Polarization in graphene nanoribbons with inherent defects using first-principles calculations

Nevhal, S. K.; Kundalwal, S. I.

doi:10.1007/s00707-021-03136-9

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…Several studies have demonstrated the exceptional thermo‐mechanical properties of polymer composites, particularly those incorporating CNTs 49–54 . Structures such as plates and shells made from these composites find wide applications in noise reduction and vibration damping 55–57 . The investigation of such composite structures has led to the development of materials with lightweight characteristics, high stiffness, and remarkable strength.…”

Section: Introductionmentioning

confidence: 99%

“…[49][50][51][52][53][54] Structures such as plates and shells made from these composites find wide applications in noise reduction and vibration damping. [55][56][57] The investigation of such composite structures has led to the development of materials with lightweight characteristics, high stiffness, and remarkable strength. The addition of CNTs as a reinforcement in polymer matrices enhances the overall properties of the composites due to the excellent thermo-mechanical properties of CNTs.…”

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confidence: 99%

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Conductive polymer nanocomposite incorporated with carbon nanotubes for effective electromagnetic interference shielding: A numerical study

Dubey,

Gupta,

Kundalwal

2023

Polymer Composites

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This study presents a comprehensive numerical investigation of the electromagnetic interference (EMI) shielding capabilities of a conductive polymer nanocomposite incorporated with carbon nanotubes (CPNC) in the X‐band. The investigation is conducted using a commercial finite element simulation package, Ansys HFSS. The study focuses on analyzing the EMI shielding effectiveness (SE) of CPNC by varying the weight percent (wt%) of carbon nanotubes (CNTs), thickness, and shape of the shielding material. Our outcomes show that the EMI SE of CPNC increases with the wt% of CNTs. Notably, a maximum EMI SE is observed around ~90 dB due to absorption (SEA) at a 4 mm pallet‐shaped CPNC and ~180 dB for a hollow cylindrical CPNC (22.86 mm outer diameter and 3 mm thickness) with 2 wt% of CNT. The EMI SE due to absorption (SEA) at a pallet thickness of 4 mm exhibits a significant improvement of ~107% compared to the SEA observed at a thickness of 1 mm. These findings highlight the exceptional absorption capabilities and lightweight nature of CPNC, rendering it a promising shielding material for aerospace and satellite communication applications.Highlights EMI SE of CPNC increases with the wt% of CNT in the polypyrrole (PPy) matrix. The percolation threshold is observed at a low 2 wt% concentration of CNT. Thickness of the shielding material influences the EMI SE of CPNC. CPNC provides absorption‐dominated shielding.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Conductive polymer nanocomposite incorporated with carbon nanotubes for effective electromagnetic interference shielding: A numerical study

Dubey,

Gupta,

Kundalwal

2023

Polymer Composites

View full text Add to dashboard Cite

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“…On the mechanical characteristics of graphene, they considered the effects of several modulators, such as system temperature, strain rate, single vacancy defect, and chiral orientation. In a recent study, S K Nevhal [27] and colleagues conducted an investigation using first-principles calculations to explore induced piezoelectricity in armchair graphene nanoribbons (AGNRs). This research encompassed AGNRs with non-centrosymmetric pores and various defects, including divacancy, line, and Stone-Wales (SW) defects.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach on the prediction of mechanical characteristics of pristine, boron doped and nitrogen doped graphene

Sharma,

Ajori

2023

Phys. Scr.

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Machine Learning (ML), a subset of Artificial Intelligence has been widely applied in various domains, but it has only just begun to be employed in the field of engineering. In the present investigation, various ML algorithms and artificial neural network (ANN) structures are used for the first time to predict the mechanical properties of pristine, boron-doped, and nitrogen-doped graphene while also taking into account the effects of various types of vacancy defects.Fracture strain, Ultimate Tensile Strength (UTS), and Young's modulus are all predicted. ML technique reduces the computational cost and time required to find out mechanical properties of these materials. The training dataset for the ML models is developed using Molecular Dynamics (MD) simulations. It was shown that defects and doping both had an adverse effect on mechanical characteristics. While ANN, LASSO, and LASSO Lars have all performed quite well at predicting these features, pipeline polynomial regression has performed best across all datasets. New insights on the research of mechanical characteristics utilizing cutting-edge computational techniques are provided by the discoveries in this research.

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Polarization in the van der Waals–bonded graphene/hBN heterostructures with triangular pores

2023

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Polarization in graphene nanoribbons with inherent defects using first-principles calculations

Cited by 8 publications

References 40 publications

Conductive polymer nanocomposite incorporated with carbon nanotubes for effective electromagnetic interference shielding: A numerical study

Conductive polymer nanocomposite incorporated with carbon nanotubes for effective electromagnetic interference shielding: A numerical study

Machine learning approach on the prediction of mechanical characteristics of pristine, boron doped and nitrogen doped graphene

Polarization in the van der Waals–bonded graphene/hBN heterostructures with triangular pores

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