Surface characterization of graphene based materials
Marcin Pisarek , Marcin Hołdyński , Mirosław Krawczyk , Robert Nowakowski , Agata Roguska , Artur Małolepszy , Leszek Stobiński , Aleksander Jabłoński
AbstractIn the present study, two kind of samples were used: (i) a monolayer graphene film with a thickness of 0.345 nm deposited by the CVD method on Cu foil, (ii) graphene flakes obtained by modified Hummers method and followed by reduction of graphene oxide. The inelastic mean free path (IMFP), characterizing electron transport in graphene/Cu sample and reduced graphene oxide material, which determines the sampling depth of XPS and AES were evaluated from relative Elastic Peak Electron Spectroscopy (EPES) measurements with the Au standard in the energy range 0.5–2 keV. The measured IMFPs were compared with IMFPs resulting from experimental optical data published in the literature for the graphite sample. The EPES IMFP values at 0.5 and 1.5 keV was practically identical to that calculated from optical data for graphite (less than 4% deviation). For energies 1 and 2 keV, the EPES IMFPs for rGO were deviated up to 14% from IMFPs calculated using the optical data by Tanuma et al. . Before EPES measurements all samples were characterized by various techniques like: FE-SEM, AFM, XPS, AES and REELS to visualize the surface morphology/topography and identify the chemical composition.
|Journal series||Applied Surface Science, ISSN 0169-4332|
|Vol||388, Part B|
|Publication size in sheets||0.5|
|Conference||12th International Symposium on Electrochemical/ Chemical Reactivity of New Materials - „Surface Science - key to understand advanced materials” (ECRNM 12), 15-09-2015 - 18-09-2015, Warszawa, Polska|
|Keywords in English||graphene reduced graphene oxide (rGO), elastic-peak electron spectroscopy (EPES), electron inelastic mean free path (IMFP), surface analysis (AES, REELS, XPS)|
|Score|| = 35.0, 28-11-2017, ArticleFromJournalAndMatConf|
= 35.0, 28-11-2017, ArticleFromJournalAndMatConf
|Publication indicators||: 2016 = 3.387 (2) - 2016=3.184 (5)|
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