The role of Ti as a catalyst for the dissociation of hydrogen on a Mg(0001) surface

In this paper, the dissociative chemisorption of hydrogen on both pure and Ti-incorporated Mg(0001) surfaces are studied by ab initio density functional theory (DFT) calculations. The calculated dissociation barrier of hydrogen molecule on a pure Mg(0001) surface (1.05 eV) is in good agreement with comparable theoretical studies. For the Ti-incorporated Mg(0001) surface, the activated barrier decreases to 0.103 eV due to the strong interaction between the molecular orbital of hydrogen and the d metal state of Ti. This could explain the experimentally observed improvement in absorption kinetics of hydrogen when transition metals have been introduced into the magnesium materials.

Ab initio studies of hydrogen desorption from low index magnesium hydride surface

The low index Magnesium hydride surfaces, MgH2(001) and MgH2(110), have been studied by ab intio Density Functional Theory (DFT) calculations. It was found that the MgH2(110) surface is more stable than MgH2(001) surface, which is in good agreement with the experimental observation. The H-2 desorption barriers vary depending on the crystalline surfaces that are exposed and also the specific H atom sites involved-they are found to be generally high, due to the thermodynamic stability of the MgH2, system, and are larger for the MgH2(001) surface. The pathway for recombinative desorption of one in-plane and one bridging H atom from the MgH2(110) surface was found to be the lowest energy barrier amongst those computed (172 KJ/mol) and is in good agreement with the experimental estimates. (c) 2006 Elsevier B.V. All rights reserved.

Catalytic effects of subsurface carbon in the chemisorption of hydrogen on a Mg(0001) surface: an ab-initio study

Ab initio density functional theory (DFT) calculations are performed to explore possible catalytic effects on the dissociative chemisorption of hydrogen on a Mg(0001) surface when carbon is incorporated into Mg materials. The computational results imply that a C atom located initially on a Mg(0001) surface can migrate into the subsurface and occupy an fcc interstitial site, with charge transfer to the C atom from neighboring Mg atoms. The effect of subsurface C on the dissociation of H-2 on the Mg(0001) surface is found to be relatively marginal: a perfect sublayer of interstitial C is calculated to lower the barrier by 0.16 eV compared with that on a pure Mg(0001) surface. Further calculations reveal, however, that sublayer C may have a significant effect in enhancing the diffusion of atomic hydrogen into the sublayers through fcc channels. This contributes new physical understanding toward rationalizing the experimentally observed improvement in absorption kinetics of H2 when graphite or single walled carbon nanotubes (SWCNT) are introduced into the Mg powder during ball milling.

First-principle study of adsorption of hydrogen on Ti-doped Mg(0001) surface

Ab initio density functional theory (DFT) calculations are performed to study the adsorption of H-2 molecules on a Ti-doped Mg(0001) surface. We find that two hydrogen molecules are able to dissociate on top of the Ti atom with very small activation barriers (0.103 and 0.145 eV for the first and second H-2 molecules, respectively). Additionally, a molecular adsorption state of H-2 above the Ti atom is observed for the first time and is attributed to the polarization of the H-2 molecule by the Ti cation. Our results parallel recent findings for H-2 adsorption on Ti-doped carbon nanotubes or fullerenes. They provide new insight into the preliminary stages of hydrogen adsorption onto Ti-incorporated Mg surfaces.

Boosting the tree augmented naive bayes classifier

The Tree Augmented Naïve Bayes (TAN) classifier relaxes the sweeping independence assumptions of the Naïve Bayes approach by taking account of conditional probabilities. It does this in a limited sense, by incorporating the conditional probability of each attribute given the class and (at most) one other attribute. The method of boosting has previously proven very effective in improving the performance of Naïve Bayes classifiers and in this paper, we investigate its effectiveness on application to the TAN classifier.