Rational design of nanofibrous materials

Making Carbon nanotubes a functional material for widespread use is a very cumbersome and challenging task. Not only do CNT materials require the tubes to be well dispersed and individualized rather than in bundles but resulting material has much poorer properties than expected due to insufficient load transfer between crossing CNT. This work tries to provide insight and solutions onto both of these problems, by employing computer simulations to reveal the dual nature of surfactant mediated forces on CNT. A generic coarse grain model has been used along with a dissipative particle dynamics thermostat and implicit solvent treatment. Results illustrate that depending on the bulk concentration of surfactants and their geometry, one can control the surfatantmediated forces on tubes being able to trigger both tube gluing or dispersion. Furthermore, an adsorption study elucidating the differences between surfactant adsorption on individual tubes and their bundles has been done. Surfactants follow a superlinear synergetic adsorption isothermon individual tubes,whereas adsorb via a Langmuir mechanism on their bundles. This work provides a solid framework of knowledge and insight regarding the nature of CNT and surfactants interaction and adsorption, providing rational arguments for the design of optimum CNT materials

Kapitza Resistance between Few-Layer Graphene and Water: Liquid Layering Effects

The Kapitza resistance (RK) between few-layer graphene (FLG) and water was studied using molecular dynamics simulations. The R_K was found to depend on the number of the layers in the FLG though, surprisingly, not on the water block thickness. This distinct size dependence is attributed to the large difference in the phonon mean free path between the FLG and water. Remarkably, R_K is strongly dependent on the layering of water adjacent to the FLG, exhibiting an inverse proportionality relationship to the peak density of the first water layer, which is consistent with better acoustic phonon matching between FLG and water. These findings suggest novel ways to engineer the thermal transport properties of solid–liquid interfaces by controlling and regulating the liquid layering at the interface

Bayesian system identification of dynamical systems using highly informative training data

This paper is concerned with the Bayesian system identification of structural dynamical systems using experimentally obtained training data. It is motivated by situations where, from a large quantity of training data, one must select a subset to infer probabilistic models. To that end, using concepts from information theory, expressions are derived which allow one to approximate the effect that a set of training data will have on parameter uncertainty as well as the plausibility of candidate model structures. The usefulness of this concept is then demonstrated through the system identification of several dynamical systems using both physics-based and emulator models. The result is a rigorous scientific framework which can be used to select 'highly informative' subsets from large quantities of training data