We present a Raman study of Ar(+)-bombarded graphene samples with increasing ion doses. This allows us to have a controlled, increasing, amount of defects. We find that the ratio between the D and G peak intensities, for a given defect density, strongly depends on the laser excitation energy. We quantify this effect and present a simple equation for the determination of the point defect density in graphene via Raman spectroscopy for any visible excitation energy. We note that, for all excitations, the D to G intensity ratio reaches a maximum for an interdefect distance ∼3 nm. Thus, a given ratio could correspond to two different defect densities, above or below the maximum. The analysis of the G peak width and its dispersion with excitation energy solves this ambiguity.

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We report on the micro-Raman spectroscopy of monolayer, bilayer, trilayer, and many layers of graphene ͑graphite͒ bombarded by low-energy argon ions with different doses. The evolution of peak frequencies, intensities, linewidths, and areas of the main Raman bands of graphene is analyzed as function of the distance between defects and number of layers. We describe the disorder-induced frequency shifts and the increase in the linewidth of the Raman bands by means of a spatial-correlation model. Also, the evolution of the relative areas A

When two identical two-dimensional periodic structures are superposed, a mismatch rotation angle between the structures generates a superlattice. This effect is commonly observed in graphite, where the rotation between graphene layers generates Moiré patterns in scanning tunneling microscopy images. Here, a study of intravalley and intervalley double-resonance Raman processes mediated by static potentials in rotationally stacked bilayer graphene is presented. The peak properties depend on the mismatch rotation angle and can be used as an optical signature for superlattices in bilayer graphene. An atomic force microscopy system is used to produce and identify specific rotationally stacked bilayer graphenes that demonstrate the validity of our model.