Computational Discrete Methods in Nanomaterials

A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Theory and Simulation of Nanostructures".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 7325

Special Issue Editor


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Guest Editor
School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW 2006, Australia
Interests: molecular and mesoscale simulations; microfluidics; nanofluidics; nanoparticles; polymer crystallization; tribology; rheology; computational nanotechnology
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Special Issue Information

Dear Colleagues,

This Special Issue covers research in using discrete methods such as molecular dynamics (MD), dissipative particle dynamics (DPD), and lattice–Boltzmann (LB) and hybrid finite element methods and other multiscale methods in the simulation of materials at the nanoscale. In a continuum-based approach in traditional finite element and finite volume methods, the simulations are conducted by discretization of appropriate constitutive equations that describe the behavior of materials using partial differential equations, and their solutions lead to obtaining material response, stress–strain, pressure, temperature, and other field information. In discrete methods using, for example, atoms, particles or discrete elements, the properties of the materials and their behavior are obtained directly from the interaction of discrete elements and particles. While traditional computational methods such as finite elements and finite volume methods have served the research community immensely, they have limitations and may not be applicable in some situations. This may be due to lack of appropriate constitutive models, or breakdown of the continuity, or when dealing with fast processes where transient spatiotemporal properties of the nanomaterials need to be determined at the nanoscale. In such situations, particle-based methods offer an alternative approach. Examples of research in the application of these methods may include nanomaterials crystallization and phase transition, nanoparticles formation, impact mechanics at the nanoscale, nanocomposite materials, nanomaterial deformation, nanomechanics, and nanotribology. In this Special Issue, we would like to bring together some of the latest progress in the field and provide a stepping stone for future developments. Contributions in the form of original new research or reviews of the most recent developments in this area are welcome.

Prof. Dr. Ahmad Jabbarzadeh
Guest Editor

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Published Papers (3 papers)

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Research

13 pages, 4627 KiB  
Article
The Fingerprints of Resonant Frequency for Atomic Vacancy Defect Identification in Graphene
by Liu Chu, Jiajia Shi and Eduardo Souza de Cursi
Nanomaterials 2021, 11(12), 3451; https://doi.org/10.3390/nano11123451 - 20 Dec 2021
Cited by 4 | Viewed by 2360
Abstract
The identification of atomic vacancy defects in graphene is an important and challenging issue, which involves inhomogeneous spatial randomness and requires high experimental conditions. In this paper, the fingerprints of resonant frequency for atomic vacancy defect identification are provided, based on the database [...] Read more.
The identification of atomic vacancy defects in graphene is an important and challenging issue, which involves inhomogeneous spatial randomness and requires high experimental conditions. In this paper, the fingerprints of resonant frequency for atomic vacancy defect identification are provided, based on the database of massive samples. Every possible atomic vacancy defect in the graphene lattice is considered and computed by the finite element model in sequence. Based on the sample database, the histograms of resonant frequency are provided to compare the probability density distributions and interval ranges. Furthermore, the implicit relationship between the locations of the atomic vacancy defects and the resonant frequencies of graphene is established. The fingerprint patterns are depicted by mapping the locations of atomic vacancy defects to the resonant frequency magnitudes. The geometrical characteristics of computed fingerprints are discussed to explore the feasibility of atomic vacancy defects identification. The work in this paper provides meaningful supplementary information for non-destructive defect detection and identification in nanomaterials. Full article
(This article belongs to the Special Issue Computational Discrete Methods in Nanomaterials)
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11 pages, 18076 KiB  
Article
Efficient Structural Relaxation of Polycrystalline Graphene Models
by Federico D’Ambrosio, Joris Barkema and Gerard T. Barkema
Nanomaterials 2021, 11(5), 1242; https://doi.org/10.3390/nano11051242 - 08 May 2021
Cited by 3 | Viewed by 1623
Abstract
Large samples of experimentally produced graphene are polycrystalline. For the study of this material, it helps to have realistic computer samples that are also polycrystalline. A common approach to produce such samples in computer simulations is based on the method of Wooten, Winer, [...] Read more.
Large samples of experimentally produced graphene are polycrystalline. For the study of this material, it helps to have realistic computer samples that are also polycrystalline. A common approach to produce such samples in computer simulations is based on the method of Wooten, Winer, and Weaire, originally introduced for the simulation of amorphous silicon. We introduce an early rejection variation of their method, applied to graphene, which exploits the local nature of the structural changes to achieve a significant speed-up in the relaxation of the material, without compromising the dynamics. We test it on a 3200 atoms sample, obtaining a speed-up between one and two orders of magnitude. We also introduce a further variation called early decision specifically for relaxing large samples even faster, and we test it on two samples of 10,024 and 20,000 atoms, obtaining a further speed-up of an order of magnitude. Furthermore, we provide a graphical manipulation tool to remove unwanted artifacts in a sample, such as bond crossings. Full article
(This article belongs to the Special Issue Computational Discrete Methods in Nanomaterials)
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13 pages, 2771 KiB  
Article
The Origins of Enhanced and Retarded Crystallization in Nanocomposite Polymers
by Ahmad Jabbarzadeh
Nanomaterials 2019, 9(10), 1472; https://doi.org/10.3390/nano9101472 - 16 Oct 2019
Cited by 21 | Viewed by 2882
Abstract
Controlling the crystallinity of hybrid polymeric systems has an important impact on their properties and is essential for developing novel functional materials. The crystallization of nanocomposite polymers with gold nanoparticles is shown to be determined by free space between nanoparticles. Results of large-scale [...] Read more.
Controlling the crystallinity of hybrid polymeric systems has an important impact on their properties and is essential for developing novel functional materials. The crystallization of nanocomposite polymers with gold nanoparticles is shown to be determined by free space between nanoparticles. Results of large-scale molecular dynamics simulations reveal while crystallinity is affected by the nanoparticle size and its volume fraction, their combined effects can only be measured by interparticle free space and characteristic size of the crystals. When interparticle free space becomes smaller than the characteristic extended length of the polymer molecule, nanoparticles impede the crystallization because of the confinement effects. Based on the findings from this work, equations for critical particle size or volume fraction that lead to this confinement-induced retardation of crystallization are proposed. The findings based on these equations are demonstrated to agree with the results reported in experiments for nanocomposite systems. The results of simulations also explain the origin of a two-tier crystallization regime observed in some of the hybrid polymeric systems with planar surfaces where the crystallization is initially enhanced and then retarded by the presence of nanoparticles. Full article
(This article belongs to the Special Issue Computational Discrete Methods in Nanomaterials)
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