Adsorption-Induced Step Formation

Thostrup, Peter; Christoffersen, Ebbe; Lorensen, Henrik Qvist; Jacobsen, Karsten Wedel; Besenbacher, Flemming; Nørskov, Jens K.

doi:10.1103/physrevlett.87.126102

Cited by 69 publications

(40 citation statements)

References 20 publications

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“…The surface sites are practically depleted in O. The step is therefore stabilized compared with the facet, because CO adsorption is stronger on the step than at the facet 31,34,35 , and a more spherical shape of the Pt nanoparticles is thus expected at high CO pressure. At lower CO pressures (p CO < 1 mbar), the facet and step sites obtain a significant O coverage.…”

Section: Resultsmentioning

confidence: 97%

Visualization of oscillatory behaviour of Pt nanoparticles catalysing CO oxidation

et al. 2014

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Many catalytic reactions under fixed conditions exhibit oscillatory behaviour. The oscillations are often attributed to dynamic changes in the catalyst surface. So far, however, such relationships were difficult to determine for catalysts consisting of supported nanoparticles. Here, we employ a nanoreactor to study the oscillatory CO oxidation catalysed by Pt nanoparticles using time-resolved high-resolution transmission electron microscopy, mass spectrometry and calorimetry. The observations reveal that periodic changes in the CO oxidation are synchronous with a periodic refacetting of the Pt nanoparticles. The oscillatory reaction is modelled using density functional theory and mass transport calculations, considering the CO adsorption energy and the oxidation rate as site-dependent. We find that to successfully explain the oscillations, the model must contain the phenomenon of refacetting. The nanoreactor approach can thus provide atomic-scale information that is specific to surface sites. This will improve the understanding of dynamic properties in catalysis and related fields.

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Section: Resultsmentioning

confidence: 97%

Visualization of oscillatory behaviour of Pt nanoparticles catalysing CO oxidation

et al. 2014

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“…The formation and growth conditions of nanoparticles in solution differ markedly from those of nanoparticles in a high-vacuum environment because the surface of nanoparticles adapts to its environment. This difference has been demonstrated using in situ spectroscopy and microscopy techniques123941424344454647484950515253.…”

Section: Resultsmentioning

confidence: 99%

Structural and electronic analysis of the atomic scale nucleation of Ag on α-Ag2WO4 induced by electron irradiation

Andrés

Gracia

González-Navarrete

et al. 2014

Sci Rep

107

126

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In this work, we utilise a combination of theory, computation and experiments to understand the early events related to the nucleation of Ag filaments on α-Ag2WO4 crystals, which is driven by an accelerated electron beam from an electron microscope under high vacuum. The growth process and the chemical composition and elemental distribution in these filaments were analysed in depth at the nanoscale level using TEM, HAADF, EDS and XPS; the structural and electronic aspects were systematically studied in using first-principles electronic structure theory within QTAIM framework. The Ag nucleation and formation on α-Ag2WO4 is a result of the order/disorder effects generated in the crystal by the electron-beam irradiation. Both experimental and theoretical results show that this behavior is associated with structural and electronic changes of the [AgO2] and [AgO4] clusters and, to a minor extent, to the [WO6] cluster; these clusters collectively represent the constituent building blocks of α-Ag2WO4.

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“…This problem has been touched upon only sporadically for simple unimolecular processes [128][129][130] and rarely for non-linear and/or complex problems [47,99]. The time can be advanced based on a single process either at a site or over the entire lattice every time this process is picked.…”

Section: Time Increments and Time Clocksmentioning

confidence: 98%

“…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%

An overview of spatial microscopic and accelerated kinetic Monte Carlo methods

Chatterjee

Vlachos

2007

J Computer-Aided Mater Des

424

400

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The microscopic spatial kinetic Monte Carlo (KMC) method has been employed extensively in materials modeling. In this review paper, we focus on different traditional and multiscale KMC algorithms, challenges associated with their implementation, and methods developed to overcome these challenges. In the first part of the paper, we compare the implementation and computational cost of the null-event and rejection-free microscopic KMC algorithms. A firmer and more general foundation of the null-event KMC algorithm is presented. Statistical equivalence between the null-event and rejection-free KMC algorithms is also demonstrated. Implementation and efficiency of various search and update algorithms, which are at the heart of all spatial KMC simulations, are outlined and compared via numerical examples. In the second half of the paper, we review various spatial and temporal multiscale KMC methods, namely, the coarse-grained Monte Carlo (CGMC), the stochastic singular perturbation approximation, and the τ -leap methods, introduced recently to overcome the disparity of length and time scales and the one-at-a time execution of events. The concepts of the CGMC and the τ -leap methods, stochastic closures, multigrid methods, error associated with coarse-graining, a posteriori error estimates for generating spatially adaptive coarse-grained lattices, and computational speed-up upon coarse-graining are illustrated through simple examples from crystal growth, defect dynamics, adsorption-desorption, surface diffusion, and phase transitions.

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Adsorption-Induced Step Formation

Cited by 69 publications

References 20 publications

Visualization of oscillatory behaviour of Pt nanoparticles catalysing CO oxidation

Visualization of oscillatory behaviour of Pt nanoparticles catalysing CO oxidation

Structural and electronic analysis of the atomic scale nucleation of Ag on α-Ag2WO4 induced by electron irradiation

An overview of spatial microscopic and accelerated kinetic Monte Carlo methods

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