Modeling of Surface Processes as Exemplified by Hydrocarbon Reactions

Garrison, Barbara J.; Kodali, Prasad B. S.; Srivastava, Deepak

doi:10.1021/cr9502155

Cited by 85 publications

(64 citation statements)

References 128 publications

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“…The ability to desorb intact molecular species has been attributed in part to the concerted motion of multiple atoms in the collision cascade. 62,63 Species that are directly impacted by the projectile ion (e.g. hard spheres contact within a fewångstroms of impact) can also be ejected into the gas phase.…”

Section: Static Secondary Ion Mass Spectrometry (Sims)mentioning

confidence: 99%

“…Monte Carlo and molecular dynamics computer simulations have held a central role in determining SIMS and laser desorption mechanisms. 9,62,78,79 Another feature of polyatomic projectile ions that may affect their secondary ion yields is their ability to deposit their kinetic energy closer to the surface. Simulations on the NH 3 -CO-Ni 111 system supported this argument, finding less damage to the underlying substrate by SF 5 C than by Xe C .…”

Section: Static Secondary Ion Mass Spectrometry (Sims)mentioning

confidence: 99%

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Surface mass spectrometry of molecular species

et al. 1999

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Section: Static Secondary Ion Mass Spectrometry (Sims)mentioning

confidence: 99%

Section: Static Secondary Ion Mass Spectrometry (Sims)mentioning

confidence: 99%

Surface mass spectrometry of molecular species

et al. 1999

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“…Therefore, a large number of models has also been implemented to approach surface as well as catalytic studies. 1,2 The design of the time-dependent Monte Carlo (tdMC) algorithms, 3,4 as well as of other equilibrium and nonequilibrium Monte Carlo (MC) and Cellular Automata approaches, [5][6][7][8][9] followed the need of getting deeper insights on the kinetics and the reaction mechanisms also involved in heterogeneous catalysis. [10][11][12][13][14][15][16] The tdMC codes, to be used in catalytic applications, were built, in developing the ZGB-MC 17 model, by introducing dedicated subroutines, 15,16,[18][19][20][21] allowing for "real time" dynamic studies.…”

Section: Introductionmentioning

confidence: 99%

IDEA: Interface dynamics and energetics algorithm

et al. 2007

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IDEA, interface dynamics and energetics algorithm, was implemented, in FORTRAN, under different operating systems to mimic dynamics and energetics of elementary events involved in interfacial processes. The code included a parallel elaboration scheme in which both the stochastic and the deterministic components, involved in the developed physical model, worked simultaneously. IDEA also embodied an optionally running VISUAL subroutine, showing the dynamic energy changes caused by the surface events, e.g., occurring at the gas-solid interface. Monte Carlo and ordinary differential equation system subroutines were employed in a synergistic way to drive the occurrence of the elementary events and to manage the implied energy flows, respectively. Biphase processes, namely isothermal and isobaric adsorption of carbon monoxide on nickel, palladium, and platinum surfaces, were first studied to test the capability of the code in modeling real frames. On the whole, the simulated results showed that IDEA could reproduce the inner characteristics of the studied systems and predict properties not yet experimentally investigated.

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“…While simplified approaches, such as DFTB, alleviate this restriction, both the number of atoms and the attainable time scale remain 1 or 2 orders of magnitude smaller than what can be achieved with classical MD. Therefore, whereas quantum mechanical calculations provide valuable kinetic data and they have a decreasing computational cost (due to cheaper and faster processors), MD simulations are believed to continue playing an important role for the exploration of unknown reaction mechanisms [122]. After the discovery of possible reaction paths, an investigation at higher level of theory might then be appropriate.…”

Section: Detailed Atomistic Simulations Of Nanostructure Growth Procementioning

confidence: 99%

Computer modelling of the plasma chemistry and plasma-based growth mechanisms for nanostructured materials

Bogaerts¹,

Eckert²,

Mao³

et al. 2011

J. Phys. D: Appl. Phys.

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In this review paper, an overview is given of different modeling efforts for plasmas used for the formation and growth of nanostructured materials. This includes both the plasma chemistry, providing information on the precursors for nanostructure formation, as well as the growth processes itself. We limit ourselves to carbon (and silicon) nanostructures. Examples of the plasma modeling comprise nanoparticle formation in silane and hydrocarbon plasmas, as well as the plasma chemistry giving rise to carbon nanostructure formation, such as (ultra)nanocrystalline diamond ((U)NCD) and carbon nanotubes (CNTs). The second part of the paper deals with the simulation of the (plasma-based) growth mechanisms of the same carbon nanostructures, i.e., (U)NCD and CNTs, both by mechanistic modeling and detailed atomistic simulations. IntroductionLow temperature plasmas are playing an increasingly important role for the formation and growth of nanostructures and nanostructured materials (e.g., [1][2][3][4][5][6][7]). To improve the performance of these plasma growth processes, a good insight is desired in the plasma and in the interaction with the growing nanostructures. This insight can be obtained by experimental research, but the latter is not always straightforward, in view of the small dimensions of the nanomaterials and the possible disturbance of the plasma characteristics, e.g., when introducing a probe. Therefore, computer simulations can be very useful to assist in the experimental developments.In the present paper, we will give an overview of different modeling efforts that have been presented in the literature for plasmas used for nanostructure formation. This includes two different aspects. First, a thorough understanding of the plasma behavior is needed. This comprises background gas flow and heating (i.e., fluid dynamics), gas breakdown, transport and heating of the electrons and the so-called heavy particles, as well as the plasma chemistry, i.e., creation and destruction of the various plasma species by chemical reactions. Modeling of the plasma chemistry can give indications on which species are important precursors for the growth of nanostructured materials and how the plasma operating conditions can be tuned to optimize the growth process. Therefore, in this paper we will mainly focus on the plasma chemistry modeling. Several plasma modeling approaches exist in literature, such as analytical models, zero-dimensional (0D) chemical kinetics simulations, fluid models (describing the chemical kinetics as well as fluid dynamics), solving the Boltzmann transport equation, Monte Carlo (MC) and particle-in-cell (PIC)-MC simulations, as well as hybrid methods, combining the above (e.g., MC + fluid) models. For describing the plasma chemistry, the 0D chemical kinetics approach is often applied, as it can take into account a large number of different species and reactions without too much computational effort. However, it assumes a spatially uniform plasma composition and does not consider transport of the plasm...

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Modeling of Surface Processes as Exemplified by Hydrocarbon Reactions

Cited by 85 publications

References 128 publications

Surface mass spectrometry of molecular species

Surface mass spectrometry of molecular species

IDEA: Interface dynamics and energetics algorithm

Computer modelling of the plasma chemistry and plasma-based growth mechanisms for nanostructured materials

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