Thermal shock resistance of air plasma sprayed thermal barrier coatings

The spallation resistance of an air plasma sprayed (APS) thermal barrier coating (TBC) to cool-down/reheat is evaluated for a pre-existing delamination crack. The delamination emanates from a vertical crack through the coating and resides at the interface between coating and underlying thermally grown oxide layer (TGO). The coating progressively sinters during engine operation, and this leads to a depth-dependent increase in modulus. Following high temperature exposure, the coating is subjected to a cooling/reheating cycle representative of engine shut-down and start-up. The interfacial stress intensity factors are calculated for the delamination crack over this thermal cycle and are compared with the mode-dependent fracture toughness of the interface between sintered APS and TGO. The study reveals the role played by microstructural evolution during sintering in dictating the spallation life of the thermal barrier coating, and also describes a test method for the measurement of delamination toughness of a thin coating. © 2014 Elsevier Ltd.

Vortex breaking and cutting in type II superconductors.

In technological superconductors, the Lorentz force on the flux vortices is opposed by inhomogeneous pinning and so the critical current may be controlled by a combination of vortex entanglement, cutting, and cross-joining. To understand the roles of these processes we report measurements of structures in which a weak pinning layer is sandwiched between two strongly pinning leads. Quantitative modeling of the results demonstrates that in such systems the critical current is limited by the deformation of individual vortices and not by subsequent cross-joining processes.

Surface attachment of protein fibrils via covalent modification strategies.

Chemical control of surface functionality and topography is an essential requirement for many technological purposes. In particular, the covalent attachment of monomeric proteins to surfaces has been the object of intense studies in recent years, for applications as varied as electrochemistry, immuno-sensing, and the production of biocompatible coatings. Little is known, however, about the characteristics and requirements underlying surface attachment of supramolecular protein nanostructures. Amyloid fibrils formed by the self-assembly of peptide and protein molecules represent one important class of such structures. These highly organized beta-sheet-rich assemblies are a hallmark of a range of neurodegenerative disorders, including Alzheimer's disease and type II diabetes, but recent findings suggest that they have much broader significance, potentially representing the global free energy minima of the energy landscapes of proteins and having potential applications in material science. In this paper, we describe strategies for attaching amyloid fibrils formed from different proteins to gold surfaces under different solution conditions. Our methods involve the reaction of sulfur containing small molecules (cystamine and 2-iminothiolane) with the amyloid fibrils, enabling their covalent linkage to gold surfaces. We demonstrate that irreversible attachment using these approaches makes possible quantitative analysis of experiments using biosensor techniques, such as quartz crystal microbalance (QCM) assays that are revolutionizing our understanding of the mechanisms of amyloid growth and the factors that determine its kinetic behavior. Moreover, our results shed light on the nature and relative importance of covalent versus noncovalent forces acting on protein superstructures at metal surfaces.

A microstructural investigation of deformation in fine SiC particulate- and whisker-reinforced aluminium-matrix composites

Aluminium-based composites, reinforced with low volume fractions of whiskers and small particles, have been formed by a powder route. The materials have been tested in tension, and the microstructures examined using transmission electron microscopy. The whisker composites showed an improvement in flow stress over the particulate composites, and this was linked to an initially enhanced work-hardening rate in the whisker composites. The overall dislocation densities were estimated to be somewhat higher in the whisker composites than the particulate composites, but in the early stages of deformation the distribution was rather different, with deformation in the whisker material being far more localized and inhomogeneous. This factor, together with differences in the internal stress distribution in the materials, is used to explain the difference in mechanical properties.

Cross-sectional structure of tetrahedral amorphous carbon thin films

A cross-sectional transmission electron microscope study of the low density layers at the surface and at the substrate-film interface of tetrahedral amorphous carbon (ta-C) films grown on (001) silicon substrates is presented. Spatially resolved electron energy loss spectroscopy is used to determine the bonding and composition of a tetrahedral amorphous carbon film with nanometre spatial resolution. For a ta-C film grown with a substrate bias of -300 V, an interfacial region approximately 5 nm wide is present in which the carbon is sp2 bonded and is mixed with silicon and oxygen from the substrate. An sp2 bonded layer observed at the surface of the film is 1.3 ± 0.3 nm thick and contains no detectable impurities. It is argued that the sp2 bonded surface layer is intrinsic to the growth process, but that the sp2 bonding in the interfacial layer at the substrate may be related to the presence of oxygen from the substrate.

Resonant Raman characterisation of ultra-thin nano-protective carbon layers for magnetic storage devices

Carbon thin films are very important as protective coatings for a wide range of applications such as magnetic storage devices. The key parameter of interest is the sp3 fraction, since it controls the mechanical properties of the film. Visible Raman spectroscopy is a very popular technique to determine the carbon bonding. However, the visible Raman spectra mainly depend on the configuration and clustering of the sp2 sites. This can result in the Raman spectra of different samples looking similar albeit having a different structure. Thus, visible Raman alone cannot be used to derive the sp3 content. Here we monitor the carbon bonding by using a combined study of Raman spectra taken at two wavelengths (514 and 244 nm). We show how the G peak dispersion is a very useful parameter to investigate the carbon samples and we endorse it as a production-line characterisation tool. The dispersion is proportional to the degree of disorder, thus making it possible to distinguish between graphitic and diamond-like carbon. © 2003 Elsevier B.V. All rights reserved.

Transparent electrode with a nanostructured coating.

Using single-walled nanotubes as an example, we fabricated transparent conductive coatings and demonstrated a new technique of centrifuge coating as a potential low-waste, solution-based batch process for the fabrication of nanostructured coatings. A theoretical model is developed to account for the sheet resistance exhibited by layered random-network coatings such as nanofilaments and graphene. The model equation is analytical and compact, and allows the correlation of very different scaling regimes reported in the literature to the underlying coating microstructure. Finally, we also show a refined experimental setup to systematically measure the curvature-dependent sheet resistance.

Quantitative approaches for characterising fibrillar protein nanostructures

Polypeptide sequences have an inherent tendency to self-assemble into filamentous nanostructures commonly known as amyloid fibrils. Such self-assembly is used in nature to generate a variety of functional materials ranging from protective coatings in bacteria to catalytic scaffolds in mammals. The aberrant self-assembly of misfolded peptides and proteins is also, however, implicated in a range of disease states including neurodegenerative conditions such as Alzheimer's and Parkinson's diseases. It is increasingly evident that the intrinsic material properties of these structures are crucial for understanding the thermodynamics and kinetics of the pathological deposition of proteins, particularly as the mechanical fragmentation of aggregates enhances the rate of protein deposition by exposing new fibril ends which can promote further growth. We discuss here recent advances in physical techniques that are able to characterise the hierarchical self-assembly of misfolded protein molecnles and define their properties. © 2010 Materials Research Society.

Tractable nonparametric bayesian inference in poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finitedimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets. Copyright 2009.

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finite-dimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets.