The correlation between graphene characteristic parameters and resonant frequencies by Monte Carlo based stochastic finite element model

Chu, Liu; Shi, Jiajia; Cursi, Eduardo Souza de

doi:10.1038/s41598-021-02429-2

Cited by 3 publications

(2 citation statements)

References 78 publications

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“…The geometrical configuration of graphene lattice and the corresponding geometri and material parameters are defined as above, while the resonant frequencies and def mations under different vibration modes are computed based on the finite eleme method. The first four order resonant frequencies of the pristine graphene without v cancy defects are 1.7282, 3.2925, 3.7442, and 5.189 THz, respectively, which reaches go agreement with the reported literature [28,30,31].…”

Section: Flowchart Of Fingerprint Compilersupporting

confidence: 90%

The Fingerprints of Resonant Frequency for Atomic Vacancy Defect Identification in Graphene

2021

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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.

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Section: Flowchart Of Fingerprint Compilersupporting

confidence: 90%

The Fingerprints of Resonant Frequency for Atomic Vacancy Defect Identification in Graphene

2021

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“…The special periodic honeycomb lattice of graphene leads to its extraordinary properties in thermal, electronic, optical and mechanical aspects [26]. The characteristic parameters corresponding with the geometrical properties include the thickness, length of the hexagon edge, and the length and width of graphene sheets [27]. According to the measurements and computation in the reported literature [28,29], the mentioned geometrical parameters are settled with certain values for parallel comparison.…”

Section: Graphene With Random Porositiesmentioning

confidence: 99%

The shear strain energy fluctuations caused by random porosities in graphene based on the stochastic finite element model

2022

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Strain-induced deformation is a promising strategy to modify and functionalize the material properties of graphene. However, the impacts of random porosities are inevitable and complicated in the microstructure. In order to quantify and analyze the effects of random porosities in graphene under shear stress, the energy fluctuations and the equivalent elastic modulus are computed and recorded based on the stochastic finite element model. The finite element computation is combined with the Monte Carlo stochastic sampling procedure to distribute and propagate the random porosities in pristine graphene. Two different boundary conditions are taken into consideration and compared. Furthermore, the probability statistics of shear strain energy and equivalent elastic modulus are provided based on the comparison with the results of pristine graphene. The inhomogeneous spatial randomness is founded in the statistic records of shear strain energy. The sensitivity to the graphene chirality and boundary conditions are also shown for the porous graphene. The work in this paper provides important references for strain-induced engineering and artificial functionalization through topological vacancy control in graphene.

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A review of stochastic finite element and nonparametric modelling for ship propulsion shaft dynamic alignment

Lu,

Li,

Xing

et al. 2023

Ocean Engineering

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The correlation between graphene characteristic parameters and resonant frequencies by Monte Carlo based stochastic finite element model

Cited by 3 publications

References 78 publications

The Fingerprints of Resonant Frequency for Atomic Vacancy Defect Identification in Graphene

The Fingerprints of Resonant Frequency for Atomic Vacancy Defect Identification in Graphene

The shear strain energy fluctuations caused by random porosities in graphene based on the stochastic finite element model

A review of stochastic finite element and nonparametric modelling for ship propulsion shaft dynamic alignment

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