Ethanol and Water Adsorption on Close-Packed 3d, 4d, and 5d Transition-Metal Surfaces: A Density Functional Theory Investigation with van der Waals Correction

Nowadays, there is a great interest in the economic success of direct ethanol fuel cells; however, our atomistic understanding of the designing of stable and low-cost catalysts for the steam reforming of ethanol is still far from satisfactory, in particular due to the large number of undesirable intermediates. In this study, we will report a first-principles investigation of the adsorption properties of ethanol and water at low coverage on close-packed transition-metal (TM) surfaces, namely, Fe(110), Co(0001), Ni(111), Cu(111), Ru(0001), Rh(111), Pd(111), Ag(111), Os(0001), Ir(111), Pt(111), and Au(111), employing density functional theory (DFT) calculations. We employed the generalized gradient approximation with the formulation proposed by Perdew, Burke, and Erzenholf (PBE) to the exchange correlation functional and the empirical correction proposed by S. Grimme (DFT+D3) for the van der Waals correction. We found that both adsorbates binds preferentially near or on the on top sites of the TM surfaces through the 0 atoms. The PBE adsorption energies of ethanol and water decreases almost linearly with the increased occupation of the 4d and 5d d-band, while there is a deviation for the 3d systems. The van der Waals correction affects the linear behavior and increases the adsorption energy for both adsorbates, which is expected as the van der Waals energy due to the correlation effects is strongly underestimated by DFT-PBE for weak interacting systems. The geometric parameters for water/TM are not affected by the van der Waals correction, i.e., both DFT and DFT+D3 yield an almost parallel orientation for water on the TM surfaces; however, DFT+D3 changes drastically the ethanol orientation. For example, DFT yields an almost perpendicular orientation of the C-C bond to the TM surface, while the C-C bond is almost parallel to the surface using DFT +D3 for all systems, except for ethanol/Fe(110). Thus, the van der Waals correction decreases the distance of the C atoms to the TM surfaces, which might contribute to break the C-C bond. The work function decreases upon the adsorption of ethanol and water, and both follow the same trends, however, with different magnitude (larger for ethanol/TM) due to the weak binding of water to the surface. The electron density increases mainly in the region between the topmost layer and the adsorbates, which explains the reduction of the substrate work function.

Hausdorff Distance evaluation of orthodontic accessories' streaking artifacts in 3D model superimposition

The aim of this study was to determine whether image artifacts caused by orthodontic metal accessories interfere with the accuracy of 3D CBCT model superimposition. A human dry skull was subjected three times to a CBCT scan: at first without orthodontic brackets (T1), then with stainless steel brackets bonded without (T2) and with orthodontic arch wires (T3) inserted into the brackets' slots. The registration of image surfaces and the superimposition of 3D models were performed. Within-subject surface distances between T1-T2, T1-T3 and T2-T3 were computed and calculated for comparison among the three data sets. The minimum and maximum Hausdorff Distance units (HDu) computed between the corresponding data points of the T1 and T2 CBCT 3D surface images were 0.000000 and 0.049280 HDu, respectively, and the mean distance was 0.002497 HDu. The minimum and maximum Hausdorff Distances between T1 and T3 were 0.000000 and 0.047440 HDu, respectively, with a mean distance of 0.002585 HDu. In the comparison between T2 and T3, the minimum, maximum and mean Hausdorff Distances were 0.000000, 0.025616 and 0.000347 HDu, respectively. In the current study, the image artifacts caused by metal orthodontic accessories did not compromise the accuracy of the 3D model superimposition. Color-coded maps of overlaid structures complemented the computed Hausdorff Distances and demonstrated a precise fusion between the data sets.