Decoration of graphene with metals and metal-oxides is known to be one of the effective methods to enhance gas sensing and catalytic properties of graphene. We use density functional theory in combination with the nonequilibrium Green’s function formalism to study the conductance response of Fe-doped graphene nanoribbons to CO2 gas adsorption. A single Fe atom is either adsorbed on graphene’s surface (aFe-graphene) or it substitutes the carbon atom (sFe-graphene). Metal atom doping reduces the electronic transmission of pristine graphene due to the localization of electronic states near the impurities. The reduction in the transmission is more pronounced in the case of aFe-graphene. In addition, the aFe-graphene is found to be less sensitive to the CO2 molecule attachment as compared to the sFe-graphene system. Pristine graphene is also found to be less sensitive to the molecular adsorption. Since the change in the conductivity is one of the main outputs of sensors, our findings will be useful in developing graphene-based solid-state gas sensors.
We theoretically investigate the electronic transport and Klein tunneling in AA-stacked bilayer graphene (AA-BLG) encapsulated by dielectric materials. Using the four-band continuum model, we evaluate the transmission and reflection probabilities along with the respective conductances. We find that the interlayer mass-term difference induced by the dielectric materials opens a gap in the energy spectrum and couples the upper and lower Dirac cones in AA-BLG. This cone coupling induces an inter-cone transport that is asymmetric with respect to the normal incidence in the presence of the asymmetric mass-term. The energy spectrum of the gapped AA-BLG exhibits electron-hole asymmetry that is reflected in the associated intra- and inter-cone transport channels. We also find that even though Klein tunneling exists in gated and biased AA-BLG, it is precluded by the interlayer mass-term difference and instead Fabry-Pérot resonances appear.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.