Graphene nano-ribbon under tension

The mechanical response of graphene nano-ribbon under tensile loading has been investigated using atomistic simulation. Lattice symmetry dependence of elastic properties are found, which fits prediction from Cauchy-Born rule well. Concurrent brittle and ductile behaviors are observed in the failure process at elastic limit, which dominates at low and high temperature respectively. In addition, the free edges of finite width ribbon help to activate bond-flip events and initialize ductile behavior

Strain engineering on graphene towards tunable and reversible hydrogenation

Graphene is the extreme material for molecular sensory and hydrogen storage applications because of its two-dimensional geometry and unique structure-property relationship. In this Letter, hydrogenation of graphene is discussed in the extent of intercoupling between mechanical deformation and electronic configuration. Our first principles calculation reveals that the atomic structures, binding energies, mechanical and electronic properties of graphene are significantly modified by the hydrogenation and applied strain. Under an in-plane strain of 10 %, the binding energies of hydrogen on graphene can be improved by 53.89 % and 23.56 % in the symmetric and anti-symmetric phase respectively. Furthermore the instability of symmetrically bound hydrogen atoms under compression suggests a reversible storage approach of hydrogen. In the anti-symmetric phase, the binding of hydrogen breaks the sp2 characteristic of graphene, which can be partly recovered at tensile strain. A charge density based analysis unveils the underline mechanisms. The results reported here offer a way not only to tune the binding of hydrogen on graphene in a controllable and reversible manner, but also to engineer the properties of graphene through a synergistic control through mechanical loads and hydrogen doping

A note on self-similarity for discrete time series

The purpose of this paper is to study the self-similar properties of discrete-time long memory processes. We apply our results to specific processes such as GARMA processes and GIGARCH processes, heteroscedastic models and the processes with switches and jumps.Covariance stationary, Long memory processes, short memory processes, self-similar, asymptotically second-order self-similar, autocorrelation function.