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Property Combinations (property + combination)

Distribution by Scientific Domains

Polymers and Materials Science	50%

Selected Abstracts

Functionalization of COC Surfaces by Microwave Plasmas

PLASMA PROCESSES AND POLYMERS, Issue S1 2007
Hartmut Steffen
Abstract Cyclic olefin copolymers (COC) combine excellent transparency, high moisture barrier, high strength and stiffness and very low shrinkage. COCs have excellent chemical resistance to aqueous acids and bases and to most polar solvents. This property combination makes them excellent candidates for diverse diagnostic applications in biomedical science. But they are very hydrophobic and thus not suitable for cell-contacting applications. This work investigates the surface functionalization of COC compared to PS by NH3 and SO2 microwave plasmas. The surfaces were mainly analysed by high-resolution X-ray photoelectron spectroscopy (XPS). Moreover, cells were cultivated on both substrates to verify the applicability of COC for cell-based disposables. Actually, microwave plasma-functionalized COC surfaces support the adhesion and proliferation of adherent cell lines, which usually require the coating of the substrate with extra cellular matrix molecules. [source]

Chemical Bonding Assembly of Multifunctional Oxide Nanocomposites

ADVANCED FUNCTIONAL MATERIALS, Issue 2 2010
Gary Evans
Abstract The synthesis, functionalization and assembly of metal oxide nanoparticles BaTiO3 and CoFe2O4 is presented. The ferroelectric (BaTiO3) and ferromagnetic (CoFe2O4) oxide nanoparticle surfaces are directly functionalized via the anchoring of phosphonic acid and aminosilane molecules that engender the nanoparticles with terminal carboxylic acid and amine functional groups, respectively. These promote the electrostatic self-assembly of the particles in non-polar solvents and permit the synthesis of more chemically robust assemblies linked by the covalent amide bond via the addition of the chemical coupling agent N - N, -dicyclohexylcarbodiimide. This functionalization and assembly procedure is applied to two systems: the first comprised of 50,nm BaTiO3 and 10,nm CoFe2O4 particles and the second of 200,nm BaTiO3 and 12.5,nm CoFe2O4 particles. The latter composites possess magnetoelectric properties when processed into dense ceramics and, as a direct result of the assembly performed in solution, have a high degree of homogeneity between the ferroelectric and ferromagnetic phases. The developed functionalization and assembly procedure is considered to be adaptable to the preparation of other hybrid oxide nanomaterials with different property combinations. [source]

Inversion of time-dependent nuclear well-logging data using neural networks

GEOPHYSICAL PROSPECTING, Issue 1 2008
Laura Carmine
ABSTRACT The purpose of this work was to investigate a new and fast inversion methodology for the prediction of subsurface formation properties such as porosity, salinity and oil saturation, using time-dependent nuclear well logging data. Although the ultimate aim is to apply the technique to real-field data, an initial investigation as described in this paper, was first required; this has been carried out using simulation results from the time-dependent radiation transport problem within a borehole. Simulated neutron and ,-ray fluxes at two sodium iodide (NaI) detectors, one near and one far from a pulsed neutron source emitting at ,14 MeV, were used for the investigation. A total of 67 energy groups from the BUGLE96 cross section library together with 567 property combinations were employed for the original flux response generation, achieved by solving numerically the time-dependent Boltzmann radiation transport equation in its even parity form. Material property combinations (scenarios) and their correspondent teaching outputs (flux response at detectors) are used to train the Artificial Neural Networks (ANNs) and test data is used to assess the accuracy of the ANNs. The trained networks are then used to produce a surrogate model of the expensive, in terms of computational time and resources, forward model with which a simple inversion method is applied to calculate material properties from the time evolution of flux responses at the two detectors. The inversion technique uses a fast surrogate model comprising 8026 artificial neural networks, which consist of an input layer with three input units (neurons) for porosity, salinity and oil saturation; and two hidden layers and one output neuron representing the scalar photon or neutron flux prediction at the detector. This is the first time this technique has been applied to invert pulsed neutron logging tool information and the results produced are very promising. The next step in the procedure is to apply the methodology to real data. [source]