4 resultados para Philip der Grossmütige, landgrave of Hesse, 1504-1567.

em Doria (National Library of Finland DSpace Services) - National Library of Finland, Finland


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Computational material science with the Density Functional Theory (DFT) has recently gained a method for describing, for the first time the non local bonding i.e., van der Waals (vdW) bonding. The newly proposed van der Waals-Density Functional (vdW-DF) is employed here to address the role of non local interactions in the case of H2 adsorption on Ru(0001) surface. The later vdW-DF2 implementation with the DFT code VASP (Vienna Ab-initio Simulation Package) is used in this study. The motivation for studying H2 adsorption on ruthenium surface arose from the interest to hydrogenation processes. Potential energy surface (PES) plots are created for adsorption sites top, bridge, fcc and hcp, employing the vdW-DF2 functional. The vdW-DF yields 0.1 eV - 0.2 eV higher barriers for the dissociation of the H2 molecule; the vdW-DF seems to bind the H2 molecule more tightly together. Furthermore, at the top site, which is found to be the most reactive, the vdW functional suggests no entrance barrier or in any case smaller than 0.05 eV, whereas the corresponding calculation without the vdW-DF does. Ruthenium and H2 are found to have the opposite behaviors with the vdW-DF; Ru lattice constants are overestimated while H2 bond length is shorter. Also evaluation of the CPU time demand of the vdW-DF2 is done from the PES data. From top to fcc sites the vdW-DF computational time demand is larger by 4.77 % to 20.09 %, while at the hcp site it is slightly smaller. Also the behavior of a few exchange correlation functionals is investigated along addressing the role of vdW-DF. Behavior of the different functionals is not consistent between the Ru lattice constants and H2 bond lengths. It is thus difficult to determine the quality of a particular exchange correlation functional by comparing equilibrium separations of the different elements. By comparing PESs it would be computationally highly consuming.