We present a comprehensive first-principles investigation of the atomic and electronic structures of gallium nitride nanowires, and examine the dependence on nanowire diameter and shape. We consider nanowires in the ͓0001͔ growth direction, with diameters ranging from 8 to 35 Å, and investigate the influence of saturating the dangling bonds at the edges of… (More)
Extending chip performance beyond current limits of miniaturisation requires new materials and functionalities that integrate well with the silicon platform. Germanium fits these requirements and has been proposed as a high-mobility channel material, a light emitting medium in silicon-integrated lasers, and a plasmonic conductor for bio-sensing. Common to… (More)
We investigate the performance of the vdW-DF functional of Dion et al. implemented in the SIESTA code. In particular, the S22 data set and several calixarene-based host-guest structures are examined to assess the performance of the functional. The binding energy error statistics for the S22 data set reveal that the vdW-DF functional performs very well when… (More)
Atom implantation in graphene or graphene nanoribbons offers a rich opportunity to tune the material structure and functional properties. In this study, zigzag graphene nanoribbons with Ti or Sn adatoms stabilised on a double carbon vacancy site are theoretically studied to investigate their sensitivity to sulfur-containing gases (H2S and SO2). Due to the… (More)
We investigate gallium and nitrogen vacancies in gallium nitride (GaN) bulk and nanowires using self-interaction corrected pseudopotentials (SIC). In particular, we examine the band structures to compare and contrast differences between the SIC results and standard density functional theory (DFT) results using a generalized gradient approximation (GGA)… (More)
Two recent algorithms for estimating zero-upcross wave periods from altimeter data are investigated using data from a buoy in the North East Pacific with a relatively high occurence of swell. Both algorithms performed well in wind seas, with low wave age, but only one – developed using a neural network – was satisfactory when the wave age was high.