Modeling Plasmonics and Electronics in Semiconducting Graphene Nanostrips

Abstract

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 Full Text: PDF

Dataset for Electronics and Plasmonics in Graphene, Silicene, and Germanene Nanostrips

“…In Equation ( 8), e, ε, q, θ, and N 2D represent the electron charge, dielectric constant, wave vector, excitation angle, and 2D charge density, respectively. Specifically, the 2D charge density can be employed to customize Equation ( 8) and analyze the ribbons as either isolated systems or periodic arrays using the following expression [13,25,31,32]:…”

“…We point out this in our GW calculations for graphene. We chose LDA for its computational efficiency and proven accuracy in this specific context [13,32]. While generalized gradient approximation (GGA) or hybrid functionals might offer certain advantages in modeling complex exchange-correlation effects, the LDA-GW approach is less computationally demanding, allowing for more extensive sampling or larger systems within the same computational budget.…”

“…The datasets accompanying this study as supplementary files provide the data compiled during the computational studies in [13,27,31,32]. The primary file is separated into subfolders that are denoted in Scheme 1.…”

“…We point out that while the semi-analytical model effectively derives the properties of nanostrips, our focus on GW calculations is driven by the need for accurate Fermi velocity estimation. As an example, for graphene [13], the conventionally used Fermi velocity (1.1 × 10 6 m/s [28]) notably varies from experimental and GW-calculated values. These variations are crucial at the THz scale (≤0.1 eV), where our research is focused, as they can significantly impact the predicted electronics and plasmonics.…”

“…Notably, graphene, silicene, and germanene inherently possess zero bandgaps. Nevertheless, they do exhibit a finite bandgap when transitioning into a nanoribbon configuration [13] (i.e., quasi-1D systems), namely graphene nanoribbons (GNRs), silicene nanoribbons (SiNRs), and germanene nanoribbons (GeNRs). Interestingly, the bandgap of these nanoribbons can be readily adjusted through processes such as hydrogenation or altering the ribbon width [14].…”