Tuning the Electronic Properties of 2D Silicon Carbide Nanoribbons for Nanodevices Application

Silicon carbide nanoribbons (SiC NRs) are emerging as essential materials for the future of miniaturized electronic and spintronic devices due to their exceptional structural, electronic, and magnetic properties. This study explores how altering the edges of these nanoribbons by attaching hydrogen or fluorine atoms influences their stability, electrical characteristics, and electron transport behavior in both zigzag and armchair configurations. Utilizing advanced computational simulations based on density functional theory (DFT), we investigated various edge passivation techniques to understand their impact on structural robustness, electronic band structure, spin alignment, and electrical conductivity. Our results indicate that hydrogen passivation significantly enhances the stability of SiC nanoribbons while enabling precise control over their electronic properties. In zigzag nanoribbons, hydrogenation allows for a tunable transition between semiconductor and magnetic states, making them highly suitable for spintronic applications. On the other hand, fluorine passivation results in even greater structural stabilization due to fluorine’s strong electron-withdrawing nature, leading to increased charge transfer and stronger chemical bonding at the edges. Additionally, fluorinated zigzag nanoribbons exhibit half-metallicity, a highly desirable property for efficient spin filtering, which is crucial for spintronic memory and logic devices. Furthermore, both hydrogenated and fluorinated nanoribbons demonstrate negative differential resistance (NDR), a phenomenon that makes them promising candidates for high-speed switching and oscillator applications. Moreover, our study evaluates how external electric fields influence the electronic and magnetic properties of SiC nanoribbons. We found that applying an electric field can modulate the band gap and magnetic behavior, enabling precise tuning of these materials for low-power electronic applications. Computational modeling