Unexpected softness of bilayer graphene and softening of A-A stacked graphene layers

Density functional theory has been used to investigate the behavior of the π electrons in bilayer graphene and graphite under compression along the c axis. We have studied both conventional Bernal (A-B) and A-A stackings of the graphene layers. In bilayer graphene, only about 0.5% of the π-electron density is squeezed through the sp2 network for a compression of 20%, regardless of the stacking order. However, this has a major effect, resulting in bilayer graphene being about six times softer than graphite along the c axis. Under compression along the c axis, the heavily deformed electron orbitals (mainly those of the π electrons) increase the interlayer interaction between the graphene layers as expected, but, surprisingly, to a similar extent for A-A and Bernal stackings. On the other hand, this compression shifts the in-plane phonon frequencies of A-A stacked graphene layers significantly and very differently from the Bernal stacked layers. We attribute these results to some sp2 electrons in A-A stacking escaping the graphene plane and filling lower charge-density regions when under compression, hence, resulting in a nonmonotonic change in the sp2-bond stiffness

Trends in the elastic response of binary early transition metal nitrides

Motivated by an increasing demand for coherent data that can be used for selecting materials with properties tailored for specific application requirements, we studied elastic response of nine binary early transition metal nitrides (ScN, TiN, VN, YN, ZrN, NbN, LaN, HfN, and TaN) and AlN. In particular, single crystal elastic constants, Young's modulus in different crystallographic directions, polycrystalline values of shear and Young's moduli, and the elastic anisotropy factor were calculated. Additionally, we provide estimates of the third order elastic constants for the ten binary nitrides.Comment: 10 pages, 7 figure

Using ILP to Identify Pathway Activation Patterns in Systems Biology

We show a logical aggregation method that, combined with propositionalization methods, can construct novel structured biological features from gene expression data. We do this to gain understanding of pathway mechanisms, for instance, those associated with a particular disease. We illustrate this method on the task of distinguishing between two types of lung cancer; Squamous Cell Carcinoma (SCC) and Adenocarcinoma (AC). We identify pathway activation patterns in pathways previously implicated in the development of cancers. Our method identified a model with comparable predictive performance to the winning algorithm of a recent challenge, while providing biologically relevant explanations that may be useful to a biologist

Buckling of ZnS-filled single-walled carbon nanotubes - The influence of aspect ratio

The mechanical response of single-walled carbon nanotubes (SWCNT) filled with crystalline zinc sulphide (ZnS) nanowires under uniaxial compression is studied using classical molecular dynamics. These simulations were used to analyse the behaviour of SWCNT, with and without ZnS filling, in terms of critical force and critical strain. Force versus strain curves have been computed for hollow and filled systems, the latter clearly showing an improvement of the mechanical behaviour caused by the ZnS nanowire. The same simulations were repeated for a large range of dimensions in order to evaluate the influence of the aspect ratio on the mechanical response of the tubes. (C) 2014 Elsevier Ltd. All rights reserved