Graphene Twistronics: Tuning the Absorption Spectrum and Achieving Metamaterial Properties

被引:9
作者
Armghan, Ammar [1 ]
Alsharari, Meshari [1 ]
Aliqab, Khaled [1 ]
Alsalman, Osamah [2 ]
Parmar, Juveriya [3 ,4 ]
Patel, Shobhit K. [4 ]
机构
[1] Jouf Univ, Coll Engn, Dept Elect Engn, Sakaka 72388, Saudi Arabia
[2] King Saud Univ, Coll Engn, Dept Elect Engn, POB 800, Riyadh 11421, Saudi Arabia
[3] Univ Nebraska Lincoln, Dept Mech & Mat Engn, 1400 R St, Lincoln, NE 68588 USA
[4] Marwadi Univ, Dept Comp Engn, Rajkot 360003, India
关键词
twistronics; photonics; graphene; metamaterial; absorption; tuning; MATHEMATICAL-ANALYSIS; RESONANCES;
D O I
10.3390/math11071579
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Graphene twistronics using multilayer graphene is presented in such a way that it provides a metamaterial effect. This manuscript also analyzes the prediction of behavior using machine learning. The metamaterial effect is achieved by twisting the graphene layers. Graphene twistronics is a new concept for changing the electrical and optical properties of bilayer graphene by applying a small angle twist between the layers. The angle twists of 5(?)degrees, 10(?)degrees, and 15 degrees are analyzed for the proposed graphene twistronics design. Tuning in the absorption spectrum is achieved by applying small twists to the angles of the bilayer graphene. Results in the form of absorption, conductivity, permeability, permittivity, and impedance are presented for different twist angles. The twisted graphene layers also demonstrate negative permittivity and negative permeability, similar to metamaterials. These negative refraction properties of graphene twistronics provide flexibility and transparency, which can be applied in photovoltaic applications. Machine-learning-based regression models are used to reduce the simulation time and resources. The results show that a regression model can reliably estimate intermediate wavelength absorption values with an R-2 of 0.9999.
引用
收藏
页数:17
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