Advanced multifunctional graphene aerogel – Poly (methyl methacrylate) composites: Experiments and modeling

Fan, Zeng; Gong, Feng; Nguyen, Son Truong; Duong, Hai M.

doi:10.1016/j.carbon.2014.09.072

Cited by 130 publications

(70 citation statements)

References 82 publications

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“…The theoretical calculations predict thermal conductivity values of polymer nanocomposites far greater than the experimental values [156]. It can be explained on the basis of Thermal Boundary Resistance (TBR, also known as Kapitza resistance) between graphene and polymer chains [157].…”

Section: Thermal Propertiesmentioning

confidence: 92%

“…In Brownian motion, the thermal walkers exhibit random changes in motion in each time step and can be modeled using random algorithm [159]- [162]. In each space direction, these position changes take values from a normal distribution with a zero mean and a standard deviation depending on the thermal diffusivity (D m ) and the time increment (Δt) as given in Equation (70) [156].…”

Section: Thermal Propertiesmentioning

confidence: 99%

“…A comparative infrared microscopy technique can be used to measure thermal properties of nanocomposites [156]. The thermal properties can be approximated by Fourier's law [170] [171].…”

Section: Thermal Propertiesmentioning

confidence: 99%

“…The thermal properties can be approximated by Fourier's law [170] [171]. The variation in thermal conductivity of graphene-PMMA nanocomposites with varying graphene content is shown in Figure 27 [156]. The thermal conductivity increases up to 3.5 times of pure PMMA at 2.5 vol% of graphene.…”

Section: Thermal Propertiesmentioning

confidence: 99%

“…When graphene is oriented along the heat flux, higher K eff values are obtained due to efficient heat transfer channel provided by graphene. However, when graphene is oriented Inset is the set-up scheme for comparative infrared microscopy technique, where the amorphous quartz is used as reference material [156].…”

Section: Thermal Propertiesmentioning

confidence: 99%

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Modeling and Simulation of Graphene Based Polymer Nanocomposites: Advances in the Last Decade

Atif¹,

Inam²

2016

Graphene

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Modeling and simulation allow methodical variation of material properties beyond the capacity of experimental methods. The polymers are one of the most commonly used matrices of choice for composites and have found applications in numerous fields. The stiff and fragile structure of monolithic polymers leads to the innate cracks to cause fracture and therefore the engineering applications of monolithic polymers, requiring robust damage tolerance and high fracture toughness, are not ubiquitous. In addition, when "many-parts" cling together to form polymers, a labyrinth of molecules results, which does not offer to electrons and phonons a smooth and continuous passageway. Therefore, the monolithic polymers are bad conductors of heat and electricity. However, it is well established that when polymers are embedded with suitable entities especially nano-fillers, such as metallic oxides, clays, carbon nanotubes, and other carbonaceous materials, their performance is propitiously improved. Among various additives, graphene has recently been employed as nano-filler to enhance mechanical, thermal, electrical, and functional properties of polymers. In this review, advances in the modeling and simulation of grapheme based polymer nanocomposites will be discussed in terms of graphene structure, topographical features, interfacial interactions, dispersion state, aspect ratio, weight fraction, and trade-off between variables and overall performance.

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Section: Thermal Propertiesmentioning

confidence: 92%

Section: Thermal Propertiesmentioning

confidence: 99%

“…A comparative infrared microscopy technique can be used to measure thermal properties of nanocomposites [156]. The thermal properties can be approximated by Fourier's law [170] [171].…”

Section: Thermal Propertiesmentioning

confidence: 99%

Section: Thermal Propertiesmentioning

confidence: 99%

Section: Thermal Propertiesmentioning

confidence: 99%

See 3 more Smart Citations

Modeling and Simulation of Graphene Based Polymer Nanocomposites: Advances in the Last Decade

Atif¹,

Inam²

2016

Graphene

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Multiscale Modeling of Graphene‐polymer Nanocomposites with Tunneling Effect

Lu¹,

Yvonnet²,

Detrez³

et al. 2022

Nanocomposites

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Porous Nanofiber Membrane: Rational Platform for Highly Sensitive Thermochromic Sensor

Kim

Bae

Lee

et al. 2022

Adv Funct Materials

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Thermochromic sensors provide an intuitive and real‐time solution for monitoring the local temperature with naked eyes. Conventional thermochromic sensors often utilize either solution‐type or dense film‐type platforms, which are suboptimal morphologies for exposing a large number of dye molecules to the surface, leading to low sensitivity and sluggish responding speeds. Herein, this article introduces rational synthetic routes to fabricate highly sensitive nanofiber (NF) sensor membranes loaded with thermochromic dyes (C3H6N6·CH2O)x‐loaded nanofibers (NFs) sensor membranes by alignment‐controllable electrospinning techniques (x–y perpendicular and rotary). The NF‐based porous sensor membranes exhibit two‐ to fivefold improved thermochromic sensitivity (ΔRGB) compared to those of dense film‐type sensors at 31.6–42.7 °C. This is attributed to the uniform distribution of dyes throughout the porous NF structure (≈95.7%), which exhibits excellent light transmittance that is 10–30‐fold higher than that of film‐type sensors. Based on the available shape‐conforming synthetic strategies, this article further demonstrates wearable thermochromic sensors in the forms of mask‐, patch‐, and bracelet‐type devices, which can accurately monitor body temperature in real time.

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Advanced multifunctional graphene aerogel – Poly (methyl methacrylate) composites: Experiments and modeling

Cited by 130 publications

References 82 publications

Modeling and Simulation of Graphene Based Polymer Nanocomposites: Advances in the Last Decade

Modeling and Simulation of Graphene Based Polymer Nanocomposites: Advances in the Last Decade

Multiscale Modeling of Graphene‐polymer Nanocomposites with Tunneling Effect

Porous Nanofiber Membrane: Rational Platform for Highly Sensitive Thermochromic Sensor

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