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dc.contributor.authorKovalska, I
dc.contributor.authorPavlov, I
dc.contributor.authorDeminskyi, P
dc.contributor.authorBaldycheva, A
dc.contributor.authorIlday, FÖ
dc.contributor.authorKocabas, C
dc.date.accessioned2018-01-19T14:35:36Z
dc.date.issued2018-02-06
dc.description.abstractThe 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 bioelectronicsen_GB
dc.description.sponsorshipThis research was partially supported by the European Research Council (ERC) Consolidator Grants ERC682723 SmartGraphene and ERC-617521 NLL; the European Union funding: Marie Curie Fellowship visiting grant; and the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom via Grant No. EP/N035569/1.en_GB
dc.identifier.citationVol 3 (2), pp. 1546–1554.
dc.identifier.doi10.1021/acsomega.7b01853
dc.identifier.urihttp://hdl.handle.net/10871/31098
dc.language.isoenen_GB
dc.publisherAmerican Chemical Societyen_GB
dc.rightsThis is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited: https://pubs.acs.org/page/policy/authorchoice_ccby_termsofuse.html
dc.titleNLL-assisted Multilayer Graphene Patterningen_GB
dc.typeArticleen_GB
dc.identifier.issn2470-1343
dc.descriptionThis is the author accepted manuscript. The final version is available from American Chemical Society via the DOI in this record.en_GB
dc.identifier.journalACS Omegaen_GB


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