The effect of processing route on properties of HfNbTaTiZr high entropy alloy

High entropy alloys (HEA) have been one of the most attractive groups of materials for researchers in the last several years. Since HEAs are potential candidates for many (e.g., refractory, cryogenic, medical) applications, their properties are studied intensively. The most frequent method of HEA synthesis is arc or induction melting. Powder metallurgy is a perspective technique of alloy synthesis and therefore in this work the possibilities of synthesis of HfNbTaTiZr HEA from powders were studied. Blended elemental powders were sintered, hot isostatically pressed, and subsequently swaged using a special technique of swaging where the sample is enveloped by a titanium alloy. This method does not result in a full density alloy due to cracking during swaging. Spark plasma sintering (SPS) of mechanically alloyed powders resulted in a fully dense but brittle specimen. The most promising result was obtained by SPS treatment of gas atomized powder with low oxygen content. The microstructure of HfNbTaTiZr specimen prepared this way can be refined by high pressure torsion deformation resulting in a high hardness of 410 HV10 and very fine microstructure with grain size well below 500 nm.Web of Science1223art. no. 402

DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling

We propose DoPAMINE, a new neural network based multiplicative noise despeckling algorithm. Our algorithm is inspired by Neural AIDE (N-AIDE), which is a recently proposed neural adaptive image denoiser. While the original N-AIDE was designed for the additive noise case, we show that the same framework, i.e., adaptively learning a network for pixel-wise affine denoisers by minimizing an unbiased estimate of MSE, can be applied to the multiplicative noise case as well. Moreover, we derive a double-sided masked CNN architecture which can control the variance of the activation values in each layer and converge fast to high denoising performance during supervised training. In the experimental results, we show our DoPAMINE possesses high adaptivity via fine-tuning the network parameters based on the given noisy image and achieves significantly better despeckling results compared to SAR-DRN, a state-of-the-art CNN-based algorithm.Comment: AAAI 2019 Camera Ready Versio

An optical NMR spectrometer for Larmor-beat detection and high-resolution POWER NMR

Optical nuclear magnetic resonance (ONMR) is a powerful probe of electronic properties in III-V semiconductors. Larmor-beat detection (LBD) is a sensitivity optimized, time-domain NMR version of optical detection based on the Hanle effect. Combining LBD ONMR with the line-narrowing method of POWER (perturbations observed with enhanced resolution) NMR further enables atomically detailed views of local electronic features in III-Vs. POWER NMR spectra display the distribution of resonance shifts or line splittings introduced by a perturbation, such as optical excitation or application of an electric field, that is synchronized with a NMR multiple-pulse time-suspension sequence. Meanwhile, ONMR provides the requisite sensitivity and spatial selectivity to isolate local signals within macroscopic samples. Optical NMR, LBD, and the POWER method each introduce unique demands on instrumentation. Here, we detail the design and implementation of our system, including cryogenic, optical, and radio-frequency components. The result is a flexible, low-cost system with important applications in semiconductor electronics and spin physics. We also demonstrate the performance of our systems with high-resolution ONMR spectra of an epitaxial AlGaAs/GaAs heterojunction. NMR linewidths down to 4.1 Hz full width at half maximum were obtained, a 10^3-fold resolution enhancement relative any previous optically detected NMR experiment

Control of the plasmonic resonance of a graphene coated plasmonic nanoparticle array combined with a nematic liquid crystal

We report on the fabrication and characterization of a switchable plasmonic device based on a conductive graphene oxide (cGO) coated plasmonic nanoparticle (NP) array, layered with nematic liquid crystal (NLC) as an active medium. A monolayer of NPs has been immobilized on a glass substrate through electrostatic interaction, and then grown in place using nanochemistry. This monolayer is then coated with a thin (less then 100nm) cGO film which acts simultaneously as both an electro-conductive and active medium. The combination of the conductive NP array with a separate top cover substrate having both cGO and a standard LC alignment layer is used for aligning a NLC film in a hybrid configuration. The system is analysed in terms of morphological and electro-optical properties. The spectral response of the sample characterized after each element is added (air, cGO, NLC) reveals a red-shift of the localized plasmonic resonance (LPR) frequency of approximately 62nm with respect to the NP array surrounded by air. The application of an external voltage (8Vpp) is suitable to modulate (blue shift) the LPR frequency by approximately 22nm

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Preparation-microstructure-property relationships in double-walled carbon nanotubes/alumina composites

Double-walled carbon nanotube/alumina composite powders with low carbon contents (2– 3 wt.%) are prepared using three different methods and densified by spark plasma sintering. The mechanical properties and electrical conductivity are investigated and correlated with the microstructure of the dense materials. Samples prepared by in situ synthesis of carbon nanotubes (CNTs) in impregnated submicronic alumina are highly homogeneous and present the higher electrical conductivity (2.2–3.5 Scm-1) but carbon films at grain boundaries induce a poor cohesion of the materials. Composites prepared by mixing using moderate sonication of as-prepared double-walled CNTs and lyophilisation, with little damage to the CNTs, have a fracture strength higher (+30%) and a fracture toughness similar (5.6 vs 5.4 MPa m1/2) to alumina with a similar submicronic grain size. This is correlated with crack-bridging by CNTs on a large scale, despite a lack of homogeneity of the CNT distribution