RESUMO
N-doped graphene displays many interesting properties compared with pristine graphene, which makes it a potential candidate in many applications. Here, we report that the Shubnikov-de Haas (SdH) oscillation effect in graphene can be enhanced by N-doping. We show that the amplitude of the SdH oscillation increases with N-doping and reaches around 5k Ω under a field of 14 T at 10 K for highly N-doped graphene, which is over 1 order of magnitude larger than the value found for pristine graphene devices with the same geometry. Moreover, in contrast to the well-established standard Lifshitz-Kosevich theory, the amplitude of the SdH oscillation decreases linearly with increasing temperature and persists up to a temperature of 150 K. Our results also show that the magnetoresistance (MR) in N-doped graphene increases with increasing temperature. Our results may be useful for the application of N-doped graphene in magnetic devices.
RESUMO
Recently, significant attention has been paid to the resistance switching (RS) behaviour in Fe3O4 and it was explained through the analogy of the electrically driven metal-insulator transition based on the quantum tunneling theory. Here, we propose a method to experimentally support this explanation and provide a way to tune the critical switching parameter by introducing self-aligned localized impurities through the growth of Fe3O4 thin films on stepped SrTiO3 substrates. Anisotropic behavior in the RS was observed, where a lower switching voltage in the range of 10(4) V cm(-1) is required to switch Fe3O4 from a high conducting state to a low conducting state when the electrical field is applied along the steps. The anisotropic RS behavior is attributed to a high density array of anti-phase boundaries (APBs) formed at the step edges and thus are aligned along the same direction in the film which act as a train of hotspot forming conduits for resonant tunneling. Our experimental studies open an interesting window to tune the electrical-field-driven metal-insulator transition in strongly correlated systems.