Growth of nitrogen-doped graphene on copper: Multiscale simulations

Gaillard, Philippe; Schoenhalz, Aline L.; Moskovkin, Pavel; Lucas, Stéphane; Henrard, Luc

doi:10.1016/j.susc.2015.08.038

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…Interestingly, the number of layers in N-graphene may vary within the same domain. This could be due to a difference in the diffusion of carbon and nitrogen atoms over copper [21].…”

Section: Discussionmentioning

confidence: 99%

“…This form of the inserted nitrogen, so-called graphitic N, is the most preferable energetically [20]. At low synthesis temperatures, pyridinic N and pyrrolic N forms are usually realized [21,22]. The former nitrogen is a two-coordinated atom located at boundaries of vacancies or graphene domains, while the latter nitrogen is in a carbon pentagonal ring and bonded with hydrogen atom.…”

Section: Introductionmentioning

confidence: 99%

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Electronic Structure of Nitrogen- and Phosphorus-Doped Graphenes Grown by Chemical Vapor Deposition Method

et al. 2020

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Heteroatom doping is a widely used method for the modification of the electronic and chemical properties of graphene. A low-pressure chemical vapor deposition technique (CVD) is used here to grow pure, nitrogen-doped and phosphorous-doped few-layer graphene films from methane, acetonitrile and methane-phosphine mixture, respectively. The electronic structure of the films transferred onto SiO2/Si wafers by wet etching of copper substrates is studied by X-ray photoelectron spectroscopy (XPS) and near-edge X-ray absorption fine structure (NEXAFS) spectroscopy using a synchrotron radiation source. Annealing in an ultra-high vacuum at ca. 773 K allows for the removal of impurities formed on the surface of films during the synthesis and transfer procedure and changes the chemical state of nitrogen in nitrogen-doped graphene. Core level XPS spectra detect a low n-type doping of graphene film when nitrogen or phosphorous atoms are incorporated in the lattice. The electrical sheet resistance increases in the order: graphene < P-graphene < N-graphene. This tendency is related to the density of defects evaluated from the ratio of intensities of Raman peaks, valence band XPS and NEXAFS spectroscopy data.

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“…Interestingly, the number of layers in N-graphene may vary within the same domain. This could be due to a difference in the diffusion of carbon and nitrogen atoms over copper [21].…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electronic Structure of Nitrogen- and Phosphorus-Doped Graphenes Grown by Chemical Vapor Deposition Method

et al. 2020

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“…KMC has been extensively applied to simulate the growth of graphene, e.g., the evolution of vacancy complexes and formation of vacancy chains 155 , the formation and kinetic effect of multimember carbon ring complexes 156 , etching of graphene GBs due to oxygen migration and reaction 157 , and GB evolution following the Stone-Wales mechanism 158 . Growth of nanoscale graphene islands from carbon monomer nuclei or pre-existing growth fronts has been captured, showing catalytic growth behaviors 159 , anisotropic morphological patterns 160 , temperature-and deposition-flux-ratedependent size and shapes 161 , inhomogeneous and nonlinear growth kinetics due to lattice mismatch 162 , and geometrydetermined growth mechanisms 163 . The "coastline" graphene morphology during sublimation 164 and the step-flow growth of epitaxial graphene have also been reported 165 .…”

Section: Mesoscale Simulationsmentioning

confidence: 99%

Multiscale computational understanding and growth of 2D materials: a review

et al. 2020

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The successful discovery and isolation of graphene in 2004, and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional (2D) materials that explore their growth, new atomic-scale physics, and potential device applications. This review aims to provide an overview of theoretical, computational, and machine learning methods and tools at multiple length and time scales, and discuss how they can be utilized to assist/guide the design and synthesis of 2D materials beyond graphene. We focus on three methods at different length and time scales as follows: (i) nanoscale atomistic simulations including density functional theory (DFT) calculations and molecular dynamics simulations employing empirical and reactive interatomic potentials; (ii) mesoscale methods such as phase-field method; and (iii) macroscale continuum approaches by coupling thermal and chemical transport equations. We discuss how machine learning can be combined with computation and experiments to understand the correlations between structures and properties of 2D materials, and to guide the discovery of new 2D materials. We will also provide an outlook for the applications of computational approaches to 2D materials synthesis and growth in general.npj Computational Materials (2020) 6:22 ; https://doi.

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“…Furthermore, we assumed a perfect, fixed Cu(111) substrate and a monolayer of graphene, as most experimental studies aim to produce high-quality monolayer graphene sheets with uniform properties that are comparable to exfoliated graphene [91]. We did not account for defects in the copper substrate or doping by other atoms, such as nitrogen, as has been reported in previous studies [60,92]. Therefore, the method developed here cannot simulate a wide range of possible experimental conditions.…”

Section: Discussionmentioning

confidence: 99%

Multiscale Model of CVD Growth of Graphene on Cu(111) Surface

Esmaeilpour

Bügel

Fink

et al. 2023

IJMS

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Due to its outstanding properties, graphene has emerged as one of the most promising 2D materials in a large variety of research fields. Among the available fabrication protocols, chemical vapor deposition (CVD) enables the production of high quality single-layered large area graphene. To better understand the kinetics of CVD graphene growth, multiscale modeling approaches are sought after. Although a variety of models have been developed to study the growth mechanism, prior studies are either limited to very small systems, are forced to simplify the model to eliminate the fast process, or they simplify reactions. While it is possible to rationalize these approximations, it is important to note that they have non-trivial consequences on the overall growth of graphene. Therefore, a comprehensive understanding of the kinetics of graphene growth in CVD remains a challenge. Here, we introduce a kinetic Monte Carlo protocol that permits, for the first time, the representation of relevant reactions on the atomic scale, without additional approximations, while still reaching very long time and length scales of the simulation of graphene growth. The quantum-mechanics-based multiscale model, which links kinetic Monte Carlo growth processes with the rates of occurring chemical reactions, calculated from first principles makes it possible to investigate the contributions of the most important species in graphene growth. It permits the proper investigation of the role of carbon and its dimer in the growth process, thus indicating the carbon dimer to be the dominant species. The consideration of hydrogenation and dehydrogenation reactions enables us to correlate the quality of the material grown within the CVD control parameters and to demonstrate an important role of these reactions in the quality of the grown graphene in terms of its surface roughness, hydrogenation sites, and vacancy defects. The model developed is capable of providing additional insights to control the graphene growth mechanism on Cu(111), which may guide further experimental and theoretical developments.

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Growth of nitrogen-doped graphene on copper: Multiscale simulations

Cited by 7 publications

References 23 publications

Electronic Structure of Nitrogen- and Phosphorus-Doped Graphenes Grown by Chemical Vapor Deposition Method

Electronic Structure of Nitrogen- and Phosphorus-Doped Graphenes Grown by Chemical Vapor Deposition Method

Multiscale computational understanding and growth of 2D materials: a review

Multiscale Model of CVD Growth of Graphene on Cu(111) Surface

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