Free vibration of size and temperature‐dependent carbon nanotube (CNT)‐reinforced composite nanoplates with CNT agglomeration

Daghigh, Hamid; Daghigh, Vahid

doi:10.1002/pc.25057

Cited by 34 publications

(18 citation statements)

References 91 publications

(87 reference statements)

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“…With this study, it was concluded that the presence of the foundation beneath the nanoplates reduced the effects of CNT agglomeration. The same author led another study on the dynamic behaviour of size-and temperature-dependent agglomerated CNTRC nanoplates resting on a visco-Pasternak foundation [19], where it was concluded that ignoring the agglomeration effect causes an overestimation of the elastic properties and the presence of the Pasternak foundation clearly increased the nanoplate natural frequency.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Effect of Carbon Nanotubes’ Distribution and Agglomeration in the Free Vibration of Nanocomposite Plates

Craveiro¹,

Loja

2020

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The present work aimed to characterize the free vibrations’ behaviour of nanocomposite plates obtained by incorporating graded distributions of carbon nanotubes (CNTs) in a polymeric matrix, considering the carbon nanotubes’ agglomeration effect. This effect is known to degrade material properties, therefore being important to predict the consequences it may bring to structures’ mechanical performance. To this purpose, the elastic properties’ estimation is performed according to the two-parameter agglomeration model based on the Eshelby–Mori–Tanaka approach for randomly dispersed nano-inclusions. This approach is implemented in association with the finite element method to determine the natural frequencies and corresponding mode shapes. Three main agglomeration cases were considered, namely, agglomeration absence, complete agglomeration, and partial agglomeration. The results show that the agglomeration effect has a negative impact on the natural frequencies of the plates, regardless the CNTs’ distribution considered. For the corresponding vibrations’ mode shapes, the agglomeration effect was shown in most cases not to have a significant impact, except for two of the cases studied: for a square plate and a rectangular plate with symmetrical and unsymmetrical CNTs’ distribution, respectively. Globally, the results confirm that not accounting for the nanotubes’ agglomeration effect may lead to less accurate elastic properties and less structures’ performance predictions.

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Section: Introductionmentioning

confidence: 99%

A Study on the Effect of Carbon Nanotubes’ Distribution and Agglomeration in the Free Vibration of Nanocomposite Plates

Craveiro¹,

Loja

2020

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“…Electrical conductivity in polymer nanocomposites containing conductive nanofiller such as carbon nanotubes (CNT) provides some applications in electronics, shielding, actuating, and sensing . The conductivity of nanocomposites meaningfully improves at a critical concentration as percolation threshold, because the number of nanoparticles at this point is enough to establish the conductive networks .…”

Section: Introductionmentioning

confidence: 99%

Significances of interphase conductivity and tunneling resistance on the conductivity of carbon nanotubes nanocomposites

Zare

Rhee

2019

Polymer Composites

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This article expresses a simple model for prediction of conductivity in polymer carbon nanotubes (CNT) nanocomposites (PCNT). This model suggests the roles of CNT concentration, CNT dimensions, CNT conductivity, the percentage of networked CNT, interphase thickness and tunneling properties in the conductivity of PCNT. The suggested model is applied to predict the conductivity in several samples. In addition, the significances of all parameters attributed to CNT, interphase and tunneling regions on the predicted conductivity are justified to confirm the suggested model. The calculations of conductivity properly agree with the experimental results demonstrating the capability of suggested model for prediction of conductivity. Thick interphase increases the conductivity of nanocomposites, because it enlarges the conductive networks. In addition, high tunneling resistivity due to polymer layer, large tunneling distance between adjacent CNT and small tunneling diameter deteriorate the conductivity, because they enhance the tunneling resistance limiting the charge transferring via tunneling regions. The suggested model can replace the available models to predict the conductivity in future researches. K E Y W O R D Scarbon nanotubes (CNT), conductivity, interphase, polymer nanocomposites, tunneling effect

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“…30 Micro-and nano-powder fillers are mostly used to enhance hardness, elastic moduli, impact, wear, and thermal resistance, while reducing cost and mechanical shrinkage. 14 For example, micron-sized rigid glassy spheres, elastic rubber particles, rigid nano-sized CaCO 3 , 31 Al 2 O 3 , 32 SiO 2 , 33 and TiO 2 34 powders, carbon nano-tubes, [35][36][37] and nano-clay (NC) platelets 38 have been used as reinforcements.…”

Section: Introductionmentioning

confidence: 99%

Machine learning predictions on fracture toughness of multiscale bio-nano-composites

Daghigh

Lacy

Daghigh

et al. 2020

Journal of Reinforced Plastics and Composites

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Tailorability is an important advantage of composites. Incorporating new bio-reinforcements into composites can contribute to using agricultural wastes and creating tougher and more reliable materials. Nevertheless, the huge number of possible natural material combinations works against finding optimal composite designs. Here, machine learning was employed to effectively predict fracture toughness properties of multiscale bio-nano-composites. Charpy impact tests were conducted on composites with various combinations of two new bio fillers, pistachio shell powders, and fractal date seed particles, as well as nano-clays and short latania fibers, all which reinforce a poly(propylene)/ethylene–propylene–diene-monomer matrix. The measured energy absorptions obtained were used to calculate strain energy release rates as a fracture toughness parameter using linear elastic fracture mechanics and finite element analysis approaches. Despite the limited number of training data obtained from these impact tests and finite element analysis, the machine learning results were accurate for prediction and optimal design. This study applied the decision tree regressor and adaptive boosting regressor machine learning methods in contrast to the K-nearest neighbor regressor machine learning approach used in our previous study for heat deflection temperature predictions. Scanning electron microscopy, optical microscopy, and transmission electron microscopy were used to study the nano-clay dispersion and impact fracture morphology.

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Free vibration of size and temperature‐dependent carbon nanotube (CNT)‐reinforced composite nanoplates with CNT agglomeration

Cited by 34 publications

References 91 publications

A Study on the Effect of Carbon Nanotubes’ Distribution and Agglomeration in the Free Vibration of Nanocomposite Plates

A Study on the Effect of Carbon Nanotubes’ Distribution and Agglomeration in the Free Vibration of Nanocomposite Plates

Significances of interphase conductivity and tunneling resistance on the conductivity of carbon nanotubes nanocomposites

Machine learning predictions on fracture toughness of multiscale bio-nano-composites

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