Formation of an extended CoSi2 thin nanohexagons array coherently buried in silicon single crystal

A Co-doped silica film was deposited on the surface of a Si(100) wafer and isothermally annealed at 750 degrees C to form spherical Co nanoparticles embedded in the silica film and a few atomic layer thick CoSi2 nanoplatelets within the wafer. The structure, morphology, and spatial orientation of the nanoplatelets were characterized. The experimental results indicate that the nanoplatelets exhibit hexagonal shape and a uniform thickness. The CoSi2 nanostructures lattice is coherent with the Si lattice, and each of them is parallel to one of the four planes belonging to the {111} crystallographic form of the host lattice. (C) 2012 American Institute of Physics. [doi:10.1063/1.3683493]

Investigation into the enhancement of polycarbonate with conductive nanomaterials

Polymers are typically electrically and thermally insulating materials. The electrical and thermal conductivities of polymers can be increased by the addition conductive fillers such as carbons. Once the polymer composites have been made electrically and thermally conductive, they can be used in applications where these conductivities are desired such as electromagnetic shielding and static dissipation. In this project, three carbon nanomaterials are added to polycarbonate to enhance the electrical and thermal conductivity of the resulting composite. Hyperion Catalysis FIBRILs carbon nanotubes were added to a maximum loading of 8 wt%. Ketjenblack EC-600 JD carbon black was added to a maximum loading of 10 wt%. XG Sciences xGnP™ graphene nanoplatelets were added to a maximum loading of 15 wt%. These three materials have drastically different morphologies and will have varying effects on the various properties of polycarbonate composites. It was determined that carbon nanotubes have the largest effect on electrical conductivity with an 8 wt% carbon nanotube in polycarbonate composite having an electrical conductivity of 0.128 S/cm (from a pure polycarbonate value of 10-17 S/cm). Carbon black has the next largest effect with an 8 wt% carbon black in polycarbonate composite having an electrical conductivity of 0.008 S/cm. Graphene nanoplatelets have the least effect with an 8 wt% graphene nanoplatelet in polycarbonate having an electrical conductivity of 2.53 x 10-8 S/cm. Graphene nanoplatelets show a significantly higher effect on increasing thermal conductivity than either carbon nanotubes or carbon black. Mechanically, all three materials have similar effects with graphene nanoplatelets being somewhat more effective at increasing the tensile modulus of the composite than the other fillers. Carbon black and graphene nanoplatelets show standard carbon-filler rheology where the addition of filler increases the viscosity of the resulting composite. Carbon nanotubes, on the other hand, show an unexpected rheology. As carbon nanotubes are added to polycarbonate the viscosity of the composite is reduced below that of the original polycarbonate. It was seen that the addition of carbon nanotubes offsets the increased viscosity from a second filler, such as carbon black or graphene nanoplatelets.

Oxygen evolution at ultrathin nanostructured Ni(OH)2 layers deposited on conducting glass

Ultrathin and transparent nanostructured Ni(OH)2 films were deposited on conducting glass (F:SnO2) by a urea-based chemical bath deposition method. By controlling the deposition time, the amount of deposited Ni(OH)2 was varied over 7 orders of magnitude. The turnover number for O2 generation, defined as the number of O2 molecules generated per catalytic site (Ni atom) and per second, increases drastically as the electrocatalyst amount decreases. The electrocatalytic activity of the studied samples (measured as the current density at a certain potential) increases with the amount of deposited Ni(OH)2 until a saturation value is already obtained for a thin film of around 1 nm in thickness, composed of Ni(OH)2 nanoplatelets lying flat on the conductive support. The deposition of additional amounts of catalyst generates a porous honeycomb structure that does not improve (only maintains) the electrocatalytic activity. The optimized ultrathin electrodes show a remarkable stability, which indicates that the preparation of highly transparent electrodes, efficient for oxygen evolution, with a minimum amount of nickel is possible.

A Comparison Between Graphene and WS2 as Solid Lubricant Additives to Aluminum for Automobile Applications

The purpose of this thesis was to compare graphene nanoplatelets (GNP) and WS2 as solid lubricant additives to aluminum in order to reduce friction and wear. The central hypothesis of this work relied on lubricating properties of 2D materials, which consist layers that slip under a shear force. Two aluminum composites were made (Al-2 vol.% GNP and Al-2 vol.% WS2) by spark plasma sintering. Tribological properties were evaluated by ball-on-disk wear tests at room temperature (RT) and 200°C. WS2 not only presented the lowest COF (0.66) but also improved the wear resistance of aluminum by 54% at RT. Al-2 vol.% GNP composite displayed poor densification (91%) and low hardness resulting in poor wear resistance. The wear rate of Al-2 vol.% GNP composite increased by 233% at RT and 48% at 200°C as compared to pure aluminum. GNP addition also resulted in lower COF (0.79) as compared to pure aluminum (0.87).

Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks

The focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP) were processed in a twin screw extruder with three different screw speeds and feeder speeds (50, 100, and 150 rpm). These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. The tensile strength reached a maximum value at 4 wt% C-GNP and a speed of 150 rpm as this represented the optimum condition for the stress transfer through the amorphous chains of the matrix to the C-GNP. ANN can be confidently used as a tool to predict the above material properties before investing in development programs and actual manufacturing, thus significantly saving money, time, and effort.

MOLECULAR MODELING OF EPON 862-DETDA / CARBON COMPOSITES

The thermoset epoxy resin EPON 862, coupled with the DETDA hardening agent, are utilized as the polymer matrix component in many graphite (carbon fiber) composites. Because it is difficult to experimentally characterize the interfacial region, computational molecular modeling is a necessary tool for understanding the influence of the interfacial molecular structure on bulk-level material properties. The purpose of this research is to investigate the many possible variables that may influence the interfacial structure and the effect they will have on the mechanical behavior of the bulk level composite. Molecular models are established for EPON 862-DETDA polymer in the presence of a graphite surface. Material characteristics such as polymer mass-density, residual stresses, and molecular potential energy are investigated near the polymer/fiber interface. Because the exact degree of crosslinking in these thermoset systems is not known, many different crosslink densities (degrees of curing) are investigated. It is determined that a region exists near the carbon fiber surface in which the polymer mass density is different than that of the bulk mass density. These surface effects extend ~10 Å into the polymer from the center of the outermost graphite layer. Early simulations predict polymer residual stress levels to be higher near the graphite surface. It is also seen that the molecular potential energy in polymer atoms decreases with increasing crosslink density. New models are then established in order to investigate the interface between EPON 862-DETDA polymer and graphene nanoplatelets (GNPs) of various atomic thicknesses. Mechanical properties are extracted from the models using Molecular Dynamics techniques. These properties are then implemented into micromechanics software that utilizes the generalized method of cells to create representations of macro-scale composites. Micromechanics models are created representing GNP doped epoxy with varying number of graphene layers and interfacial polymer crosslink densities. The initial micromechanics results for the GNP doped epoxy are then taken to represent the matrix component and are re-run through the micromechanics software with the addition of a carbon fiber to simulate a GNP doped epoxy/carbon fiber composite. Micromechanics results agree well with experimental data, and indicate GNPs of 1 to 2 atomic layers to be highly favorable. The effect of oxygen bonded to the surface of the GNPs is lastly investigated. Molecular Models are created for systems with varying graphene atomic thickness, along with different amounts of oxygen species attached to them. Models are created for graphene containing hydroxyl groups only, epoxide groups only, and a combination of epoxide and hydroxyl groups. Results show models of oxidized graphene to decrease in both tensile and shear modulus. Attaching only epoxide groups gives the best results for mechanical properties, though pristine graphene is still favored.