scholarly journals Kinetic description of site ensembles on catalytic surfaces

2021 ◽  
Vol 118 (8) ◽  
pp. e2019055118
Author(s):  
Neil K. Razdan ◽  
Aditya Bhan

We demonstrate that the Langmuir–Hinshelwood formalism is an incomplete kinetic description and, in particular, that the Hinshelwood assumption (i.e., that adsorbates are randomly distributed on the surface) is inappropriate even in catalytic reactions as simple as A + A → A2. The Hinshelwood assumption results in miscounting of site pairs (e.g., A*–A*) and, consequently, in erroneous rates, reaction orders, and identification of rate-determining steps. The clustering and isolation of surface species unnoticed by the Langmuir–Hinshelwood model is rigorously accounted for by derivation of higher-order rate terms containing statistical factors specific to each site ensemble. Ensemble-specific statistical rate terms arise irrespective of and couple with lateral adsorbate interactions, are distinct for each elementary step including surface diffusion events (e.g., A* + * → * + A*), and provide physical insight obscured by the nonanalytical nature of the kinetic Monte Carlo (kMC) method—with which the higher-order formalism quantitatively agrees. The limitations of the Langmuir–Hinshelwood model are attributed to the incorrect assertion that the rate of an elementary step is the same with respect to each site ensemble. In actuality, each elementary step—including adsorbate diffusion—traverses through each ensemble with unique rate, reversibility, and kinetic-relevance to the overall reaction rate. Explicit kinetic description of ensemble-specific paths is key to the improvements of the higher-order formalism; enables quantification of ensemble-specific rate, reversibility, and degree of rate control of surface diffusion; and reveals that a single elementary step can, counter intuitively, be both equilibrated and rate determining.

2004 ◽  
Vol 365 (1-2) ◽  
pp. 66-72 ◽  
Author(s):  
S.P.A. Gill ◽  
P.E. Spencer ◽  
A.C.F. Cocks

2004 ◽  
Vol 859 ◽  
Author(s):  
Tansel Karabacak ◽  
Gwo-Ching Wang ◽  
Toh-Ming Lu

ABSTRACTThe characteristics of nucleation and island growth in oblique angle deposition with substrate rotation have recently attracted interest due to the formation of novel 3D nanostructures by a physical self-assembly process. In this study, we present the results of a solid-on-solid growth simulation by a kinetic Monte Carlo algorithm that explores the layer coverage evolution of thin films during oblique angle deposition. The simulations accounted for oblique incidence flux, shadowing effect, surface diffusion, and substrate rotation. The layer coverage, the ratio of average island volume to average island size, and root-mean-square (RMS) roughness values are reported for the initial stages of island growth from submonolayer thicknesses up to a few monolayers. RMS roughness was also investigated for later stages of the growth. Our results show that, for small deposition angles and with limited or no surface diffusion included, the average growth rate of islands is faster in lateral directions that results in enhanced layer coverages and smoother films. This is due to that the sides of the islands can be exposed to the incident flux more effectively at small deposition angles. On the other hand, normal incidence and high oblique angle depositions give poorer layer coverages and much rougher films due to the slower growth rates in lateral directions.


2020 ◽  
Author(s):  
Fabio Grillo ◽  
Job Soethoudt ◽  
Esteban A. Marques ◽  
Lilian de Martin ◽  
Kaat Van Dongen ◽  
...  

<div> <div> <p>Area-selective deposition (ASD) enables the growth of materials on target regions of patterned substrates for applications in fields ranging from microelectronics to catalysis. Selectivity is often achieved through surface modifications aimed at suppressing or promoting the adsorption of precursor molecules. Here we show, instead, that varying the surface composition can enable ASD by affecting surface diffusion rather than adsorption. Ru deposition from (carbonyl)- (alkylcyclohexadienyl)Ru and H<sub>2</sub> produces smooth films on metal nitrides and nanoparticles on SiO<sub>2</sub>. The latter form by surface diffusion and aggregation of Ru adspecies. Kinetic modeling shows that changing the surface termination of SiO<sub>2</sub> from -OH to -CH<sub>3</sub>, and thus its surface energy, leads to larger and fewer nanoparticles because of a 1000-fold increase in surface diffusion rates. Kinetic Monte Carlo simulations show that even surface diffusion alone can enable ASD because adspecies tend to migrate from high- to low-diffusivity regions. This is corroborated by deposition experiments on 3D TiN-SiO<sub>2</sub> nanopatterns, which are consistent with Ru migrating from SiO<sub>2</sub> to TiN. Such insights not only have implications for the interpretation of experimental results but may also inform new ASD protocols, based on chemical vapor and atomic layer deposition, that take advantage of surface diffusion.</p></div></div>


1994 ◽  
Vol 340 ◽  
Author(s):  
J. Deppe ◽  
J. V. Lill ◽  
D. J. Godbey ◽  
K. D. Hobart

ABSTRACTThe temperature dependence of germanium surface segregation during growth by solid source SiGe molecular beam epitaxy was studied by x-ray photoelectron spectroscopy and kinetic Monte Carlo (KMC) modeling. Germanium segregation persisted at temperatures 60ºC below that predicted by a two-state exchange model. KMC simulations, where film growth, surface diffusion, and surface segregation are modeled consistently, successfully describe the low temperature segregation of germanium. Realistic descriptions of MBE must follow the physical rates of the growth, surface diffusion, and surface segregation processes. A specific, step mediated exchange mechanism is also considered and shown to lead to surface segregation. While this model of Ge segregation seems possible, more work is necessary to obtain a consistent set of energy barriers.


2020 ◽  
Vol 183 ◽  
pp. 109789 ◽  
Author(s):  
Jyri Kimari ◽  
Ville Jansson ◽  
Simon Vigonski ◽  
Ekaterina Baibuz ◽  
Roberto Domingos ◽  
...  

1999 ◽  
Vol 06 (03n04) ◽  
pp. 323-340 ◽  
Author(s):  
F. NIETO ◽  
A. A. TARASENKO ◽  
V. PEREYRA ◽  
C. UEBING

In this paper we study the influence of adsorption-induced surface reconstruction on both the adsorption–desorption kinetics and the surface diffusion of adsorbed particles. The reconstruction is regarded as an order–disorder phase transition, which is described in the framework of the lattice gas formalism by the two-position (2P) model. Adsorption is studied by kinetic Monte Carlo simulations while surface diffusion coefficients are evaluated by using the classical Monte Carlo modeling. Utilizing Monte Carlo simulations we characterize how the behavior of the chemical (D), the jump (DJ) and the tracer (D*) diffusion coefficients depend on the different ordered phases predicted by the model.


2021 ◽  
Author(s):  
◽  
Sione Paea

<p>This thesis uses the kinetic Monte Carlo (KMC) algorithm to examine the growth morphology and structure of nanocrystals. Crystal growth in a supersaturated gas of atoms and in an undercooled binary melt is investigated. First, in the gas phase, the interplay of the deposition and surface diffusion rates is studied. Then, the KMC algorithm is refined by including solidification events and finally, by adding diffusion in the surrounding liquid. A new algorithm is developed for modelling solidification from an undercooled melt. This algorithm combines the KMC method, which models the change in shape of the crystal during growth, with a macroscopic continuum method that tracks the diffusion of material through solution towards the crystal. For small length and time scales, this approach provides simple, effective front tracking with fully resolved atomistic detail of the crystal-melt interface. Anisotropy is included in the model as a surface diffusion process and the growth rate of the crystal is found to increase monotonically with increase in the surface anisotropy value. The method allows for the study of multiple crystal nuclei and Ostwald ripening. This method will aid researchers to explain why certain crystal shapes form under particular conditions during growth, and may enable nanotechnologists to design techniques for growing nanocrystals with specific shapes for a variety of applications, from catalysis to the medicine field and electronics industry. This will lead to a better understanding of the atomistic process of crystal growth at the nanoscale.</p>


2020 ◽  
Author(s):  
Fabio Grillo ◽  
Job Soethoudt ◽  
Esteban A. Marques ◽  
Lilian de Martin ◽  
Kaat Van Dongen ◽  
...  

<div> <div> <p>Area-selective deposition (ASD) enables the growth of materials on target regions of patterned substrates for applications in fields ranging from microelectronics to catalysis. Selectivity is often achieved through surface modifications aimed at suppressing or promoting the adsorption of precursor molecules. Here we show, instead, that varying the surface composition can enable ASD by affecting surface diffusion rather than adsorption. Ru deposition from (carbonyl)- (alkylcyclohexadienyl)Ru and H<sub>2</sub> produces smooth films on metal nitrides and nanoparticles on SiO<sub>2</sub>. The latter form by surface diffusion and aggregation of Ru adspecies. Kinetic modeling shows that changing the surface termination of SiO<sub>2</sub> from -OH to -CH<sub>3</sub>, and thus its surface energy, leads to larger and fewer nanoparticles because of a 1000-fold increase in surface diffusion rates. Kinetic Monte Carlo simulations show that even surface diffusion alone can enable ASD because adspecies tend to migrate from high- to low-diffusivity regions. This is corroborated by deposition experiments on 3D TiN-SiO<sub>2</sub> nanopatterns, which are consistent with Ru migrating from SiO<sub>2</sub> to TiN. Such insights not only have implications for the interpretation of experimental results but may also inform new ASD protocols, based on chemical vapor and atomic layer deposition, that take advantage of surface diffusion.</p></div></div>


Sign in / Sign up

Export Citation Format

Share Document