. The sensing characteristics of the biosensors based on this fabrication methodology were investigated in phosphate buffer solution using electrochemical techniques. Weniger anzeigen

Zinc oxide (ZnO) nanowires have been widely investigated and various different methods of their synthesis have been suggested. This work is devoted to the optimisation of the growth conditions for uniform and evenly distributed ZnO nanowire arrays. The nanowire growth process includes two steps: 1. Radio-frequency (RF) magnetron sputtering of a ZnO nucleation layer onto a substrate; 2. A hydrothermal growth step of ZnO nanowires using the aforementioned sputtered layer as a template. The… Mehr anzeigen

Zinc oxide (ZnO) nanowires have been widely investigated and various different methods of their synthesis have been suggested. This work is devoted to the optimisation of the growth conditions for uniform and evenly distributed ZnO nanowire arrays. The nanowire growth process includes two steps: 1. Radio-frequency (RF) magnetron sputtering of a ZnO nucleation layer onto a substrate; 2. A hydrothermal growth step of ZnO nanowires using the aforementioned sputtered layer as a template. The optimisation process was divided into two sets of experiments: (i) the deposition of different thicknesses of the ZnO nucleation layer and the subsequent nanowire growth step (using the same conditions) for each thickness. The results revealed a strong dependence of the nanowire size upon the seed layer thickness and structural properties; (ii) the second set of experiments were based on growth solution temperature variation for the nucleation layers of the same thicknesses. This also showed nanowire size and distribution change with solution temperature variation. © 2010 Materials Research Society. Weniger anzeigen

We present a new Bayesian methodology to learn the unknown material density of a given sample by inverting its two-dimensional images that are taken with a Scanning Electron Microscope. An image results from a sequence of projections of the convolution of the density function with the unknown microscopy correction function that we also learn from the data. We invoke a novel design of experiment, involving imaging at multiple values of the parameter that controls the sub-surface depth from which… Mehr anzeigen

We present a new Bayesian methodology to learn the unknown material density of a given sample by inverting its two-dimensional images that are taken with a Scanning Electron Microscope. An image results from a sequence of projections of the convolution of the density function with the unknown microscopy correction function that we also learn from the data. We invoke a novel design of experiment, involving imaging at multiple values of the parameter that controls the sub-surface depth from which information about the density structure is carried, to result in the image. Real-life material density functions are characterised by high density contrasts and typically are highly discontinuous, implying that they exhibit correlation structures that do not vary smoothly. In the absence of training data, modelling such correlation structures of real material density functions is not possible. So we discretise the material sample and treat values of the density function at chosen locations inside it as independent and distribution-free parameters. Resolution of the available image dictates the discretisation length of the model; three models pertaining to distinct resolution classes are developed. We develop priors on the material density, such that these priors adapt to the sparsity inherent in the density function. The likelihood is defined in terms of the distance between the convolution of the unknown functions and the image data. The posterior probability density of the unknowns given the data is expressed using the developed priors on the density and priors on the microscopy correction function as elicitated from the Microscopy literature. We achieve posterior samples using an adaptive Metropolis-within-Gibbs inference scheme. The method is applied to learn the material density of a 3-D sample of a real nano-structure and of simulated alloy samples. Weniger anzeigen

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