Description of melting of aluminum nanoparticles

Федоров, А. В.; Shul’gin, A. V.; Lavruk, S. A.

doi:10.1134/s0010508216040092

Cited by 9 publications

(5 citation statements)

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“…After imitating to 5.2 ps, in consequence of the sudden inflexion of the potential energy curve and structural disorders seen from Figure 4b, it is observed that nanoaluminum core melts with the melting temperature of 730 K, which is in line with the experimental and theoretical works. 7,16,45 When heating to 9.5 ps, the occurence of the melting of alumina shell with the value of ca. 1153 K is also received, as expressed in Figure 4c.…”

Section: Resultsmentioning

confidence: 99%

Responses of Core–Shell Al/Al₂O₃ Nanoparticles to Heating: ReaxFF Molecular Dynamics Simulations

et al. 2018

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Molecular dynamics simulations combined with the ReaxFF reactive force field were implemented to detailedly study the atomic diffusion behaviors of core–shell Al/Al2O3 nanoparticles. According to atomic mean square displacements, the reaction initialization of core–shell Al/Al2O3 nanoparticles substantially resulted from the inward diffusion of shell oxygen atoms. In particular, the effect of shell thickness on the atomic diffusivities of the system was investigated. The results demonstrated that the diffusivities of core Al atoms and shell O atoms at the core–shell interfaces were irrelevant to the shell thickness during heating process; however, corresponding atomic diffusivities decreased as increasing the shell thickness after heating. A majority of distorted (AlO) n (n = 3, 4, and 5) clusters were ejected from the system surface at later stages, suggesting the detonation of the nanoparticles. The presence of a significant void space was observed when the alumina shell melted, which was in agreement with the experimental evidence. The alumina shell with 1 nm melts at 1153 K, and the melting point enhances with the augment of shell thickness. Furthermore, the electric field-induced atomic diffusion mechanisms of core–shell Al/Al2O3 nanoparticles are obtained as is reported, further providing extensive insights into the ignition mechanisms of passivated aluminum nanoparticles.

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Section: Resultsmentioning

confidence: 99%

Responses of Core–Shell Al/Al₂O₃ Nanoparticles to Heating: ReaxFF Molecular Dynamics Simulations

et al. 2018

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“…These temperatures vary drastically as a function of the small particle size. 70,73 Recently, ML methods have been used to accurately predict the structures of liquids, 74 as well as correctly reproduce the bulk melting temperatures of metals. 75 However, ML methods have not been widely used to study how various surface environments affect the melting temperature.…”

Section: ■ Resultsmentioning

confidence: 99%

“…This disparity becomes more complex when one moves from a slab to a nanoparticle, with small particles showing melting temperatures around 400 K lower than that of the bulk melting temperature. These temperatures vary drastically as a function of the small particle size. , …”

Section: Resultsmentioning

confidence: 99%

Nanoscale Modeling of Surface Phenomena in Aluminum Using Machine Learning Force Fields

Chapman

Ramprasad

2020

J. Phys. Chem. C

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The study of nanoscale surface phenomena is essential in understanding the physical processes that aid in technologically relevant applications, such as catalysis, material growth, and failure nucleation. While experimental observations, such as those based on various forms of microscopy, can be used to better understand surface diffusion mechanisms, the resulting information is often limited in both its spatial and temporal resolution. Therefore, computational methodologies have become critical in the study of the processes that occur in these domains. Until recently, these methodologies have fallen into two broad categories: quantum mechanics (QM) based methods and semiempirical/classical methods. The former are computationally demanding, but accurate and versatile, while the latter are computationally inexpensive, but are significantly limited in their versatility. Machine learning (ML) methods have shown the potential to bridge these two domains by combining the cost of classical methods with the accuracy of QM. In this work, we employ recently developed ML models to simulate a variety of surface phenomena of aluminum. Adatom diffusion barriers were predicted via nudged elastic band calculations. Surface dynamics were also considered by studying the melting temperature of Al slabs and nanoparticles, along with the epitaxial growth of the Al (110) surface.

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“…In recent years, new experimental data and theoretical models have appeared on the ignition, melting, heat transfer and combustion of submicron and nanoscale aluminum particles [16][17][18]. In this paper, we present a physico-mathematical model of detonation of nanoscale aluminum particles, which is a modification of the model [5,6].…”

Section: Introductionmentioning

confidence: 99%

Physical and mathematical model of detonation in aluminum gas suspensions with regard for transition processes of nanosized particle flow, heat transfer and combustion

Khmel

Федоров

2017

AIP Conference Proceedings

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Description of melting of aluminum nanoparticles

Cited by 9 publications

References 1 publication

Responses of Core–Shell Al/Al₂O₃ Nanoparticles to Heating: ReaxFF Molecular Dynamics Simulations

Responses of Core–Shell Al/Al₂O₃ Nanoparticles to Heating: ReaxFF Molecular Dynamics Simulations

Nanoscale Modeling of Surface Phenomena in Aluminum Using Machine Learning Force Fields

Physical and mathematical model of detonation in aluminum gas suspensions with regard for transition processes of nanosized particle flow, heat transfer and combustion

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Description of melting of aluminum nanoparticles

Cited by 9 publications

References 1 publication

Responses of Core–Shell Al/Al2O3 Nanoparticles to Heating: ReaxFF Molecular Dynamics Simulations

Responses of Core–Shell Al/Al2O3 Nanoparticles to Heating: ReaxFF Molecular Dynamics Simulations

Nanoscale Modeling of Surface Phenomena in Aluminum Using Machine Learning Force Fields

Physical and mathematical model of detonation in aluminum gas suspensions with regard for transition processes of nanosized particle flow, heat transfer and combustion

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Responses of Core–Shell Al/Al₂O₃ Nanoparticles to Heating: ReaxFF Molecular Dynamics Simulations

Responses of Core–Shell Al/Al₂O₃ Nanoparticles to Heating: ReaxFF Molecular Dynamics Simulations