Probing Compositional Variation within Hybrid Nanostructures

We present a detailed analysis of the structural and magnetic properties of solution-grown PtCo-CdS hybrid structures in comparison to similar free-standing PtCo alloy nanoparticles. X-ray absorption spectroscopy is utilized as a sensitive probe for identifying subtle differences in the structure of the hybrid materials. We found that the growth of bimetallic tips on a CdS nanorod substrate leads to a more complex nanoparticle structure composed of a PtCo alloy core and thin CoO shell. The core-shell architecture is an unexpected consequence of the different nanoparticle growth mechanism on the nanorod tip, as compared to free growth in solution. Magnetic measurements indicate that the PtCo-CdS hybrid structures are superparamagnetic despite the presence of a CoO shell. The use of X-ray spectroscopic techniques to detect minute differences in atomic structure and bonding in complex nanosystems makes it possible to better understand and predict catalytic or magnetic properties for nanoscale bimetallic hybrid materials

Silver ion mediated shape control of platinum nanoparticles: Removal of silver by selective etching leads to increased catalytic activity

A procedure has been developed for the selective etching of Ag from Pt nanoparticles of well-defined shape, resulting in the formation of elementally-pure Pt cubes, cuboctahedra, or octahedra, with a largest vertex-to-vertex distance of {approx}9.5 nm from Ag-modified Pt nanoparticles. A nitric acid etching process was applied Pt nanoparticles supported on mesoporous silica, as well as nanoparticles dispersed in aqueous solution. The characterization of the silica-supported particles by XRD, TEM, and N{sub 2} adsorption measurements demonstrated that the structure of the nanoparticles and the mesoporous support remained conserved during etching in concentrated nitric acid. Both elemental analysis and ethylene hydrogenation indicated etching of Ag is only effective when [HNO{sub 3}] {ge} 7 M; below this concentration, the removal of Ag is only {approx}10%. Ethylene hydrogenation activity increased by four orders of magnitude after the etching of Pt octahedra that contained the highest fraction of silver. High-resolution transmission electron microscopy of the unsupported particles after etching demonstrated that etching does not alter the surface structure of the Pt nanoparticles. High [HNO{sub 3}] led to the decomposition of the capping agent, polyvinylpyrollidone (PVP); infrared spectroscopy confirmed that many decomposition products were present on the surface during etching, including carbon monoxide

Near-Monodisperse Ni-Cu Bimetallic Nanocrystals of Variable Composition: Controlled Synthesis and Catalytic Activity for H2 Generation

Near-monodisperse Ni{sub 1-x}Cu{sub x} (x = 0.2-0.8) bimetallic nanocrystals were synthesized by a one-pot thermolysis approach in oleylamine/1-octadecene, using metal acetylacetonates as precursors. The nanocrystals form large-area 2D superlattices, and display a catalytic synergistic effect in the hydrolysis of NaBH{sub 4} to generate H{sub 2} at x = 0.5 in a strongly basic medium. The Ni{sub 0.5}Cu{sub 0.5} nanocrystals show the lowest activation energy, and also exhibit the highest H{sub 2} generation rate at 298 K

Effect of perinatal adversity on structural connectivity of the developing brain

Globally, preterm birth (defined as birth at <37 weeks of gestation) affects around 11% of deliveries and it is closely associated with cerebral palsy, cognitive impairments and neuropsychiatric diseases in later life. Magnetic Resonance Imaging (MRI) has utility for measuring different properties of the brain during the lifespan. Specially, diffusion MRI has been used in the neonatal period to quantify the effect of preterm birth on white matter structure, which enables inference about brain development and injury. By combining information from both structural and diffusion MRI, is it possible to calculate structural connectivity of the brain. This involves calculating a model of the brain as a network to extract features of interest. The process starts by defining a series of nodes (anatomical regions) and edges (connections between two anatomical regions). Once the network is created, different types of analysis can be performed to find features of interest, thereby allowing group wise comparisons. The main frameworks/tools designed to construct the brain connectome have been developed and tested in the adult human brain. There are several differences between the adult and the neonatal brain: marked variation in head size and shape, maturational processes leading to changes in signal intensity profiles, relatively lower spatial resolution, and lower contrast between tissue classes in the T1 weighted image. All of these issues make the standard processes to construct the brain connectome very challenging to apply in the neonatal population. Several groups have studied the neonatal structural connectivity proposing several alternatives to overcome these limitations. The aim of this thesis was to optimise the different steps involved in connectome analysis for neonatal data. First, to provide accurate parcellation of the cortex a new atlas was created based on a control population of term infants; this was achieved by propagating the atlas from an adult atlas through intermediate childhood spatio-temporal atlases using image registration. After this the advanced anatomically-constrained tractography framework was adapted for the neonatal population, refined using software tools for skull-stripping, tissue segmentation and parcellation specially designed and tested for the neonatal brain. Finally, the method was used to test the effect of early nutrition, specifically breast milk exposure, on structural connectivity in preterm infants. We found that infants with higher exposure to breastmilk in the weeks after preterm birth had improved structural connectivity of developing networks and greater fractional anisotropy in major white matter fasciculi. These data also show that the benefits are dose dependent with higher exposure correlating with increased white matter connectivity. In conclusion, structural connectivity is a robust method to investigate the developing human brain. We propose an optimised framework for the neonatal brain, designed for our data and using tools developed for the neonatal brain, and apply it to test the effect of breastmilk exposure on preterm infants