2003
DOI: 10.1021/jp030596n
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Kinetic Monte Carlo Study of Competing Hydrogen Pathways into Connected (100), (110), and (111) Ni Surfaces

Abstract: Hydrogen mobility upon and pathways into connected surfaces of a fcc metal, using the Ni (100), (110), and (111) faces as a model, are examined through computational methods. Our interest is in finding the time scale for an initial H-atom population density deposited on the surface to reach an equilibrium surface and sublayer distribution, and to understand the H dynamics in the region of Ni surface steps. The activation energies for H mobility from site-to-site are determined using a realistic potential energ… Show more

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Cited by 8 publications
(4 citation statements)
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References 57 publications
(116 reference statements)
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“…This information, when combined with insights from recent work showing that large stretched areas are induced in crystal surfaces by dislocations that originate in the bulk, [8] implies that nickel crystals with such defects will absorb hydrogen more readily than perfect Ni(111) surfaces. This mechanism for strain-mediated formation of subsurface hydrogen around defects, possibly combined with previous proposals for subsurface H formation by grain-boundary diffusion and/or direct hydrogen penetration through steps and kinks, [11,30] could explain the observed higher solubility of hydrogen in polycrystalline, as opposed to single-crystal, nickel samples. [31] In turn, we suggest that, if subsurface species play a special role in catalytic surface chemistry, that role might be more pronounced on real catalytic particles, where defect density is much higher than on single crystal surfaces.…”
Section: Methodssupporting
confidence: 68%
“…This information, when combined with insights from recent work showing that large stretched areas are induced in crystal surfaces by dislocations that originate in the bulk, [8] implies that nickel crystals with such defects will absorb hydrogen more readily than perfect Ni(111) surfaces. This mechanism for strain-mediated formation of subsurface hydrogen around defects, possibly combined with previous proposals for subsurface H formation by grain-boundary diffusion and/or direct hydrogen penetration through steps and kinks, [11,30] could explain the observed higher solubility of hydrogen in polycrystalline, as opposed to single-crystal, nickel samples. [31] In turn, we suggest that, if subsurface species play a special role in catalytic surface chemistry, that role might be more pronounced on real catalytic particles, where defect density is much higher than on single crystal surfaces.…”
Section: Methodssupporting
confidence: 68%
“…This information, when combined with insights from recent work showing that large stretched areas are induced in crystal surfaces by dislocations that originate in the bulk,8 implies that nickel crystals with such defects will absorb hydrogen more readily than perfect Ni(111) surfaces. This mechanism for strain‐mediated formation of subsurface hydrogen around defects, possibly combined with previous proposals for subsurface H formation by grain‐boundary diffusion and/or direct hydrogen penetration through steps and kinks,11, 30 could explain the observed higher solubility of hydrogen in polycrystalline, as opposed to single‐crystal, nickel samples 31. In turn, we suggest that, if subsurface species play a special role in catalytic surface chemistry, that role might be more pronounced on real catalytic particles, where defect density is much higher than on single crystal surfaces.…”
Section: Binding Energies Beh For Surface and Subsurface Hydrogen On supporting
confidence: 76%
“…2; see also Table 1) is to create a complete catalog of all possible processes along with their transition probabilities. This key input to any KMC simulation can be extracted from smaller length and time scale simulation tools, such as density functional theory (DFT) [18,[99][100][101][102] (these are often termed first principles KMC simulations), transition state identification methods, transition state theory (TST) [103,104], and MD [3,4,61,105,106] typically via a bottom-up approach (information passage from small to large scales) [36,[107][108][109][110]. Significant work in this area to address the computational requirements of DFT and MD and the approximations involved, e.g., in TST, has led to the development of a whole new research area.…”
Section: Input To a Kmc Simulationmentioning
confidence: 99%