Carbon nanotube growth: First-principles-based kinetic Monte Carlo model

Li, Jingde; Croiset, Eric; Ricardez‐Sandoval, Luis A.

doi:10.1016/j.jcat.2015.03.010

Cited by 46 publications

(27 citation statements)

References 119 publications

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“…As regards the effect of the temperature, an increase of j C0 ,  d and  r is observed in accordance with equations (9) to (11). From these expressions, the estimated value of the apparent activation energy for j C 0 is 103.2 kJ/mol, which comes within the interval of the values published in the literature (80-130 kJ/mol) for the intrinsic growth rate of carbonaceous nanomaterials such as graphene or carbon nanotubes [61,62,63].…”

Section: Development Of the Kinetic Growth Modelsupporting

confidence: 83%

Synthesis of graphenic nanomaterials by decomposition of methane on a Ni-Cu/biomorphic carbon catalyst. Kinetic and characterization results

et al. 2018

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This work addresses the preparation and application of the synthesis of graphene in Ni-Cu catalysts supported on carbonaceous materials. The catalysts have been prepared by a biomorphic mineralization technique which involves the thermal decomposition, under reductive atmosphere, of commercial cellulose previously impregnated with the metallic precursors. The characterization results indicate that the preparation method leads to the formation of carbonaceous supports with a moderate microporosity (ca. 33% pore volume) and adequate surface area (343 m 2 /g), maintaining the original external texture. The catalytic performance of these materials was previously tested in liquid phase reactions [11]. In order to extend the use of these catalysts, in this work we present a study corresponding to a gas phase reaction: the synthesis of graphenic nanomaterials by catalytic decomposition of methane (CDM). The influence of the reaction temperature and of the feed composition (i.e. %CH 4 and %H 2) has been studied. The graphenic nanomaterials obtained after reaction were characterized by nitrogen adsorption-desorption isotherms, Raman spectroscopy and transmission electron microscopy (TEM). The results indicate that the carbonaceous nanomaterial with the highest quality is obtained operating at 950 °C and feeding 28.6% of CH 4 and 14.3% of H 2. The evolution of the carbon mass during the reaction time was analysed using a phenomenological kinetic model that takes into account the main stages involved during the formation of carbonaceous nanomaterials (NCMs). The results obtained from the kinetic model along with the characterization results enable the influence of the operating variables on each stage of the carbonaceous nanomaterial formation to be discerned.

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Section: Development Of the Kinetic Growth Modelsupporting

confidence: 83%

Synthesis of graphenic nanomaterials by decomposition of methane on a Ni-Cu/biomorphic carbon catalyst. Kinetic and characterization results

et al. 2018

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“…Uncertainty was also applied to the adsorption constant k a;A as it is the key parameter affecting the adsorption rate of species A. Parameter uncertainty descriptions can be obtained from experiments or DFT simulations. [69][70][71][72] A multivariate normal PDF is often regarded as a practical approximation to describe the parametric uncertainty in most engineering applications. Accordingly, the key uncertain parameters considered in this work were assumed to be normally distributed around the mean values ofk a;A ;Ê Figure 7 displays the effects of the uncertainty on the output fluid concentrations C out;i 0; L ð Þ for each chemical species, determined using both the primary multiscale reactor model and the second-order PSE.…”

Section: Uncertainty Analysismentioning

confidence: 99%

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

2016

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in Wiley Online Library (wileyonlinelibrary.com) Multiscale models have been developed to simulate the behavior of spatially-heterogeneous porous catalytic flow reactors, i.e., multiscale reactors whose concentrations are spatially-dependent. While such a model provides an adequate representation of the catalytic reactor, model-plant mismatch can significantly affect the reactor's performance in control and optimization applications. In this work, power series expansion (PSE) is applied to efficiently propagate parametric uncertainty throughout the spatial domain of a heterogeneous multiscale catalytic reactor model. The PSE-based uncertainty analysis is used to evaluate and compare the effects of uncertainty in kinetic parameters on the chemical species concentrations throughout the length of the reactor. These analyses reveal that uncertainty in the kinetic parameters and in the catalyst pore radius have a substantial effect on the reactor performance. The application of the uncertainty quantification methodology is illustrated through a robust optimization formulation that aims to maximize productivity in the presence of uncertainty in the parameters.

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“…Metallic surfaces have attracted many experimental and theoretical studies in the last decades . In the last few years there has been a growing interest in technological application including thin film growth, surface catalysis, chemical reactions, and mass transport . Optical, magnetic, electronic, and structural properties of crystal grown systems have been experimentally investigated .…”

Section: Introductionmentioning

confidence: 99%

Kinetic Monte Carlo Simulation of Au Monolayer Evolution on Cu(110)

Dardouri

Hassani

Hasnaoui

et al. 2018

Physica Status Solidi (b)

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In this work, results obtained from the kinetic Monte Carlo (KMC) simulations are presented. The KMC is based on embedded‐atom method (EAM) energetics findings. The evolution of the surface morphology in Au/Cu(110) is investigated. All activation barriers are calculated by the EAM method and used in the anisotropic bond‐breaking model (ABBM). In this study, the effects of substrate temperature and deposition rate are taken into account, with the coverage rate equal to 5%. The results show the transition from 1D to 2D in the morphology of the monolayer when the temperature increases. However, when the flux decreases the density of islands increases. During monolayer evolution, many phenomena are observed such as nucleation, coalescence, evolution of island density, and geometry transition.

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Carbon nanotube growth: First-principles-based kinetic Monte Carlo model

Cited by 46 publications

References 119 publications

Synthesis of graphenic nanomaterials by decomposition of methane on a Ni-Cu/biomorphic carbon catalyst. Kinetic and characterization results

Synthesis of graphenic nanomaterials by decomposition of methane on a Ni-Cu/biomorphic carbon catalyst. Kinetic and characterization results

Distributional uncertainty analysis and robust optimization in spatially heterogeneous multiscale process systems

Kinetic Monte Carlo Simulation of Au Monolayer Evolution on Cu(110)

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