Micro raman spectroscope

SEM was carried out to observe the morphology of graphene/Cu foil and NPC/graphene as well as the thickness of the NPC film. An FEI Teneo scanning electron microscope with an operating voltage of 0.8 to 2.0 kV and a working distance of 2.5 to 7.0 mm was used. The samples were directly characterized without any conductive coating. Transmission electron microscopy (TEM) imaging of the NPC film and ED of the NPC/graphene film were conducted using an FEI Tecnai G2 Spirit microscope operating with a 120-keV incident electron beam.
Raman characterization was performed on graphene/Cu using a Renishaw micro-Raman spectroscope (457 nm, 2.33 eV, 50× objective). More than 10 spectra were collected with the mapping method for each sample. The Raman data were analyzed by curve fitting in MATLAB to extract the I_D/I_G, I_D/I_D′, and I_2D/I_G ratios. Before analysis, the background was subtracted from the Raman spectra.

Raman characterization was carried on graphene transferred onto the SiO₂/Si wafer by the wet-transfer method. The N-SLG/Cu was spin-coated with a thin poly(methyl methacrylate) (PMMA) layer (950 PMMA A4, 4% in anisole; MicroChem Corp.). After the coating, the PMMA/N-SLG/Cu was annealed at 60°C for 30 min to remove the solvent. Then, the PMMA/N-SLG/Cu was floated on a 20% Na₂S₂O₈ solution to remove the Cu foil. Following this, the floating film was transferred to deionized water to rinse the residual etchant and was scooped by SiO₂/Si wafer. To increase the bonding of PMMA/N-SLG, the sample was annealed at 150°C and then 190°C for 10 min. Last, PMMA was removed by acetone leaving N-SLG on the SiO₂/Si wafer for Raman characterization.
Single-point data collection and mapping were performed using Renishaw micro-Raman spectroscope equipped with a blue laser (λ_L = 457 nm, E_L = 2.71 eV) and a green laser (λ_L = 532 nm, E_L = 2.33 eV). Analysis of the Raman data was carried out using MATLAB. For calculation of the D and the G peak height, the background was subtracted from the Raman data using the least-squares curve fitting tool (lsqnonlin).