NLL-assisted Multilayer Graphene Patterning
Kovalska, I; Pavlov, I; Deminskyi, P; et al.Baldycheva, A; Ilday, FÖ; Kocabas, C
Date: 6 February 2018
Journal
ACS Omega
Publisher
American Chemical Society
Publisher DOI
Abstract
The range of application of diverse graphene-based devices could be limited by insufficient surface
reactivity, unsatisfied shaping, or null energy gap of graphene. Engineering the graphene structure by laser
techniques can adjust transport properties and surface area of graphene, providing devices of different nature
with a higher ...
The range of application of diverse graphene-based devices could be limited by insufficient surface
reactivity, unsatisfied shaping, or null energy gap of graphene. Engineering the graphene structure by laser
techniques can adjust transport properties and surface area of graphene, providing devices of different nature
with a higher capacitance. Additionally, the created periodic potential and appearance of the active
external/inner/edge surface centers determine the multifunctionality of the graphene surface and
corresponding devices. Here, we report on the first implementation of nonlinear laser lithography (NLL) for
multilayer graphene (MLG) structuring, which offers a low-cost, single-step, and high-speed
nanofabrication process. The NLL relies on the employment of a high repetition rate femtosecond Yb fiber
laser that provides generation of highly reproducible, robust, uniform and periodic nanostructures over a
large surface area (1 cm2
/15 sec). NLL allows one to obtain clearly pre-designed patterned graphene
structures without fabrication tolerances, caused by contacting mask contamination, polymer residuals and
direct laser exposure of the graphene layers. We represent regularly-patterned multilayer graphene (p-MLG)
obtained by the CVD-method on NLL-structured Ni foil. We also demonstrate tuning of chemical
(wettability) and electro-optical (transmittance and sheet resistance) properties of p-MLG due to laser power
adjustment. In conclusion, we show the great promise of fabricated devices, namely supercapacitors, and
Li-ion batteries by using NLL-assisted graphene patterning. Our approach demonstrates a new avenue to
pattern graphene for multifunctional device engineering in optics, photonics, and bioelectronics
Engineering
Faculty of Environment, Science and Economy
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