First principle study of multilayered graphene/MoS2 heterostructures for photodetectors

Mustafa, Hina; Irfan, Muhammad; Sattar, Abdul; Amjad, Raja J.; Latif, Hamid; Usman, Arslan; Ahmad, Ashfaq; Qin, Shengyong

doi:10.1016/j.mseb.2022.116205

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…Due to its exceptional mechanical and electrical capabilities, graphene is a strong contender for uses in electromechanical devices and flexible electronics [9,11,12]. Despite the fact that band-gap is crucial to regulate the electronic properties, graphene has an electronic structure with no band-gap [13][14][15].…”

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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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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“…The electronic structure of TMDs, which comprises distorted inversion symmetry and time-reversal symmetry, influences these traits. These unique characteristics significantly impact the transport and optical properties of TMDs [10,11]. Due to their enormous excitonic binding energies reaching up to 0.5 eV, TMDs exhibit efficient light emission and possess direct band gaps [12].…”

Section: Introductionmentioning

confidence: 99%

Bandgap engineering and tuning of optoelectronic properties of 2D NbSe₂/MoS₂ heterostructure using first principle computations

Irfan,

Ehsan,

Pang

et al. 2023

Phys. Scr.

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This study performed first-principles calculations based on density functional theory to study the interlayer electronic and optical properties of NbSe2/MoS2 heterostructures. Bandgap in 2H-MoS2 is often quite large typically around 1.8 eV, showing slow response time and low photoresponsivity (R); however, a slight bandgap variation can improve the properties of semiconducting and conducting heterostructures. Different stacking configurations of the interlayer van der Waals interaction were precisely investigated. Due to their unique properties, atomically thin NbSe2/MoS2 based heterostructures hold great potential for future electronic and optoelectronic devices. LDA, GGA, GGA with SOC, and HSE06 are used to study the monolayers of MoS2, NbSe2, and their T and H stacking structures. Our results demonstrate that the metallic NbSe2 effect on the semi-metallic MoS2 reduces the band gap of MoS2 up to 140 meV. Moreover, these heterostructures exhibit outstanding absorption properties from visible to ultraviolet regions, which makes them ideal candidates for optoelectronic applications, particularly in photodetectors.

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“…16,17 Consequently, multilayered photodetectors can detect a wider range of light signals, spanning from ultraviolet (UV) to infrared (IR) wavelengths, making them highly versatile and adaptable to various applications. [18][19][20] Recently, there has been an increasing interest in exploring novel heterostructure designs to enhance the functionality and efficiency of photodiodes. [21][22][23] Among these materials, aluminum plays a pivotal role as a single photodetection element, providing efficient and reliable light detection capabilities [24][25][26][27] One of the key advantages of aluminum-based photodetectors is their high sensitivity.…”

mentioning

confidence: 99%

Illumination-Dependent I-V Characteristics of MgF₂/Al Multilayered Photodetector

Gaballah,

Karmalawi,

ElMoghazy

2024

ECS J. Solid State Sci. Technol.

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The demand for highly-efficient and sensitive photodetectors has driven extensive research in the field of optoelectronics. In this study, a novel photodetector utilizing a multilayered structure based on MgF2/Al/Si is presented. The heterostructure device was fabricated using the electron beam deposition technique, and their structural and electrical properties were thoroughly characterized. The optoelectronic properties were explained based on the thermionic emission theory under different light intensities. Additionally, a comprehensive study was established to explore the diode electronic parameters, such as the ideality factor (n), potential barrier height (Φ0), and series resistance (Rs), using the methods proposed by Cheung and Nord. The photocurrent shows an increase with higher illumination intensity, indicating efficient generation of electron-hole pairs within the device. Additionally, the photo-transient time measured under an irradiance level of approximately 1000 W/m2 exhibits a fast detection of light. Furthermore, the MgF2 photodetector showed remarkable stability, offering long-term reliability for practical applications. Overall, this research offers valuable perspectives on the potential of MgF2 as a competent substance for the development of highly efficient and sensitive photodetectors for various optoelectronic applications.

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First principle study of multilayered graphene/MoS2 heterostructures for photodetectors

Cited by 6 publications

References 30 publications

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

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

Bandgap engineering and tuning of optoelectronic properties of 2D NbSe₂/MoS₂ heterostructure using first principle computations

Illumination-Dependent I-V Characteristics of MgF₂/Al Multilayered Photodetector

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First principle study of multilayered graphene/MoS2 heterostructures for photodetectors

Cited by 6 publications

References 30 publications

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

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

Bandgap engineering and tuning of optoelectronic properties of 2D NbSe2/MoS2 heterostructure using first principle computations

Illumination-Dependent I-V Characteristics of MgF2/Al Multilayered Photodetector

Contact Info

Product

Resources

About

Bandgap engineering and tuning of optoelectronic properties of 2D NbSe₂/MoS₂ heterostructure using first principle computations

Illumination-Dependent I-V Characteristics of MgF₂/Al Multilayered Photodetector