Estimating Young's modulus of graphene/polymer composites using stochastic multi-scale modeling

Rafiee, Roham; Eskandariyun, Amirali

doi:10.1016/j.compositesb.2019.05.053

Cited by 58 publications

(17 citation statements)

References 44 publications

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“…Graphene is receiving considerable attention due to its special phenomena of predictability and measurability, mainly originating from the massless relativistic particle behavior of electrons [33]. The characteristics of graphene materials including intrinsic Young's modulus (1.0 TPa) and tensile strength (130 GPa) [34,35], excellent carrier mobility at room temperature under ambient air conditions (2.0 × 10 5 cm 2 •V −1 •s −1 ) [36], and high thermal (5000 Wm −1 •K −1 ) and electrical conductivity (1.0 × 10 6 S•m −1 ) [15,37] make this material applicable as an active material in a variety of interdisciplinary fields such as supercapacitors [38,39], sensors [40,41], energy storage devices [42,43], and multifunctional fillers in nanocomposite materials [44].…”

Section: Properties Of Graphene-based Materialsmentioning

confidence: 99%

Memristive Non-Volatile Memory Based on Graphene Materials

Shen

Zhao

et al. 2020

Micromachines

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Resistive random access memory (RRAM), which is considered as one of the most promising next-generation non-volatile memory (NVM) devices and a representative of memristor technologies, demonstrated great potential in acting as an artificial synapse in the industry of neuromorphic systems and artificial intelligence (AI), due its advantages such as fast operation speed, low power consumption, and high device density. Graphene and related materials (GRMs), especially graphene oxide (GO), acting as active materials for RRAM devices, are considered as a promising alternative to other materials including metal oxides and perovskite materials. Herein, an overview of GRM-based RRAM devices is provided, with discussion about the properties of GRMs, main operation mechanisms for resistive switching (RS) behavior, figure of merit (FoM) summary, and prospect extension of GRM-based RRAM devices. With excellent physical and chemical advantages like intrinsic Young’s modulus (1.0 TPa), good tensile strength (130 GPa), excellent carrier mobility (2.0 × 105 cm2∙V−1∙s−1), and high thermal (5000 Wm−1∙K−1) and superior electrical conductivity (1.0 × 106 S∙m−1), GRMs can act as electrodes and resistive switching media in RRAM devices. In addition, the GRM-based interface between electrode and dielectric can have an effect on atomic diffusion limitation in dielectric and surface effect suppression. Immense amounts of concrete research indicate that GRMs might play a significant role in promoting the large-scale commercialization possibility of RRAM devices.

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Section: Properties Of Graphene-based Materialsmentioning

confidence: 99%

Memristive Non-Volatile Memory Based on Graphene Materials

Shen

Zhao

et al. 2020

Micromachines

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“…Young’s Modulus is defined as the ratio of stress and strain of the material. Stiffness of the material is a measure of elastic modulus [ 85 , 86 ]. Young’s Modulus(E) = Tensile Stress(σ)/Tensile Strain(ε) …”

Section: Mechanical Propertiesmentioning

confidence: 99%

Thiolation of Biopolymers for Developing Drug Delivery Systems with Enhanced Mechanical and Mucoadhesive Properties: A Review

et al. 2020

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Biopolymers are extensively used for developing drug delivery systems as they are easily available, economical, readily modified, nontoxic, biodegradable and biocompatible. Thiolation is a well reported approach for enhancing mucoadhesive and mechanical properties of polymers. In the present review article, for the modification of biopolymers different thiolation methods and evaluation/characterization techniques have been discussed in detail. Reported literature on thiolated biopolymers with enhanced mechanical and mucoadhesive properties has been presented conspicuously in text as well as in tabular form. Patents filed by researchers on thiolated polymers have also been presented. In conclusion, thiolation is an easily reproducible and efficient method for customization of mucoadhesive and mechanical properties of biopolymers for drug delivery applications.

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“…Elastic properties of nanocomposites with different graphene content were simulated with different multiscale modeling approaches such as MD-HT, 8 MD-FEM, 18,19 MD-M, 20,21 and MM-MT. 22 There are also concurrent-hierarchical multi-scale approaches 23 in which the graphene dispersion effect is included in the modeling, as well as multiscale stochastic approaches 24 that address the effect of size, volume ratio, and graphene orientation on the elastic characteristics of nanocomposites. In addition, the stiffness of nanocomposite materials containing randomly dispersed graphene was investigated by combining two material models at the same scale, such as FE-M. 25 Furthermore, different modeling techniques were used to model compressive strength 26 at different strain rates, impact strength, 27 and static-dynamic mechanical behavior 28 of GP nanocomposites with content different graphene by weight.…”

Section: Introductionmentioning

confidence: 99%

Simplified cooperative viscoplasticity theory based on overstress model for nanocomposites

Bakbak

Çolak

2022

J of Applied Polymer Sci

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Cooperative‐viscoplasticity theory based on overstress (C‐VBO) theory proposed to cover the total time, temperature, and graphene fraction dependent mechanical behavior of the nanocomposite material is simplified in this work. The previous version of the VBO model for nanocomposites includes the agglomeration effect of nanofiller by dividing the polymer nanocomposite into two parts as “agglomerated” and “effective matrix” phases. In this work, to be able to define the behavior of nanocomposites when nanofiller is evenly distributed into the matrix, the material model is simplified by defining the bulk and shear modulus without agglomerated and effective matrix phases and they are calculated separately using the Mori‐Tanaka homogenization scheme. The capabilities of the modified C‐VBO model for nanocomposites are investigated by simulating rate‐dependent uniaxial compression, creep, and relaxation behaviors for different graphene fractions (0.1 and 0.5 wt.%). In addition, to investigate high strain rate behavior, Split Hopkinson pressure bar tests are performed. The adiabatic heating effect that occurs at high strain rates is taken into account and incorporated into the model. All behaviors mentioned above are simulated well by simplified C‐VBO for nanocomposites. The developed constitutive model is ready to implement into finite element codes to be used in the structural analysis.

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Estimating Young's modulus of graphene/polymer composites using stochastic multi-scale modeling

Cited by 58 publications

References 44 publications

Memristive Non-Volatile Memory Based on Graphene Materials

Memristive Non-Volatile Memory Based on Graphene Materials

Thiolation of Biopolymers for Developing Drug Delivery Systems with Enhanced Mechanical and Mucoadhesive Properties: A Review

Simplified cooperative viscoplasticity theory based on overstress model for nanocomposites

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