Dispersion of carbon nanotubes: Mixing, sonication, stabilization, and composite properties

Advances in functionality and reliability of carbon nanotube (CNT) composite materials require careful formulation of processing methods to ultimately realize the desired properties. To date, controlled dispersion of CNTs in a solution or a composite matrix remains a challenge, due to the strong van der Waals binding energies associated with the CNT aggregates. There is also insufficiently defined correlation between the microstructure and the physical properties of the composite. Here, we offer a review of the dispersion processes of pristine (non-covalently functionalized) CNTs in a solvent or a polymer solution. We summarize and adapt relevant theoretical analysis to guide the dispersion design and selection, from the processes of mixing/sonication, to the application of surfactants for stabilization, to the final testing of composite properties. The same approaches are expected to be also applicable to the fabrication of other composite materials involving homogeneously dispersed nanoparticles. © 2012 by the authors; licensee MDPI, Basel, Switzerland.

First-principles energetics of water clusters and ice: a many-body analysis.

Standard forms of density-functional theory (DFT) have good predictive power for many materials, but are not yet fully satisfactory for cluster, solid, and liquid forms of water. Recent work has stressed the importance of DFT errors in describing dispersion, but we note that errors in other parts of the energy may also contribute. We obtain information about the nature of DFT errors by using a many-body separation of the total energy into its 1-body, 2-body, and beyond-2-body components to analyze the deficiencies of the popular PBE and BLYP approximations for the energetics of water clusters and ice structures. The errors of these approximations are computed by using accurate benchmark energies from the coupled-cluster technique of molecular quantum chemistry and from quantum Monte Carlo calculations. The systems studied are isomers of the water hexamer cluster, the crystal structures Ih, II, XV, and VIII of ice, and two clusters extracted from ice VIII. For the binding energies of these systems, we use the machine-learning technique of Gaussian Approximation Potentials to correct successively for 1-body and 2-body errors of the DFT approximations. We find that even after correction for these errors, substantial beyond-2-body errors remain. The characteristics of the 2-body and beyond-2-body errors of PBE are completely different from those of BLYP, but the errors of both approximations disfavor the close approach of non-hydrogen-bonded monomers. We note the possible relevance of our findings to the understanding of liquid water.

Correlations between the performance characteristics of a liquid crystal laser and the macroscopic material properties.

In order to understand how the performance of a liquid-crystal laser depends on the physical properties of the low molar mass nematic host, we have studied the energy threshold and slope efficiency of ten optically pumped liquid-crystal lasers based on different hosts. Specifically, this leads to a variation in the birefringence, the orientational order parameter, and the order parameter of the transition dipole moment of the dye. It is found that low threshold energies and high slope efficiencies correlate with high order parameters and large birefringences. To a first approximation this can be understood by considering analytical expressions for the threshold and slope efficiency, which are derived from the space-independent rate equations for a two-level system, in terms of the macroscopic liquid crystal properties.

Probabilistic fault identification using vibration data and neural networks

Bayesian formulated neural networks are implemented using hybrid Monte Carlo method for probabilistic fault identification in cylindrical shells. Each of the 20 nominally identical cylindrical shells is divided into three substructures. Holes of (12±2) mm in diameter are introduced in each of the substructures and vibration data are measured. Modal properties and the Coordinate Modal Assurance Criterion (COMAC) are utilized to train the two modal-property-neural-networks. These COMAC are calculated by taking the natural-frequency-vector to be an additional mode. Modal energies are calculated by determining the integrals of the real and imaginary components of the frequency response functions over bandwidths of 12% of the natural frequencies. The modal energies and the Coordinate Modal Energy Assurance Criterion (COMEAC) are used to train the two frequency-response-function-neural-networks. The averages of the two sets of trained-networks (COMAC and COMEAC as well as modal properties and modal energies) form two committees of networks. The COMEAC and the COMAC are found to be better identification data than using modal properties and modal energies directly. The committee approach is observed to give lower standard deviations than the individual methods. The main advantage of the Bayesian formulation is that it gives identities of damage and their respective confidence intervals.

Observing graphene grow: catalyst-graphene interactions during scalable graphene growth on polycrystalline copper.

Complementary in situ X-ray photoelectron spectroscopy (XPS), X-ray diffractometry, and environmental scanning electron microscopy are used to fingerprint the entire graphene chemical vapor deposition process on technologically important polycrystalline Cu catalysts to address the current lack of understanding of the underlying fundamental growth mechanisms and catalyst interactions. Graphene forms directly on metallic Cu during the high-temperature hydrocarbon exposure, whereby an upshift in the binding energies of the corresponding C1s XPS core level signatures is indicative of coupling between the Cu catalyst and the growing graphene. Minor carbon uptake into Cu can under certain conditions manifest itself as carbon precipitation upon cooling. Postgrowth, ambient air exposure even at room temperature decouples the graphene from Cu by (reversible) oxygen intercalation. The importance of these dynamic interactions is discussed for graphene growth, processing, and device integration.