journal article·2017-09

Charge injection in large area multilayer graphene by ambient Kelvin probe force microscopy

Igor Bdikin, Dhanajay K. Sharma, Gonzalo Otero-Irurueta, María J. Hortigüela, Pawan K. Tyagi, Victor Neto, Manoj K. Singh

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Abstract

Abstract This study demonstrated an in situ method for quantitative characterization of nanoscale electrostatic properties of as-grown multilayer-graphene (MLG) sheets on nickel by a combination of atomic force microscopy (AFM) and Kelvin probe force microscopy (KPFM). Large area epitaxial MLG sheet were grown on nickel by Hot Filament-Thermal chemical vapor deposition (CVD) technique. The high crystalline MLG sheets on nickel were confirmed by Raman spectroscopy that revealed average G-bandwidths in the range of ∼20 cm −1 . Herein, for the first time, the charge injection as well as subsequent charge diffusion over time on the MLG/nickel surface was demonstrated. The results unveiled that: (i) MLG surface can be either positively or negatively charged through injection process using Pt coated Si-based AFM probes; (ii) the charges accumulated and eventually reached to saturated concentrations of +4.45 (±0.1) μC/m 2 and −1.3 (±0.1) μC/m 2 , respectively; (iii) the charge diffusion coefficients on graphene surface were measured to be 1.50 (±0.05) × 10 −16 m 2 /s and 0.64 (±0.05) × 10 −16 m 2 /s for the positive and the negative charges, respectively. The discovery of charge injection in MLG may pave the way for designing a new class of energy harvesting devices. Additionally, our study demonstrated a technique for nano-patterning/charge lithography of surface charges by contact electrification, which could be a promising application to create charged nanostructures for next generation graphene based nanoelectronic devices.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Graphene research and applications · Molecular Junctions and Nanostructures · Nanowire Synthesis and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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