Performance prediction of graphene nanoribbon and carbon nanotube transistors

Carbon nanotubes (CNTs) and graphene nanoribbons (GNRs) field-effect transistor (FET) can be the basis for a quasi-one- dimensional (Q1D) transistor technology. Recent experiments show that the on-off ratio for GNR devices can be improved to level exploration of transistor action is justified. Here we use the tight-binding energy dipersion approximation, to assess the performance of semiconducting CNT and GNR is qualitatively in terms of drain current drive strength, bandgap and density of states for a specified device. By reducing the maximum conductance 4e2/h by half, we observed that our model has a particularly good fit with 50 nm channel single walled carbon nanotube (SWCNT) experimental data. Given the same bandgap, CNTs outperform GNRs due to valley degeneracy. Nevertheless, the variation of the device contacts will decide which transistor will exhibit better conductivity and thus higher ON currents. © 2011 American Institute of Physics.

Explosion pressure prediction via polynomial mathematical correlation based on advanced CFD Modelling

Computational Fluid Dynamics CFD can be used as a powerful tool supporting engineers throughout the steps of the design. The combination of CFD with response surface methodology can play an important role in such cases. During the conceptual engineering design phase, a quick response is always a matter of urgency. During this phase even a sketch of the geometrical model is rare. Therefore, the utilisation of typical response surface developed for congested and confined environment rather than CFD can be an important tool to help the decision making process, when the geometrical model is not available, provided that similarities can be considered when taking into account the characteristic of the geometry in which the response surface was developed. The present work investigates how three different types of response surfaces behave when predicting overpressure in accidental scenarios based on CFD input. First order, partial second order and complete second order polynomial expressions are investigated. The predicted results are compared with CFD findings for a classical offshore experiment conducted by British Gas on behalf of Mobil and good agreement is observed for higher order response surfaces. The higher order response surface calculations are also compared with CFD calculations for a typical offshore module and good agreement is also observed. © 2011 Elsevier Ltd.

Cardioviral RNA structure logo analysis: entropy, correlations, and prediction

In recent years, there has been an increased number of sequenced RNAs leading to the development of new RNA databases. Thus, predicting RNA structure from multiple alignments is an important issue to understand its function. Since RNA secondary structures are often conserved in evolution, developing methods to identify covariate sites in an alignment can be essential for discovering structural elements. Structure Logo is a technique established on the basis of entropy and mutual information measured to analyze RNA sequences from an alignment. We proposed an efficient Structure Logo approach to analyze conservations and correlations in a set of Cardioviral RNA sequences. The entropy and mutual information content were measured to examine the conservations and correlations, respectively. The conserved secondary structure motifs were predicted on the basis of the conservation and correlation analyses. Our predictive motifs were similar to the ones observed in the viral RNA structure database, and the correlations between bases also corresponded to the secondary structure in the database.