Modeling Plasmonics and Electronics in Semiconducting Graphene Nanostrips

Talia Tene, Marco Guevara, Gabriel Moreano, Edisson Calderón, Nataly Bonilla García, Cristian Vacacela Gomez, Stefano Bellucci


In recent decades, both academia and industry have shown noteworthy interest in investigating the semiconducting properties of graphene. Nevertheless, the lack of a suitable bandgap in graphene has restricted its practical applications in the current semiconductor industry. To overcome this limitation, graphene micro/nano-strips have been actively explored. The focus of the present study centers on modeling the electronic and plasmonic characteristics of graphene strips with varying widths: 2.7, 100, 135 nm, and 4 m. This analysis is conducted at ultralow energies (0.3 eV, or ~73 THz). We employ conventional density functional computations to estimate the Fermi velocity of graphene, refining the results via the GW approximation. Utilizing the accurate Fermi velocity, we employ a semi-analytical model to explore the ground state and plasmon properties (frequency and dispersion) of these graphene strips. Notably, this approach effectively replicates the density of states observed in narrow experimental graphene nano-strips (2.7 nm) grown on Ge(001) and, similarly, reproduces the plasmon spectrum found in synthesized graphene microstrips (4 μm) on Si/SiO2. Interestingly, our study also offers insights into the potential application of this approach in comprehending the plasmon frequency and plasmon dispersion of graphene nano-strips (~135 nm) acquired through liquid-phase exfoliation. The outcomes of this investigation present compelling evidence that the properties of graphene-based strips can be customized to fulfill specific requirements and applications. These findings hold significant promise for advancing graphene-based technologies, bridging the gap between fundamental research and tangible applications.


Doi: 10.28991/ESJ-2023-07-05-01

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Graphene Micro/Nano-Strips; THz Plasmons; Electronics; Fermi Velocity; Semi-Analytical Model.


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DOI: 10.28991/ESJ-2023-07-05-01


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Copyright (c) 2023 Cristian Vacacela Gomez, Cristian Vacacela Gomez, Talia Tene, Marco Guevara, Gabriel Moreano, Edisson Calderón, Nataly Bonilla García, Stefano Bellucci