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Stochastic model and simulation of GaN nanowires formation characterized by long incubation time followed by burst nucleation and growth. / Sabelfeld, K. K.; Kablukova, E. G.

In: Computational Materials Science, Vol. 213, 111664, 10.2022.

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Sabelfeld KK, Kablukova EG. Stochastic model and simulation of GaN nanowires formation characterized by long incubation time followed by burst nucleation and growth. Computational Materials Science. 2022 Oct;213:111664. doi: 10.1016/j.commatsci.2022.111664

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@article{14673f6a9f774bb0b5283a04fc025444,
title = "Stochastic model and simulation of GaN nanowires formation characterized by long incubation time followed by burst nucleation and growth",
abstract = "A combined kinetic Monte Carlo algorithm and continuous thermodynamically based model for simulation of heterogeneous nucleation of GaN islands on a substrate under burst regime when a long incubation time is followed by a rapid nucleation of stable nuclei is developed. In this model the kinetics of GaN islands nucleation on a substrate during their aggregation and disaggregation is simulated directly by the Monte Carlo method while the size of stable nuclei is taken from the thermodynamic theory of nucleation with varying supersaturation under metastable conditions. Simulations are carried out in conditions close to the experimental data on the GaN nanowires growth. Both the height and radius distributions of nanowires are extracted from the simulations which start from the very beginning of the incident Ga and N atoms on a substrate and finish with a stable distribution of nanowires whose radii ri stopped to grow and the heights become 2–4 times larger than their radii. In contrast to standard Becker–D{\"o}ring nucleation theory, where the following general behavior is known: the longer the incubation time the slower the nucleation rate, a combined hybrid Monte Carlo and metastable thermodynamic model suggested is able to predict a long incubation time followed by relatively rapid nucleation regime. A series of numerical simulations presented supports this conclusion.",
keywords = "Adatoms, Burst nucleation, Epitaxy, Incubation time, Monte Carlo kinetic algorithm, Nanowires, Surface diffusion",
author = "Sabelfeld, {K. K.} and Kablukova, {E. G.}",
note = "Funding Information: The authors gratefully acknowledge the financial support of the Russian Science Foundation under Grant 19-11-00019 . Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.",
year = "2022",
month = oct,
doi = "10.1016/j.commatsci.2022.111664",
language = "English",
volume = "213",
journal = "Computational Materials Science",
issn = "0927-0256",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Stochastic model and simulation of GaN nanowires formation characterized by long incubation time followed by burst nucleation and growth

AU - Sabelfeld, K. K.

AU - Kablukova, E. G.

N1 - Funding Information: The authors gratefully acknowledge the financial support of the Russian Science Foundation under Grant 19-11-00019 . Publisher Copyright: © 2022 Elsevier B.V.

PY - 2022/10

Y1 - 2022/10

N2 - A combined kinetic Monte Carlo algorithm and continuous thermodynamically based model for simulation of heterogeneous nucleation of GaN islands on a substrate under burst regime when a long incubation time is followed by a rapid nucleation of stable nuclei is developed. In this model the kinetics of GaN islands nucleation on a substrate during their aggregation and disaggregation is simulated directly by the Monte Carlo method while the size of stable nuclei is taken from the thermodynamic theory of nucleation with varying supersaturation under metastable conditions. Simulations are carried out in conditions close to the experimental data on the GaN nanowires growth. Both the height and radius distributions of nanowires are extracted from the simulations which start from the very beginning of the incident Ga and N atoms on a substrate and finish with a stable distribution of nanowires whose radii ri stopped to grow and the heights become 2–4 times larger than their radii. In contrast to standard Becker–Döring nucleation theory, where the following general behavior is known: the longer the incubation time the slower the nucleation rate, a combined hybrid Monte Carlo and metastable thermodynamic model suggested is able to predict a long incubation time followed by relatively rapid nucleation regime. A series of numerical simulations presented supports this conclusion.

AB - A combined kinetic Monte Carlo algorithm and continuous thermodynamically based model for simulation of heterogeneous nucleation of GaN islands on a substrate under burst regime when a long incubation time is followed by a rapid nucleation of stable nuclei is developed. In this model the kinetics of GaN islands nucleation on a substrate during their aggregation and disaggregation is simulated directly by the Monte Carlo method while the size of stable nuclei is taken from the thermodynamic theory of nucleation with varying supersaturation under metastable conditions. Simulations are carried out in conditions close to the experimental data on the GaN nanowires growth. Both the height and radius distributions of nanowires are extracted from the simulations which start from the very beginning of the incident Ga and N atoms on a substrate and finish with a stable distribution of nanowires whose radii ri stopped to grow and the heights become 2–4 times larger than their radii. In contrast to standard Becker–Döring nucleation theory, where the following general behavior is known: the longer the incubation time the slower the nucleation rate, a combined hybrid Monte Carlo and metastable thermodynamic model suggested is able to predict a long incubation time followed by relatively rapid nucleation regime. A series of numerical simulations presented supports this conclusion.

KW - Adatoms

KW - Burst nucleation

KW - Epitaxy

KW - Incubation time

KW - Monte Carlo kinetic algorithm

KW - Nanowires

KW - Surface diffusion

UR - http://www.scopus.com/inward/record.url?scp=85134768501&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/b45c71f3-b1cb-331e-b33d-603cea701520/

U2 - 10.1016/j.commatsci.2022.111664

DO - 10.1016/j.commatsci.2022.111664

M3 - Article

AN - SCOPUS:85134768501

VL - 213

JO - Computational Materials Science

JF - Computational Materials Science

SN - 0927-0256

M1 - 111664

ER -

ID: 36730113